Gradio Docs

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Building Demos

import gradio as gr

def greet(name):
    return "Hello " + name + "!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
    
if __name__ == "__main__":
    demo.launch()

import gradio as gr

def greet(name, is_morning, temperature):
    salutation = "Good morning" if is_morning else "Good evening"
    greeting = f"{salutation} {name}. It is {temperature} degrees today"
    celsius = (temperature - 32) * 5 / 9
    return greeting, round(celsius, 2)

demo = gr.Interface(
    fn=greet,
    inputs=["text", "checkbox", gr.Slider(0, 100)],
    outputs=["text", "number"],
)
if __name__ == "__main__":
    demo.launch()

import gradio as gr

title = "GPT-J-6B"

examples = [
    ["The tower is 324 metres (1,063 ft) tall,"],
    ["The Moon's orbit around Earth has"],
    ["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
]

demo = gr.load(
    "huggingface/EleutherAI/gpt-j-6B",
    inputs=gr.Textbox(lines=5, max_lines=6, label="Input Text"),
    title=title,
    examples=examples,
)

if __name__ == "__main__":
    demo.launch()

Interface

gradio.Interface(fn, inputs, outputs, ···)

Interface is Gradio's main high-level class, and allows you to create a web-based GUI / demo around a machine learning model (or any Python function) in a few lines of code. You must specify three parameters: (1) the function to create a GUI for (2) the desired input components and (3) the desired output components. Additional parameters can be used to control the appearance and behavior of the demo.

Example Usage

import gradio as gr

def image_classifier(inp):
    return {'cat': 0.3, 'dog': 0.7}

demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
demo.launch()

Parameter	Description
`fn` Callable required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` str \| IOComponent \| list[str \| IOComponent] \| None required	a single Gradio component, or list of Gradio components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of input components should match the number of parameters in fn. If set to None, then only the output components will be displayed.
`outputs` str \| IOComponent \| list[str \| IOComponent] \| None required	a single Gradio component, or list of Gradio components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of output components should match the number of values returned by fn. If set to None, then only the input components will be displayed.
`examples` list[Any] \| list[list[Any]] \| str \| None default: None	sample inputs for the function; if provided, appear below the UI components and can be clicked to populate the interface. Should be nested list, in which the outer list consists of samples and each inner list consists of an input corresponding to each input component. A string path to a directory of examples can also be provided, but it should be within the directory with the python file running the gradio app. If there are multiple input components and a directory is provided, a log.csv file must be present in the directory to link corresponding inputs.
`cache_examples` bool \| None default: None	If True, caches examples in the server for fast runtime in examples. The default option in HuggingFace Spaces is True. The default option elsewhere is False.
`examples_per_page` int default: 10	If examples are provided, how many to display per page.
`live` bool default: False	whether the interface should automatically rerun if any of the inputs change.
`interpretation` Callable \| str \| None default: None	function that provides interpretation explaining prediction output. Pass "default" to use simple built-in interpreter, "shap" to use a built-in shapley-based interpreter, or your own custom interpretation function. For more information on the different interpretation methods, see the Advanced Interface Features guide.
`num_shap` float default: 2.0	a multiplier that determines how many examples are computed for shap-based interpretation. Increasing this value will increase shap runtime, but improve results. Only applies if interpretation is "shap".
`title` str \| None default: None	a title for the interface; if provided, appears above the input and output components in large font. Also used as the tab title when opened in a browser window.
`description` str \| None default: None	a description for the interface; if provided, appears above the input and output components and beneath the title in regular font. Accepts Markdown and HTML content.
`article` str \| None default: None	an expanded article explaining the interface; if provided, appears below the input and output components in regular font. Accepts Markdown and HTML content.
`thumbnail` str \| None default: None	path or url to image to use as display image when the web demo is shared on social media.
`theme` Theme \| str \| None default: None	Theme to use, loaded from gradio.themes.
`css` str \| None default: None	custom css or path to custom css file to use with interface.
`allow_flagging` str \| None default: None	one of "never", "auto", or "manual". If "never" or "auto", users will not see a button to flag an input and output. If "manual", users will see a button to flag. If "auto", every input the user submits will be automatically flagged (outputs are not flagged). If "manual", both the input and outputs are flagged when the user clicks flag button. This parameter can be set with environmental variable GRADIO_ALLOW_FLAGGING; otherwise defaults to "manual".
`flagging_options` list[str] \| list[tuple[str, str]] \| None default: None	if provided, allows user to select from the list of options when flagging. Only applies if allow_flagging is "manual". Can either be a list of tuples of the form (label, value), where label is the string that will be displayed on the button and value is the string that will be stored in the flagging CSV; or it can be a list of strings ["X", "Y"], in which case the values will be the list of strings and the labels will ["Flag as X", "Flag as Y"], etc.
`flagging_dir` str default: "flagged"	what to name the directory where flagged data is stored.
`flagging_callback` FlaggingCallback default: CSVLogger()	An instance of a subclass of FlaggingCallback which will be called when a sample is flagged. By default logs to a local CSV file.
`analytics_enabled` bool \| None default: None	Whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable if defined, or default to True.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)

Methods

launch

gradio.Interface.launch(···)

Launches a simple web server that serves the demo. Can also be used to create a public link used by anyone to access the demo from their browser by setting share=True.

Example Usage

import gradio as gr
def reverse(text):
    return text[::-1]
demo = gr.Interface(reverse, "text", "text")
demo.launch(share=True, auth=("username", "password"))

Parameter	Description
`inline` bool \| None default: None	whether to display in the interface inline in an iframe. Defaults to True in python notebooks; False otherwise.
`inbrowser` bool default: False	whether to automatically launch the interface in a new tab on the default browser.
`share` bool \| None default: None	whether to create a publicly shareable link for the interface. Creates an SSH tunnel to make your UI accessible from anywhere. If not provided, it is set to False by default every time, except when running in Google Colab. When localhost is not accessible (e.g. Google Colab), setting share=False is not supported.
`debug` bool default: False	if True, blocks the main thread from running. If running in Google Colab, this is needed to print the errors in the cell output.
`enable_queue` bool \| None default: None	DEPRECATED (use .queue() method instead.) if True, inference requests will be served through a queue instead of with parallel threads. Required for longer inference times (> 1min) to prevent timeout. The default option in HuggingFace Spaces is True. The default option elsewhere is False.
`max_threads` int default: 40	the maximum number of total threads that the Gradio app can generate in parallel. The default is inherited from the starlette library (currently 40). Applies whether the queue is enabled or not. But if queuing is enabled, this parameter is increaseed to be at least the concurrency_count of the queue.
`auth` Callable \| tuple[str, str] \| list[tuple[str, str]] \| None default: None	If provided, username and password (or list of username-password tuples) required to access interface. Can also provide function that takes username and password and returns True if valid login.
`auth_message` str \| None default: None	If provided, HTML message provided on login page.
`prevent_thread_lock` bool default: False	If True, the interface will block the main thread while the server is running.
`show_error` bool default: False	If True, any errors in the interface will be displayed in an alert modal and printed in the browser console log
`server_name` str \| None default: None	to make app accessible on local network, set this to "0.0.0.0". Can be set by environment variable GRADIO_SERVER_NAME. If None, will use "127.0.0.1".
`server_port` int \| None default: None	will start gradio app on this port (if available). Can be set by environment variable GRADIO_SERVER_PORT. If None, will search for an available port starting at 7860.
`show_tips` bool default: False	if True, will occasionally show tips about new Gradio features
`height` int default: 500	The height in pixels of the iframe element containing the interface (used if inline=True)
`width` int \| str default: "100%"	The width in pixels of the iframe element containing the interface (used if inline=True)
`encrypt` bool \| None default: None	DEPRECATED. Has no effect.
`favicon_path` str \| None default: None	If a path to a file (.png, .gif, or .ico) is provided, it will be used as the favicon for the web page.
`ssl_keyfile` str \| None default: None	If a path to a file is provided, will use this as the private key file to create a local server running on https.
`ssl_certfile` str \| None default: None	If a path to a file is provided, will use this as the signed certificate for https. Needs to be provided if ssl_keyfile is provided.
`ssl_keyfile_password` str \| None default: None	If a password is provided, will use this with the ssl certificate for https.
`ssl_verify` bool default: True	If False, skips certificate validation which allows self-signed certificates to be used.
`quiet` bool default: False	If True, suppresses most print statements.
`show_api` bool default: True	If True, shows the api docs in the footer of the app. Default True. If the queue is enabled, then api_open parameter of .queue() will determine if the api docs are shown, independent of the value of show_api.
`file_directories` list[str] \| None default: None	This parameter has been renamed to `allowed_paths`. It will be removed in a future version.
`allowed_paths` list[str] \| None default: None	List of complete filepaths or parent directories that gradio is allowed to serve (in addition to the directory containing the gradio python file). Must be absolute paths. Warning: if you provide directories, any files in these directories or their subdirectories are accessible to all users of your app.
`blocked_paths` list[str] \| None default: None	List of complete filepaths or parent directories that gradio is not allowed to serve (i.e. users of your app are not allowed to access). Must be absolute paths. Warning: takes precedence over `allowed_paths` and all other directories exposed by Gradio by default.

load

gradio.Interface.load(name, ···)

Warning: this method will be deprecated. Use the equivalent `gradio.load()` instead. This is a class method that constructs a Blocks from a Hugging Face repo. Can accept model repos (if src is "models") or Space repos (if src is "spaces"). The input and output components are automatically loaded from the repo.

Parameter	Description
`name` str required	the name of the model (e.g. "gpt2" or "facebook/bart-base") or space (e.g. "flax-community/spanish-gpt2"), can include the `src` as prefix (e.g. "models/facebook/bart-base")
`src` str \| None default: None	the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
`api_key` str \| None default: None	optional access token for loading private Hugging Face Hub models or spaces. Find your token here: https://huggingface.co/settings/tokens
`alias` str \| None default: None	optional string used as the name of the loaded model instead of the default name (only applies if loading a Space running Gradio 2.x)

from_pipeline

gradio.Interface.from_pipeline(pipeline, ···)

Class method that constructs an Interface from a Hugging Face transformers.Pipeline object. The input and output components are automatically determined from the pipeline.

Example Usage

import gradio as gr
from transformers import pipeline
pipe = pipeline("image-classification")
gr.Interface.from_pipeline(pipe).launch()

Parameter Description

Parameter	Description
`pipeline` Pipeline required	the pipeline object to use.


                pipeline

Pipeline

required

the pipeline object to use.

integrate

gradio.Interface.integrate(···)

A catch-all method for integrating with other libraries. This method should be run after launch()

Parameter Description

Parameter	Description
`comet_ml` default: None	If a comet_ml Experiment object is provided, will integrate with the experiment and appear on Comet dashboard
`wandb` ModuleType \| None default: None	If the wandb module is provided, will integrate with it and appear on WandB dashboard
`mlflow` ModuleType \| None default: None	If the mlflow module is provided, will integrate with the experiment and appear on ML Flow dashboard


                comet_ml

default: None

If a comet_ml Experiment object is provided, will integrate with the experiment and appear on Comet dashboard


                wandb

ModuleType | None

default: None

If the wandb module is provided, will integrate with it and appear on WandB dashboard


                mlflow

ModuleType | None

default: None

If the mlflow module is provided, will integrate with the experiment and appear on ML Flow dashboard

queue

gradio.Interface.queue(···)

You can control the rate of processed requests by creating a queue. This will allow you to set the number of requests to be processed at one time, and will let users know their position in the queue.

Example Usage

demo = gr.Interface(image_generator, gr.Textbox(), gr.Image())
demo.queue(concurrency_count=3)
demo.launch()

Parameter	Description
`concurrency_count` int default: 1	Number of worker threads that will be processing requests from the queue concurrently. Increasing this number will increase the rate at which requests are processed, but will also increase the memory usage of the queue.
`status_update_rate` float \| Literal['auto'] default: "auto"	If "auto", Queue will send status estimations to all clients whenever a job is finished. Otherwise Queue will send status at regular intervals set by this parameter as the number of seconds.
`client_position_to_load_data` int \| None default: None	DEPRECATED. This parameter is deprecated and has no effect.
`default_enabled` bool \| None default: None	Deprecated and has no effect.
`api_open` bool default: True	If True, the REST routes of the backend will be open, allowing requests made directly to those endpoints to skip the queue.
`max_size` int \| None default: None	The maximum number of events the queue will store at any given moment. If the queue is full, new events will not be added and a user will receive a message saying that the queue is full. If None, the queue size will be unlimited.

Step-by-step Guides

Quickstart

Key Features

Sharing Your App

Interface State

Reactive Interfaces

Advanced Interface Features

Flagging

A Gradio Interface includes a "Flag" button that appears underneath the output. By default, clicking on the Flag button sends the input and output data back to the machine where the gradio demo is running, and saves it to a CSV log file. But this default behavior can be changed. To set what happens when the Flag button is clicked, you pass an instance of a subclass of FlaggingCallback to the flagging_callback parameter in the Interface constructor. You can use one of the FlaggingCallback subclasses that are listed below, or you can create your own, which lets you do whatever you want with the data that is being flagged.

SimpleCSVLogger

gradio.SimpleCSVLogger(···)

A simplified implementation of the FlaggingCallback abstract class provided for illustrative purposes. Each flagged sample (both the input and output data) is logged to a CSV file on the machine running the gradio app.

Example Usage

import gradio as gr
def image_classifier(inp):
    return {'cat': 0.3, 'dog': 0.7}
demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label",
                    flagging_callback=SimpleCSVLogger())

Step-by-step Guides

No guides yet, contribute a guide about SimpleCSVLogger

CSVLogger

gradio.CSVLogger(···)

The default implementation of the FlaggingCallback abstract class. Each flagged sample (both the input and output data) is logged to a CSV file with headers on the machine running the gradio app.

Example Usage

import gradio as gr
def image_classifier(inp):
    return {'cat': 0.3, 'dog': 0.7}
demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label",
                    flagging_callback=CSVLogger())

Step-by-step Guides

Using Flagging

HuggingFaceDatasetSaver

gradio.HuggingFaceDatasetSaver(hf_token, dataset_name, ···)

A callback that saves each flagged sample (both the input and output data) to a HuggingFace dataset.

Example Usage

import gradio as gr
hf_writer = gr.HuggingFaceDatasetSaver(HF_API_TOKEN, "image-classification-mistakes")
def image_classifier(inp):
    return {'cat': 0.3, 'dog': 0.7}
demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label",
                    allow_flagging="manual", flagging_callback=hf_writer)

Parameter	Description
`hf_token` str required	The HuggingFace token to use to create (and write the flagged sample to) the HuggingFace dataset (defaults to the registered one).
`dataset_name` str required	The repo_id of the dataset to save the data to, e.g. "image-classifier-1" or "username/image-classifier-1".
`organization` str \| None default: None	Deprecated argument. Please pass a full dataset id (e.g. 'username/dataset_name') to `dataset_name` instead.
`private` bool default: False	Whether the dataset should be private (defaults to False).
`info_filename` str default: "dataset_info.json"	The name of the file to save the dataset info (defaults to "dataset_infos.json").
`separate_dirs` bool default: False	If True, each flagged item will be saved in a separate directory. This makes the flagging more robust to concurrent editing, but may be less convenient to use.
`verbose` bool default: True

Step-by-step Guides

Using Flagging

Combining Interfaces

Once you have created several Interfaces, we provide several classes that let you start combining them together. For example, you can chain them in Series or compare their outputs in Parallel if the inputs and outputs match accordingly. You can also display arbitrary Interfaces together in a tabbed layout using TabbedInterface.

import gradio as gr

title = "GPT-J-6B"

tts_examples = [
    "I love learning machine learning",
    "How do you do?",
]

tts_demo = gr.load(
    "huggingface/facebook/fastspeech2-en-ljspeech",
    title=None,
    examples=tts_examples,
    description="Give me something to say!",
)

stt_demo = gr.load(
    "huggingface/facebook/wav2vec2-base-960h",
    title=None,
    inputs="mic",
    description="Let me try to guess what you're saying!",
)

demo = gr.TabbedInterface([tts_demo, stt_demo], ["Text-to-speech", "Speech-to-text"])

if __name__ == "__main__":
    demo.launch()

TabbedInterface

gradio.TabbedInterface(interface_list, ···)

A TabbedInterface is created by providing a list of Interfaces, each of which gets rendered in a separate tab.

import gradio as gr

title = "GPT-J-6B"

tts_examples = [
    "I love learning machine learning",
    "How do you do?",
]

tts_demo = gr.load(
    "huggingface/facebook/fastspeech2-en-ljspeech",
    title=None,
    examples=tts_examples,
    description="Give me something to say!",
)

stt_demo = gr.load(
    "huggingface/facebook/wav2vec2-base-960h",
    title=None,
    inputs="mic",
    description="Let me try to guess what you're saying!",
)

demo = gr.TabbedInterface([tts_demo, stt_demo], ["Text-to-speech", "Speech-to-text"])

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`interface_list` list[Interface] required	a list of interfaces to be rendered in tabs.
`tab_names` list[str] \| None default: None	a list of tab names. If None, the tab names will be "Tab 1", "Tab 2", etc.
`title` str \| None default: None	a title for the interface; if provided, appears above the input and output components in large font. Also used as the tab title when opened in a browser window.
`theme` Theme \| None default: None
`analytics_enabled` bool \| None default: None	whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable or default to True.
`css` str \| None default: None	custom css or path to custom css file to apply to entire Blocks

import gradio as gr

greeter_1 = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 1"))
greeter_2 = gr.Interface(lambda name: f"Greetings {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 2"))
demo = gr.Parallel(greeter_1, greeter_2)

if __name__ == "__main__":
    demo.launch()

import gradio as gr

generator1 = gr.load("huggingface/gpt2")
generator2 = gr.load("huggingface/EleutherAI/gpt-neo-2.7B")
generator3 = gr.load("huggingface/EleutherAI/gpt-j-6B")

demo = gr.Parallel(generator1, generator2, generator3)

if __name__ == "__main__":
    demo.launch()

Parallel

gradio.Parallel(interfaces, ···)

Creates a new Interface consisting of multiple Interfaces in parallel (comparing their outputs). The Interfaces to put in Parallel must share the same input components (but can have different output components).

import gradio as gr

greeter_1 = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 1"))
greeter_2 = gr.Interface(lambda name: f"Greetings {name}!", inputs="textbox", outputs=gr.Textbox(label="Greeter 2"))
demo = gr.Parallel(greeter_1, greeter_2)

if __name__ == "__main__":
    demo.launch()

Parameter Description

Parameter	Description
`interfaces` required	any number of Interface objects that are to be compared in parallel
`options`	additional kwargs that are passed into the new Interface object to customize it


                interfaces

required

any number of Interface objects that are to be compared in parallel


                options

additional kwargs that are passed into the new Interface object to customize it

Step-by-step Guides

Advanced Interface Features

import gradio as gr

get_name = gr.Interface(lambda name: name, inputs="textbox", outputs="textbox")
prepend_hello = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs="textbox")
append_nice = gr.Interface(lambda greeting: f"{greeting} Nice to meet you!",
                           inputs="textbox", outputs=gr.Textbox(label="Greeting"))
demo = gr.Series(get_name, prepend_hello, append_nice)

if __name__ == "__main__":
    demo.launch()

import gradio as gr

generator = gr.load("huggingface/gpt2")
translator = gr.load("huggingface/t5-small")

demo = gr.Series(generator, translator, description="This demo combines two Spaces: a text generator (`huggingface/gpt2`) and a text translator (`huggingface/t5-small`). The first Space takes a prompt as input and generates a text. The second Space takes the generated text as input and translates it into another language.")

if __name__ == "__main__":
    demo.launch()

Series

gradio.Series(interfaces, ···)

Creates a new Interface from multiple Interfaces in series (the output of one is fed as the input to the next, and so the input and output components must agree between the interfaces).

import gradio as gr

get_name = gr.Interface(lambda name: name, inputs="textbox", outputs="textbox")
prepend_hello = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs="textbox")
append_nice = gr.Interface(lambda greeting: f"{greeting} Nice to meet you!",
                           inputs="textbox", outputs=gr.Textbox(label="Greeting"))
demo = gr.Series(get_name, prepend_hello, append_nice)

if __name__ == "__main__":
    demo.launch()

Parameter Description

Parameter	Description
`interfaces` required	any number of Interface objects that are to be connected in series
`options`	additional kwargs that are passed into the new Interface object to customize it


                interfaces

required

any number of Interface objects that are to be connected in series


                options

additional kwargs that are passed into the new Interface object to customize it

Step-by-step Guides

Advanced Interface Features

import gradio as gr

def welcome(name):
    return f"Welcome to Gradio, {name}!"

with gr.Blocks() as demo:
    gr.Markdown(
    """
    # Hello World!
    Start typing below to see the output.
    """)
    inp = gr.Textbox(placeholder="What is your name?")
    out = gr.Textbox()
    inp.change(welcome, inp, out)

if __name__ == "__main__":
    demo.launch()

import numpy as np
import gradio as gr


def flip_text(x):
    return x[::-1]


def flip_image(x):
    return np.fliplr(x)


with gr.Blocks() as demo:
    gr.Markdown("Flip text or image files using this demo.")
    with gr.Tab("Flip Text"):
        text_input = gr.Textbox()
        text_output = gr.Textbox()
        text_button = gr.Button("Flip")
    with gr.Tab("Flip Image"):
        with gr.Row():
            image_input = gr.Image()
            image_output = gr.Image()
        image_button = gr.Button("Flip")

    with gr.Accordion("Open for More!"):
        gr.Markdown("Look at me...")

    text_button.click(flip_text, inputs=text_input, outputs=text_output)
    image_button.click(flip_image, inputs=image_input, outputs=image_output)

if __name__ == "__main__":
    demo.launch()

from transformers import pipeline

import gradio as gr

asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
classifier = pipeline("text-classification")


def speech_to_text(speech):
    text = asr(speech)["text"]
    return text


def text_to_sentiment(text):
    return classifier(text)[0]["label"]


demo = gr.Blocks()

with demo:
    audio_file = gr.Audio(type="filepath")
    text = gr.Textbox()
    label = gr.Label()

    b1 = gr.Button("Recognize Speech")
    b2 = gr.Button("Classify Sentiment")

    b1.click(speech_to_text, inputs=audio_file, outputs=text)
    b2.click(text_to_sentiment, inputs=text, outputs=label)

if __name__ == "__main__":
    demo.launch()

import gradio as gr

from transformers import pipeline

english_translator = gr.Blocks.load(name="spaces/gradio/english_translator")
english_generator = pipeline("text-generation", model="distilgpt2")


def generate_text(text):
    english_text = english_generator(text)[0]["generated_text"]
    german_text = english_translator(english_text)
    return english_text, german_text


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            seed = gr.Text(label="Input Phrase")
        with gr.Column():
            english = gr.Text(label="Generated English Text")
            german = gr.Text(label="Generated German Text")
    btn = gr.Button("Generate")
    btn.click(generate_text, inputs=[seed], outputs=[english, german])
    gr.Examples(["My name is Clara and I am"], inputs=[seed])

if __name__ == "__main__":
    demo.launch()

import time
import gradio as gr


js_function = "() => {new Audio('file=beep.mp3').play();}"

def task(x):
    time.sleep(2)
    return "Hello, " + x 

with gr.Blocks() as demo:
    name = gr.Textbox(label="name")
    greeting = gr.Textbox(label="greeting")
    name.blur(task, name, greeting)
    greeting.change(None, [], [], _js=js_function)  # Note that _js is a special arugment whose usage may change in the future
    
demo.launch()

Blocks

with gr.Blocks():

Blocks is Gradio's low-level API that allows you to create more custom web applications and demos than Interfaces (yet still entirely in Python).

Compared to the Interface class, Blocks offers more flexibility and control over: (1) the layout of components (2) the events that trigger the execution of functions (3) data flows (e.g. inputs can trigger outputs, which can trigger the next level of outputs). Blocks also offers ways to group together related demos such as with tabs.

The basic usage of Blocks is as follows: create a Blocks object, then use it as a context (with the "with" statement), and then define layouts, components, or events within the Blocks context. Finally, call the launch() method to launch the demo.

Example Usage

import gradio as gr
def update(name):
    return f"Welcome to Gradio, {name}!"

with gr.Blocks() as demo:
    gr.Markdown("Start typing below and then click **Run** to see the output.")
    with gr.Row():
        inp = gr.Textbox(placeholder="What is your name?")
        out = gr.Textbox()
    btn = gr.Button("Run")
    btn.click(fn=update, inputs=inp, outputs=out)

demo.launch()

Parameter	Description
`theme` Theme \| str \| None default: None	a Theme object or a string representing a theme. If a string, will look for a built-in theme with that name (e.g. "soft" or "default"), or will attempt to load a theme from the HF Hub (e.g. "gradio/monochrome"). If None, will use the Default theme.
`analytics_enabled` bool \| None default: None	whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED environment variable or default to True.
`mode` str default: "blocks"	a human-friendly name for the kind of Blocks or Interface being created.
`title` str default: "Gradio"	The tab title to display when this is opened in a browser window.
`css` str \| None default: None	custom css or path to custom css file to apply to entire Blocks

Methods

launch

gradio.Blocks.launch(···)

Launches a simple web server that serves the demo. Can also be used to create a public link used by anyone to access the demo from their browser by setting share=True.

Example Usage

import gradio as gr
def reverse(text):
    return text[::-1]
with gr.Blocks() as demo:
    button = gr.Button(value="Reverse")
    button.click(reverse, gr.Textbox(), gr.Textbox())
demo.launch(share=True, auth=("username", "password"))

Parameter	Description
`inline` bool \| None default: None	whether to display in the interface inline in an iframe. Defaults to True in python notebooks; False otherwise.
`inbrowser` bool default: False	whether to automatically launch the interface in a new tab on the default browser.
`share` bool \| None default: None	whether to create a publicly shareable link for the interface. Creates an SSH tunnel to make your UI accessible from anywhere. If not provided, it is set to False by default every time, except when running in Google Colab. When localhost is not accessible (e.g. Google Colab), setting share=False is not supported.
`debug` bool default: False	if True, blocks the main thread from running. If running in Google Colab, this is needed to print the errors in the cell output.
`enable_queue` bool \| None default: None	DEPRECATED (use .queue() method instead.) if True, inference requests will be served through a queue instead of with parallel threads. Required for longer inference times (> 1min) to prevent timeout. The default option in HuggingFace Spaces is True. The default option elsewhere is False.
`max_threads` int default: 40	the maximum number of total threads that the Gradio app can generate in parallel. The default is inherited from the starlette library (currently 40). Applies whether the queue is enabled or not. But if queuing is enabled, this parameter is increaseed to be at least the concurrency_count of the queue.
`auth` Callable \| tuple[str, str] \| list[tuple[str, str]] \| None default: None	If provided, username and password (or list of username-password tuples) required to access interface. Can also provide function that takes username and password and returns True if valid login.
`auth_message` str \| None default: None	If provided, HTML message provided on login page.
`prevent_thread_lock` bool default: False	If True, the interface will block the main thread while the server is running.
`show_error` bool default: False	If True, any errors in the interface will be displayed in an alert modal and printed in the browser console log
`server_name` str \| None default: None	to make app accessible on local network, set this to "0.0.0.0". Can be set by environment variable GRADIO_SERVER_NAME. If None, will use "127.0.0.1".
`server_port` int \| None default: None	will start gradio app on this port (if available). Can be set by environment variable GRADIO_SERVER_PORT. If None, will search for an available port starting at 7860.
`show_tips` bool default: False	if True, will occasionally show tips about new Gradio features
`height` int default: 500	The height in pixels of the iframe element containing the interface (used if inline=True)
`width` int \| str default: "100%"	The width in pixels of the iframe element containing the interface (used if inline=True)
`encrypt` bool \| None default: None	DEPRECATED. Has no effect.
`favicon_path` str \| None default: None	If a path to a file (.png, .gif, or .ico) is provided, it will be used as the favicon for the web page.
`ssl_keyfile` str \| None default: None	If a path to a file is provided, will use this as the private key file to create a local server running on https.
`ssl_certfile` str \| None default: None	If a path to a file is provided, will use this as the signed certificate for https. Needs to be provided if ssl_keyfile is provided.
`ssl_keyfile_password` str \| None default: None	If a password is provided, will use this with the ssl certificate for https.
`ssl_verify` bool default: True	If False, skips certificate validation which allows self-signed certificates to be used.
`quiet` bool default: False	If True, suppresses most print statements.
`show_api` bool default: True	If True, shows the api docs in the footer of the app. Default True. If the queue is enabled, then api_open parameter of .queue() will determine if the api docs are shown, independent of the value of show_api.
`file_directories` list[str] \| None default: None	This parameter has been renamed to `allowed_paths`. It will be removed in a future version.
`allowed_paths` list[str] \| None default: None	List of complete filepaths or parent directories that gradio is allowed to serve (in addition to the directory containing the gradio python file). Must be absolute paths. Warning: if you provide directories, any files in these directories or their subdirectories are accessible to all users of your app.
`blocked_paths` list[str] \| None default: None	List of complete filepaths or parent directories that gradio is not allowed to serve (i.e. users of your app are not allowed to access). Must be absolute paths. Warning: takes precedence over `allowed_paths` and all other directories exposed by Gradio by default.

queue

gradio.Blocks.queue(···)

You can control the rate of processed requests by creating a queue. This will allow you to set the number of requests to be processed at one time, and will let users know their position in the queue.

Example Usage

with gr.Blocks() as demo:
    button = gr.Button(label="Generate Image")
    button.click(fn=image_generator, inputs=gr.Textbox(), outputs=gr.Image())
demo.queue(concurrency_count=3)
demo.launch()

Parameter	Description
`concurrency_count` int default: 1	Number of worker threads that will be processing requests from the queue concurrently. Increasing this number will increase the rate at which requests are processed, but will also increase the memory usage of the queue.
`status_update_rate` float \| Literal['auto'] default: "auto"	If "auto", Queue will send status estimations to all clients whenever a job is finished. Otherwise Queue will send status at regular intervals set by this parameter as the number of seconds.
`client_position_to_load_data` int \| None default: None	DEPRECATED. This parameter is deprecated and has no effect.
`default_enabled` bool \| None default: None	Deprecated and has no effect.
`api_open` bool default: True	If True, the REST routes of the backend will be open, allowing requests made directly to those endpoints to skip the queue.
`max_size` int \| None default: None	The maximum number of events the queue will store at any given moment. If the queue is full, new events will not be added and a user will receive a message saying that the queue is full. If None, the queue size will be unlimited.

integrate

gradio.Blocks.integrate(···)

A catch-all method for integrating with other libraries. This method should be run after launch()

Parameter Description

Parameter	Description
`comet_ml` default: None	If a comet_ml Experiment object is provided, will integrate with the experiment and appear on Comet dashboard
`wandb` ModuleType \| None default: None	If the wandb module is provided, will integrate with it and appear on WandB dashboard
`mlflow` ModuleType \| None default: None	If the mlflow module is provided, will integrate with the experiment and appear on ML Flow dashboard


                comet_ml

default: None

If a comet_ml Experiment object is provided, will integrate with the experiment and appear on Comet dashboard


                wandb

ModuleType | None

default: None

If the wandb module is provided, will integrate with it and appear on WandB dashboard


                mlflow

ModuleType | None

default: None

If the mlflow module is provided, will integrate with the experiment and appear on ML Flow dashboard

load

gradio.Blocks.load(···)

For reverse compatibility reasons, this is both a class method and an instance method, the two of which, confusingly, do two completely different things.

Class method: loads a demo from a Hugging Face Spaces repo and creates it locally and returns a block instance. Warning: this method will be deprecated. Use the equivalent `gradio.load()` instead.

Instance method: adds event that runs as soon as the demo loads in the browser. Example usage below.

Example Usage

import gradio as gr
import datetime
with gr.Blocks() as demo:
    def get_time():
        return datetime.datetime.now().time()
    dt = gr.Textbox(label="Current time")
    demo.load(get_time, inputs=None, outputs=dt)
demo.launch()

Parameter	Description
`fn` Callable \| None default: None	Instance Method - the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` list[Component] \| None default: None	Instance Method - List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` list[Component] \| None default: None	Instance Method - List of gradio.components to use as inputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Instance Method - Defining this parameter exposes the endpoint in the api docs
`scroll_to_output` bool default: False	Instance Method - If True, will scroll to output component on completion
`show_progress` bool default: True	Instance Method - If True, will show progress animation while pending
`queue` default: None	Instance Method - If True, will place the request on the queue, if the queue exists
`batch` bool default: False	Instance Method - If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Instance Method - Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	Instance Method - If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	Instance Method - If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`every` float \| None default: None	Instance Method - Run this event 'every' number of seconds. Interpreted in seconds. Queue must be enabled.
`name` str \| None default: None	Class Method - the name of the model (e.g. "gpt2" or "facebook/bart-base") or space (e.g. "flax-community/spanish-gpt2"), can include the `src` as prefix (e.g. "models/facebook/bart-base")
`src` str \| None default: None	Class Method - the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
`api_key` str \| None default: None	Class Method - optional access token for loading private Hugging Face Hub models or spaces. Find your token here: https://huggingface.co/settings/tokens
`alias` str \| None default: None	Class Method - optional string used as the name of the loaded model instead of the default name (only applies if loading a Space running Gradio 2.x)

Step-by-step Guides

Blocks And Event Listeners

Controlling Layout

State In Blocks

Custom CSS And JS

Custom Interpretations With Blocks

Using Blocks Like Functions

Block Layouts

Customize the layout of your Blocks UI with the layout classes below.

Row

with gr.Row():

Row is a layout element within Blocks that renders all children horizontally.

Example Usage

with gr.Blocks() as demo:
    with gr.Row():
        gr.Image("lion.jpg")
        gr.Image("tiger.jpg")
demo.launch()

Parameter Description

Parameter	Description
`variant` str default: "default"	row type, 'default' (no background), 'panel' (gray background color and rounded corners), or 'compact' (rounded corners and no internal gap).
`visible` bool default: True	If False, row will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.


                variant

str

default: "default"

row type, 'default' (no background), 'panel' (gray background color and rounded corners), or 'compact' (rounded corners and no internal gap).


                visible

bool

default: True

If False, row will be hidden.


                elem_id

str | None

default: None

An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.

Step-by-step Guides

Controlling Layout

Column

with gr.Column():

Column is a layout element within Blocks that renders all children vertically. The widths of columns can be set through the `scale` and `min_width` parameters. If a certain scale results in a column narrower than min_width, the min_width parameter will win.

Example Usage

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=1):
            text1 = gr.Textbox()
            text2 = gr.Textbox()
        with gr.Column(scale=4):
            btn1 = gr.Button("Button 1")
            btn2 = gr.Button("Button 2")

Parameter	Description
`scale` int default: 1	relative width compared to adjacent Columns. For example, if Column A has scale=2, and Column B has scale=1, A will be twice as wide as B.
`min_width` int default: 320	minimum pixel width of Column, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in a column narrower than min_width, the min_width parameter will be respected first.
`variant` str default: "default"	column type, 'default' (no background), 'panel' (gray background color and rounded corners), or 'compact' (rounded corners and no internal gap).
`visible` bool default: True	If False, column will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.

Step-by-step Guides

Controlling Layout

Tab

with gr.Tab():

Tab (or its alias TabItem) is a layout element. Components defined within the Tab will be visible when this tab is selected tab.

Example Usage

with gr.Blocks() as demo:
    with gr.Tab("Lion"):
        gr.Image("lion.jpg")
        gr.Button("New Lion")
    with gr.Tab("Tiger"):
        gr.Image("tiger.jpg")
        gr.Button("New Tiger")

Parameter Description

Parameter	Description
`label` str required	The visual label for the tab
`id` int \| str \| None default: None	An optional identifier for the tab, required if you wish to control the selected tab from a predict function.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.


                label

str

required

The visual label for the tab

id

int | str | None

default: None

An optional identifier for the tab, required if you wish to control the selected tab from a predict function.


                elem_id

str | None

default: None

An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.

Methods

select

gradio.Tab.select(fn, ···)

This event is triggered when the user selects from within the Component. This event has EventData of type gradio.SelectData that carries information, accessible through SelectData.index and SelectData.value. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

Controlling Layout

Box

with gr.Box():

Box is a a layout element which places children in a box with rounded corners and some padding around them.

Example Usage

with gr.Box():
    gr.Textbox(label="First")
    gr.Textbox(label="Last")

Parameter Description

Parameter	Description
`visible` bool default: True	If False, box will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.


                visible

bool

default: True

If False, box will be hidden.


                elem_id

str | None

default: None

An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.

Step-by-step Guides

No guides yet, contribute a guide about Box

Accordion

gradio.Accordion(label, ···)

Accordion is a layout element which can be toggled to show/hide the contained content.

Example Usage

with gr.Accordion("See Details"):
    gr.Markdown("lorem ipsum")

Parameter	Description
`label` required	name of accordion section.
`open` bool default: True	if True, accordion is open by default.
`visible` bool default: True
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.

Step-by-step Guides

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Themes

Customize the look of your app by writing your own custom theme

Base

gradio.Base(···)

Parameter	Description
`primary_hue` colors.Color \| str default: Color()	The primary hue of the theme. Load a preset, like gradio.themes.colors.green (or just the string "green"), or pass your own gradio.themes.utils.Color object.
`secondary_hue` colors.Color \| str default: Color()	The secondary hue of the theme. Load a preset, like gradio.themes.colors.green (or just the string "green"), or pass your own gradio.themes.utils.Color object.
`neutral_hue` colors.Color \| str default: Color()	The neutral hue of the theme, used . Load a preset, like gradio.themes.colors.green (or just the string "green"), or pass your own gradio.themes.utils.Color object.
`text_size` sizes.Size \| str default: Size()	The size of the text. Load a preset, like gradio.themes.sizes.text_sm (or just the string "sm"), or pass your own gradio.themes.utils.Size object.
`spacing_size` sizes.Size \| str default: Size()	The size of the spacing. Load a preset, like gradio.themes.sizes.spacing_sm (or just the string "sm"), or pass your own gradio.themes.utils.Size object.
`radius_size` sizes.Size \| str default: Size()	The radius size of corners. Load a preset, like gradio.themes.sizes.radius_sm (or just the string "sm"), or pass your own gradio.themes.utils.Size object.
`font` fonts.Font \| str \| Iterable[fonts.Font \| str] default: (, 'ui-sans-serif', 'system-ui', 'sans-serif')	The primary font to use for the theme. Pass a string for a system font, or a gradio.themes.font.GoogleFont object to load a font from Google Fonts. Pass a list of fonts for fallbacks.
`font_mono` fonts.Font \| str \| Iterable[fonts.Font \| str] default: (, 'ui-monospace', 'Consolas', 'monospace')	The monospace font to use for the theme, applies to code. Pass a string for a system font, or a gradio.themes.font.GoogleFont object to load a font from Google Fonts. Pass a list of fonts for fallbacks.

Methods

push_to_hub

gradio.Base.push_to_hub(repo_name, ···)

Upload a theme to the HuggingFace hub.
This requires a HuggingFace account.

Parameter	Description
`repo_name` str required	The name of the repository to store the theme assets, e.g. 'my_theme' or 'sunset'.
`org_name` str \| None default: None	The name of the org to save the space in. If None (the default), the username corresponding to the logged in user, or hƒ_token is used.
`version` str \| None default: None	A semantic version tag for theme. Bumping the version tag lets you publish updates to a theme without changing the look of applications that already loaded your theme.
`hf_token` str \| None default: None	API token for your HuggingFace account
`theme_name` str \| None default: None	Name for the name. If None, defaults to repo_name
`description` str \| None default: None	A long form description to your theme.
`private` bool default: False

from_hub

gradio.Base.from_hub(repo_name, ···)

Load a theme from the hub.
This DOES NOT require a HuggingFace account for downloading publicly available themes.

Parameter Description

Parameter	Description
`repo_name` str required	string of the form /@. If a semantic version expression is omitted, the latest version will be fetched.
`hf_token` str \| None default: None	HuggingFace Token. Only needed to download private themes.


                repo_name

str

required

string of the form /@. If a semantic version expression is omitted, the latest version will be fetched.


                hf_token

str | None

default: None

HuggingFace Token. Only needed to download private themes.

load

gradio.Base.load(path, ···)

Load a theme from a json file.

Parameter Description

Parameter	Description
`path` str required	The filepath to read.


                path

str

required

The filepath to read.

dump

gradio.Base.dump(filename, ···)

Write the theme to a json file.

Parameter Description

Parameter	Description
`filename` str required	The path to write the theme too


                filename

str

required

The path to write the theme too

from_dict

gradio.Base.from_dict(theme, ···)

Create a theme instance from a dictionary representation.

Parameter Description

Parameter	Description
`theme` dict[str, dict[str, str]] required	The dictionary representation of the theme.


                theme

dict[str, dict[str, str]]

required

The dictionary representation of the theme.

to_dict

gradio.Base.to_dict(···)

Convert the theme into a python dictionary.

Step-by-step Guides

No guides yet, contribute a guide about Base

Components

Gradio includes pre-built components that can be used as inputs or outputs in your Interface or Blocks with a single line of code. Components include preprocessing steps that convert user data submitted through browser to something that be can used by a Python function, and postprocessing steps to convert values returned by a Python function into something that can be displayed in a browser.

Consider an example with three inputs (Textbox, Number, and Image) and two outputs (Number and Gallery), below is a diagram of what our preprocessing will send to the function and what our postprocessing will require from it.

Components also come with certain events that they support. These are methods that are triggered with user actions. Below is a table showing which events are supported for each component. All events are also listed (with parameters) in the component's docs.

	Change	Click	Submit	Edit	Clear	Play	Pause	Stream	Blur	Upload
AnnotatedImage	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Audio	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
BarPlot	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Button	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Chatbot	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Checkbox	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
CheckboxGroup	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Code	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
ColorPicker	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Dataframe	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Dataset	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Dropdown	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
File	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Gallery	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
HTML	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
HighlightedText	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Image	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Interpretation	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
JSON	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Label	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
LinePlot	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Markdown	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Model3D	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Number	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Plot	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Radio	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
ScatterPlot	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Slider	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
State	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Textbox	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Timeseries	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
UploadButton	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕
Video	✕	✕	✕	✕	✕	✕	✕	✕	✕	✕

import gradio as gr
import numpy as np
import random

with gr.Blocks() as demo:
    section_labels = [
        "apple",
        "banana",
        "carrot",
        "donut",
        "eggplant",
        "fish",
        "grapes",
        "hamburger",
        "ice cream",
        "juice",
    ]

    with gr.Row():
        num_boxes = gr.Slider(0, 5, 2, step=1, label="Number of boxes")
        num_segments = gr.Slider(0, 5, 1, step=1, label="Number of segments")

    with gr.Row():
        img_input = gr.Image()
        img_output = gr.AnnotatedImage().style(
            color_map={"banana": "#a89a00", "carrot": "#ffae00"}
        )

    section_btn = gr.Button("Identify Sections")
    selected_section = gr.Textbox(label="Selected Section")

    def section(img, num_boxes, num_segments):
        sections = []
        for a in range(num_boxes):
            x = random.randint(0, img.shape[1])
            y = random.randint(0, img.shape[0])
            w = random.randint(0, img.shape[1] - x)
            h = random.randint(0, img.shape[0] - y)
            sections.append(((x, y, x + w, y + h), section_labels[a]))
        for b in range(num_segments):
            x = random.randint(0, img.shape[1])
            y = random.randint(0, img.shape[0])
            r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))
            mask = np.zeros(img.shape[:2])
            for i in range(img.shape[0]):
                for j in range(img.shape[1]):
                    dist_square = (i - y) ** 2 + (j - x) ** 2
                    if dist_square < r**2:
                        mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4
            sections.append((mask, section_labels[b + num_boxes]))
        return (img, sections)

    section_btn.click(section, [img_input, num_boxes, num_segments], img_output)

    def select_section(evt: gr.SelectData):
        return section_labels[evt.index]

    img_output.select(select_section, None, selected_section)


demo.launch()

AnnotatedImage

gradio.AnnotatedImage(···)

Displays a base image and colored subsections on top of that image. Subsections can take the from of rectangles (e.g. object detection) or masks (e.g. image segmentation).

As input: this component does *not* accept input.

As output: expects a Tuple[numpy.ndarray | PIL.Image | str, List[Tuple[numpy.ndarray | Tuple[int, int, int, int], str]]] consisting of a base image and a list of subsections, that are either (x1, y1, x2, y2) tuples identifying object boundaries, or 0-1 confidence masks of the same shape as the image. A label is provided for each subsection.

import gradio as gr
import numpy as np
import random

with gr.Blocks() as demo:
    section_labels = [
        "apple",
        "banana",
        "carrot",
        "donut",
        "eggplant",
        "fish",
        "grapes",
        "hamburger",
        "ice cream",
        "juice",
    ]

    with gr.Row():
        num_boxes = gr.Slider(0, 5, 2, step=1, label="Number of boxes")
        num_segments = gr.Slider(0, 5, 1, step=1, label="Number of segments")

    with gr.Row():
        img_input = gr.Image()
        img_output = gr.AnnotatedImage().style(
            color_map={"banana": "#a89a00", "carrot": "#ffae00"}
        )

    section_btn = gr.Button("Identify Sections")
    selected_section = gr.Textbox(label="Selected Section")

    def section(img, num_boxes, num_segments):
        sections = []
        for a in range(num_boxes):
            x = random.randint(0, img.shape[1])
            y = random.randint(0, img.shape[0])
            w = random.randint(0, img.shape[1] - x)
            h = random.randint(0, img.shape[0] - y)
            sections.append(((x, y, x + w, y + h), section_labels[a]))
        for b in range(num_segments):
            x = random.randint(0, img.shape[1])
            y = random.randint(0, img.shape[0])
            r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))
            mask = np.zeros(img.shape[:2])
            for i in range(img.shape[0]):
                for j in range(img.shape[1]):
                    dist_square = (i - y) ** 2 + (j - x) ** 2
                    if dist_square < r**2:
                        mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4
            sections.append((mask, section_labels[b + num_boxes]))
        return (img, sections)

    section_btn.click(section, [img_input, num_boxes, num_segments], img_output)

    def select_section(evt: gr.SelectData):
        return section_labels[evt.index]

    img_output.select(select_section, None, selected_section)


demo.launch()

Parameter	Description
`value` tuple[np.ndarray \| _Image.Image \| str, list[tuple[np.ndarray \| tuple[int, int, int, int], str]]] \| None default: None	Tuple of base image and list of (subsection, label) pairs.
`show_legend` bool default: True	If True, will show a legend of the subsections.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.AnnotatedImage`	"annotatedimage"	Uses default values

Methods

style

gradio.AnnotatedImage.style(···)

This method can be used to change the appearance of the Image component.

Parameter Description

Parameter	Description
`height` int \| None default: None	Height of the image.
`width` int \| None default: None	Width of the image.
`color_map` dict[str, str] \| None default: None	A dictionary mapping labels to colors. The colors must be specified as hex codes.


                height

int | None

default: None

Height of the image.


                width

int | None

default: None

Width of the image.


                color_map

dict[str, str] | None

default: None

A dictionary mapping labels to colors. The colors must be specified as hex codes.

select

gradio.AnnotatedImage.select(fn, ···)

Event listener for when the user selects Image subsection. Uses event data gradio.SelectData to carry `value` referring to selected subsection label, and `index` to refer to subsection index. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about AnnotatedImage

from math import log2, pow
import os

import numpy as np
from scipy.fftpack import fft

import gradio as gr

A4 = 440
C0 = A4 * pow(2, -4.75)
name = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]


def get_pitch(freq):
    h = round(12 * log2(freq / C0))
    n = h % 12
    return name[n]


def main_note(audio):
    rate, y = audio
    if len(y.shape) == 2:
        y = y.T[0]
    N = len(y)
    T = 1.0 / rate
    yf = fft(y)
    yf2 = 2.0 / N * np.abs(yf[0 : N // 2])
    xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2)

    volume_per_pitch = {}
    total_volume = np.sum(yf2)
    for freq, volume in zip(xf, yf2):
        if freq == 0:
            continue
        pitch = get_pitch(freq)
        if pitch not in volume_per_pitch:
            volume_per_pitch[pitch] = 0
        volume_per_pitch[pitch] += 1.0 * volume / total_volume
    volume_per_pitch = {k: float(v) for k, v in volume_per_pitch.items()}
    return volume_per_pitch


demo = gr.Interface(
    main_note,
    gr.Audio(source="microphone"),
    gr.Label(num_top_classes=4),
    examples=[
        [os.path.join(os.path.dirname(__file__),"audio/recording1.wav")],
        [os.path.join(os.path.dirname(__file__),"audio/cantina.wav")],
    ],
    interpretation="default",
)

if __name__ == "__main__":
    demo.launch()

import numpy as np
import gradio as gr

notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]

def generate_tone(note, octave, duration):
    sr = 48000
    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)
    frequency = a4_freq * 2 ** (tones_from_a4 / 12)
    duration = int(duration)
    audio = np.linspace(0, duration, duration * sr)
    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)
    return sr, audio

demo = gr.Interface(
    generate_tone,
    [
        gr.Dropdown(notes, type="index"),
        gr.Slider(4, 6, step=1),
        gr.Textbox(value=1, label="Duration in seconds"),
    ],
    "audio",
)
if __name__ == "__main__":
    demo.launch()

import os

import numpy as np

import gradio as gr


def reverse_audio(audio):
    sr, data = audio
    return (sr, np.flipud(data))


demo = gr.Interface(fn=reverse_audio, 
                    inputs="microphone", 
                    outputs="audio", 
                    examples=[
                    "https://samplelib.com/lib/preview/mp3/sample-3s.mp3",
                    os.path.join(os.path.dirname(__file__), "audio/recording1.wav")
        ], cache_examples=True)

if __name__ == "__main__":
    demo.launch()

Audio

gradio.Audio(···)

Creates an audio component that can be used to upload/record audio (as an input) or display audio (as an output).

As input: passes the uploaded audio as a Tuple(int, numpy.array) corresponding to (sample rate in Hz, audio data as a 16-bit int array whose values range from -32768 to 32767), or as a str filepath, depending on `type`.

As output: expects a Tuple(int, numpy.array) corresponding to (sample rate in Hz, audio data as a float or int numpy array) or as a str filepath or URL to an audio file, which gets displayed

Format expected for examples: a str filepath to a local file that contains audio.

Supported events: check_streamable()

from math import log2, pow
import os

import numpy as np
from scipy.fftpack import fft

import gradio as gr

A4 = 440
C0 = A4 * pow(2, -4.75)
name = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]


def get_pitch(freq):
    h = round(12 * log2(freq / C0))
    n = h % 12
    return name[n]


def main_note(audio):
    rate, y = audio
    if len(y.shape) == 2:
        y = y.T[0]
    N = len(y)
    T = 1.0 / rate
    yf = fft(y)
    yf2 = 2.0 / N * np.abs(yf[0 : N // 2])
    xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2)

    volume_per_pitch = {}
    total_volume = np.sum(yf2)
    for freq, volume in zip(xf, yf2):
        if freq == 0:
            continue
        pitch = get_pitch(freq)
        if pitch not in volume_per_pitch:
            volume_per_pitch[pitch] = 0
        volume_per_pitch[pitch] += 1.0 * volume / total_volume
    volume_per_pitch = {k: float(v) for k, v in volume_per_pitch.items()}
    return volume_per_pitch


demo = gr.Interface(
    main_note,
    gr.Audio(source="microphone"),
    gr.Label(num_top_classes=4),
    examples=[
        [os.path.join(os.path.dirname(__file__),"audio/recording1.wav")],
        [os.path.join(os.path.dirname(__file__),"audio/cantina.wav")],
    ],
    interpretation="default",
)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` str \| tuple[int, np.ndarray] \| Callable \| None default: None	A path, URL, or [sample_rate, numpy array] tuple (sample rate in Hz, audio data as a float or int numpy array) for the default value that Audio component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component.
`source` str default: "upload"	Source of audio. "upload" creates a box where user can drop an audio file, "microphone" creates a microphone input.
`type` str default: "numpy"	The format the audio file is converted to before being passed into the prediction function. "numpy" converts the audio to a tuple consisting of: (int sample rate, numpy.array for the data), "filepath" passes a str path to a temporary file containing the audio.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, will allow users to upload and edit a audio file; if False, can only be used to play audio. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`streaming` bool default: False	If set to True when used in a `live` interface, will automatically stream webcam feed. Only valid is source is 'microphone'.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Audio`	"audio"	Uses default values
`gradio.Microphone`	"microphone"	Uses source="microphone"

Methods

style

gradio.Audio.style(···)

This method can be used to change the appearance of the audio component.

change

gradio.Audio.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

clear

gradio.Audio.clear(fn, ···)

This event is triggered when the user clears the component (e.g. image or audio) using the X button for the component. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

play

gradio.Audio.play(fn, ···)

This event is triggered when the user plays the component (e.g. audio or video). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

pause

gradio.Audio.pause(fn, ···)

This event is triggered when the user pauses the component (e.g. audio or video). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

stop

gradio.Audio.stop(fn, ···)

This event is triggered when the user stops the component (e.g. audio or video). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

stream

gradio.Audio.stream(fn, ···)

This event is triggered when the user streams the component (e.g. a live webcam component). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

upload

gradio.Audio.upload(fn, ···)

This event is triggered when the user uploads a file into the component (e.g. when the user uploads a video into a video component). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

Real Time Speech Recognition

import gradio as gr

from scatter_plot_demo import scatter_plot
from line_plot_demo import line_plot
from bar_plot_demo import bar_plot


with gr.Blocks() as demo:
    with gr.Tabs():
        with gr.TabItem("Scatter Plot"):
            scatter_plot.render()
        with gr.TabItem("Line Plot"):
            line_plot.render()
        with gr.TabItem("Bar Plot"):
            bar_plot.render()

if __name__ == "__main__":
    demo.launch()

import os
import gradio as gr
import pandas as pd

DB_USER = os.getenv("DB_USER")
DB_PASSWORD = os.getenv("DB_PASSWORD")
DB_HOST = os.getenv("DB_HOST")
PORT = 8080
DB_NAME = "bikeshare"

connection_string = (
    f"postgresql://{DB_USER}:{DB_PASSWORD}@{DB_HOST}?port={PORT}&dbname={DB_NAME}"
)


def get_count_ride_type():
    df = pd.read_sql(
        """
        SELECT COUNT(ride_id) as n, rideable_type
        FROM rides
        GROUP BY rideable_type
        ORDER BY n DESC
    """,
        con=connection_string,
    )
    return df


def get_most_popular_stations():

    df = pd.read_sql(
        """
    SELECT COUNT(ride_id) as n, MAX(start_station_name) as station
    FROM RIDES
    WHERE start_station_name is NOT NULL
    GROUP BY start_station_id
    ORDER BY n DESC
    LIMIT 5
    """,
        con=connection_string,
    )
    return df


with gr.Blocks() as demo:
    gr.Markdown(
        """
    # Chicago Bike Share Dashboard
    
    This demo pulls Chicago bike share data for March 2022 from a postgresql database hosted on AWS.
    This demo uses psycopg2 but any postgresql client library (SQLAlchemy)
    is compatible with gradio.
    
    Connection credentials are handled by environment variables
    defined as secrets in the Space.

    If data were added to the database, the plots in this demo would update
    whenever the webpage is reloaded.
    
    This demo serves as a starting point for your database-connected apps!
    """
    )
    with gr.Row():
        bike_type = gr.BarPlot(
            x="rideable_type",
            y='n',
            title="Number of rides per bicycle type",
            y_title="Number of Rides",
            x_title="Bicycle Type",
            vertical=False,
            tooltip=['rideable_type', "n"],
            height=300,
            width=300,
        )
        station = gr.BarPlot(
            x='station',
            y='n',
            title="Most Popular Stations",
            y_title="Number of Rides",
            x_title="Station Name",
            vertical=False,
            tooltip=['station', 'n'],
            height=300,
            width=300
        )

    demo.load(get_count_ride_type, inputs=None, outputs=bike_type)
    demo.load(get_most_popular_stations, inputs=None, outputs=station)

if __name__ == "__main__":
    demo.launch()

BarPlot

gradio.BarPlot(···)

Create a bar plot.

As input: this component does *not* accept input.

As output: expects a pandas dataframe with the data to plot.

import gradio as gr

from scatter_plot_demo import scatter_plot
from line_plot_demo import line_plot
from bar_plot_demo import bar_plot


with gr.Blocks() as demo:
    with gr.Tabs():
        with gr.TabItem("Scatter Plot"):
            scatter_plot.render()
        with gr.TabItem("Line Plot"):
            line_plot.render()
        with gr.TabItem("Bar Plot"):
            bar_plot.render()

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` pd.DataFrame \| Callable \| None default: None	The pandas dataframe containing the data to display in a scatter plot.
`x` str \| None default: None	Column corresponding to the x axis.
`y` str \| None default: None	Column corresponding to the y axis.
`color` str \| None default: None	The column to determine the bar color. Must be categorical (discrete values).
`vertical` bool default: True	If True, the bars will be displayed vertically. If False, the x and y axis will be switched, displaying the bars horizontally. Default is True.
`group` str \| None default: None	The column with which to split the overall plot into smaller subplots.
`title` str \| None default: None	The title to display on top of the chart.
`tooltip` list[str] \| str \| None default: None	The column (or list of columns) to display on the tooltip when a user hovers over a bar.
`x_title` str \| None default: None	The title given to the x axis. By default, uses the value of the x parameter.
`y_title` str \| None default: None	The title given to the y axis. By default, uses the value of the y parameter.
`color_legend_title` str \| None default: None	The title given to the color legend. By default, uses the value of color parameter.
`group_title` str \| None default: None	The label displayed on top of the subplot columns (or rows if vertical=True). Use an empty string to omit.
`color_legend_position` str \| None default: None	The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
`height` int \| None default: None	The height of the plot in pixels.
`width` int \| None default: None	The width of the plot in pixels.
`y_lim` list[int] \| None default: None	A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
`caption` str \| None default: None	The (optional) caption to display below the plot.
`interactive` bool \| None default: True	Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
`label` str \| None default: None	The (optional) label to display on the top left corner of the plot.
`show_label` bool default: True	Whether the label should be displayed.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`visible` bool default: True	Whether the plot should be visible.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.BarPlot`	"barplot"	Uses default values

Methods

change

gradio.BarPlot.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

clear

gradio.BarPlot.clear(fn, ···)

This event is triggered when the user clears the component (e.g. image or audio) using the X button for the component. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about BarPlot

import gradio as gr
import os


def combine(a, b):
    return a + " " + b


def mirror(x):
    return x


with gr.Blocks() as demo:

    txt = gr.Textbox(label="Input", lines=2)
    txt_2 = gr.Textbox(label="Input 2")
    txt_3 = gr.Textbox(value="", label="Output")
    btn = gr.Button(value="Submit")
    btn.click(combine, inputs=[txt, txt_2], outputs=[txt_3])

    with gr.Row():
        im = gr.Image()
        im_2 = gr.Image()

    btn = gr.Button(value="Mirror Image")
    btn.click(mirror, inputs=[im], outputs=[im_2])

    gr.Markdown("## Text Examples")
    gr.Examples(
        [["hi", "Adam"], ["hello", "Eve"]],
        [txt, txt_2],
        txt_3,
        combine,
        cache_examples=True,
    )
    gr.Markdown("## Image Examples")
    gr.Examples(
        examples=[os.path.join(os.path.dirname(__file__), "lion.jpg")],
        inputs=im,
        outputs=im_2,
        fn=mirror,
        cache_examples=True,
    )

if __name__ == "__main__":
    demo.launch()

import pandas as pd
import numpy as np

import gradio as gr


def plot(v, a):
    g = 9.81
    theta = a / 180 * 3.14
    tmax = ((2 * v) * np.sin(theta)) / g
    timemat = tmax * np.linspace(0, 1, 40)

    x = (v * timemat) * np.cos(theta)
    y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))
    df = pd.DataFrame({"x": x, "y": y})
    return df


demo = gr.Blocks()

with demo:
    gr.Markdown(
        r"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \cdot \frac{\sin(2\theta)}{g}$"
    )

    with gr.Row():
        speed = gr.Slider(1, 30, 25, label="Speed")
        angle = gr.Slider(0, 90, 45, label="Angle")
    output = gr.LinePlot(
        x="x",
        y="y",
        overlay_point=True,
        tooltip=["x", "y"],
        x_lim=[0, 100],
        y_lim=[0, 60],
        width=350,
        height=300,
    )
    btn = gr.Button(value="Run")
    btn.click(plot, [speed, angle], output)

if __name__ == "__main__":
    demo.launch()

Button

gradio.Button(···)

Used to create a button, that can be assigned arbitrary click() events. The label (value) of the button can be used as an input or set via the output of a function.

As input: passes the button value as a str into the function

As output: expects a str to be returned from a function, which is set as the label of the button

import gradio as gr
import os


def combine(a, b):
    return a + " " + b


def mirror(x):
    return x


with gr.Blocks() as demo:

    txt = gr.Textbox(label="Input", lines=2)
    txt_2 = gr.Textbox(label="Input 2")
    txt_3 = gr.Textbox(value="", label="Output")
    btn = gr.Button(value="Submit")
    btn.click(combine, inputs=[txt, txt_2], outputs=[txt_3])

    with gr.Row():
        im = gr.Image()
        im_2 = gr.Image()

    btn = gr.Button(value="Mirror Image")
    btn.click(mirror, inputs=[im], outputs=[im_2])

    gr.Markdown("## Text Examples")
    gr.Examples(
        [["hi", "Adam"], ["hello", "Eve"]],
        [txt, txt_2],
        txt_3,
        combine,
        cache_examples=True,
    )
    gr.Markdown("## Image Examples")
    gr.Examples(
        examples=[os.path.join(os.path.dirname(__file__), "lion.jpg")],
        inputs=im,
        outputs=im_2,
        fn=mirror,
        cache_examples=True,
    )

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` str \| Callable default: "Run"	Default text for the button to display. If callable, the function will be called whenever the app loads to set the initial value of the component.
`variant` str default: "secondary"	'primary' for main call-to-action, 'secondary' for a more subdued style, 'stop' for a stop button.
`visible` bool default: True	If False, component will be hidden.
`interactive` bool default: True	If False, the Button will be in a disabled state.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Button`	"button"	Uses default values

Methods

style

gradio.Button.style(···)

This method can be used to change the appearance of the button component.

Parameter Description

Parameter	Description
`full_width` bool \| None default: None	If True, will expand to fill parent container.
`size` Literal['sm'] \| Literal['lg'] \| None default: None	Size of the button. Can be "sm" or "lg".


                full_width

bool | None

default: None

If True, will expand to fill parent container.


                size

Literal['sm'] | Literal['lg'] | None

default: None

Size of the button. Can be "sm" or "lg".

click

gradio.Button.click(fn, ···)

This event is triggered when the component (e.g. a button) is clicked. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about Button

import gradio as gr
import random
import time

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.Button("Clear")

    def respond(message, chat_history):
        bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
        chat_history.append((message, bot_message))
        time.sleep(1)
        return "", chat_history

    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

if __name__ == "__main__":
    demo.launch()

import gradio as gr

def add_text(history, text):
    history = history + [(text, None)]
    return history, ""

def add_file(history, file):
    history = history + [((file.name,), None)]
    return history

def bot(history):
    response = "**That's cool!**"
    history[-1][1] = response
    return history

with gr.Blocks() as demo:
    chatbot = gr.Chatbot([], elem_id="chatbot").style(height=750)
    
    with gr.Row():
        with gr.Column(scale=0.85):
            txt = gr.Textbox(
                show_label=False,
                placeholder="Enter text and press enter, or upload an image",
            ).style(container=False)
        with gr.Column(scale=0.15, min_width=0):
            btn = gr.UploadButton("📁", file_types=["image", "video", "audio"])
            
    txt.submit(add_text, [chatbot, txt], [chatbot, txt]).then(
        bot, chatbot, chatbot
    )
    btn.upload(add_file, [chatbot, btn], [chatbot]).then(
        bot, chatbot, chatbot
    )

if __name__ == "__main__":
    demo.launch()

Chatbot

gradio.Chatbot(···)

Displays a chatbot output showing both user submitted messages and responses. Supports a subset of Markdown including bold, italics, code, and images.

As input: this component does *not* accept input.

As output: expects function to return a List[List[str | None | Tuple]], a list of lists. The inner list should have 2 elements: the user message and the response message. Messages should be strings, tuples, or Nones. If the message is a string, it can include Markdown. If it is a tuple, it should consist of (string filepath to image/video/audio, [optional string alt text]). Messages that are `None` are not displayed.

import gradio as gr
import random
import time

with gr.Blocks() as demo:
    chatbot = gr.Chatbot()
    msg = gr.Textbox()
    clear = gr.Button("Clear")

    def respond(message, chat_history):
        bot_message = random.choice(["How are you?", "I love you", "I'm very hungry"])
        chat_history.append((message, bot_message))
        time.sleep(1)
        return "", chat_history

    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` list[list[str \| tuple[str] \| tuple[str, str] \| None]] \| Callable \| None default: None	Default value to show in chatbot. If callable, the function will be called whenever the app loads to set the initial value of the component.
`color_map` dict[str, str] \| None default: None
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Chatbot`	"chatbot"	Uses default values

Methods

style

gradio.Chatbot.style(···)

This method can be used to change the appearance of the Chatbot component.

Parameter Description


                height

int | None

default: None

change

gradio.Chatbot.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

select

gradio.Chatbot.select(fn, ···)

Event listener for when the user selects message from Chatbot. Uses event data gradio.SelectData to carry `value` referring to text of selected message, and `index` tuple to refer to [message, participant] index. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

Creating A Chatbot

import gradio as gr


def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose betwen 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

if __name__ == "__main__":
    demo.launch()

import os

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

import gradio as gr

current_dir = os.path.dirname(os.path.realpath(__file__))
data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv"))


def encode_age(df):
    df.Age = df.Age.fillna(-0.5)
    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
    categories = pd.cut(df.Age, bins, labels=False)
    df.Age = categories
    return df


def encode_fare(df):
    df.Fare = df.Fare.fillna(-0.5)
    bins = (-1, 0, 8, 15, 31, 1000)
    categories = pd.cut(df.Fare, bins, labels=False)
    df.Fare = categories
    return df


def encode_df(df):
    df = encode_age(df)
    df = encode_fare(df)
    sex_mapping = {"male": 0, "female": 1}
    df = df.replace({"Sex": sex_mapping})
    embark_mapping = {"S": 1, "C": 2, "Q": 3}
    df = df.replace({"Embarked": embark_mapping})
    df.Embarked = df.Embarked.fillna(0)
    df["Company"] = 0
    df.loc[(df["SibSp"] > 0), "Company"] = 1
    df.loc[(df["Parch"] > 0), "Company"] = 2
    df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
    df = df[
        [
            "PassengerId",
            "Pclass",
            "Sex",
            "Age",
            "Fare",
            "Embarked",
            "Company",
            "Survived",
        ]
    ]
    return df


train = encode_df(data)

X_all = train.drop(["Survived", "PassengerId"], axis=1)
y_all = train["Survived"]

num_test = 0.20
X_train, X_test, y_train, y_test = train_test_split(
    X_all, y_all, test_size=num_test, random_state=23
)

clf = RandomForestClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)


def predict_survival(passenger_class, is_male, age, company, fare, embark_point):
    if passenger_class is None or embark_point is None:
        return None
    df = pd.DataFrame.from_dict(
        {
            "Pclass": [passenger_class + 1],
            "Sex": [0 if is_male else 1],
            "Age": [age],
            "Fare": [fare],
            "Embarked": [embark_point + 1],
            "Company": [
                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
            ]
        }
    )
    df = encode_age(df)
    df = encode_fare(df)
    pred = clf.predict_proba(df)[0]
    return {"Perishes": float(pred[0]), "Survives": float(pred[1])}


demo = gr.Interface(
    predict_survival,
    [
        gr.Dropdown(["first", "second", "third"], type="index"),
        "checkbox",
        gr.Slider(0, 80, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        gr.Radio(["S", "C", "Q"], type="index"),
    ],
    "label",
    examples=[
        ["first", True, 30, [], 50, "S"],
        ["second", False, 40, ["Sibling", "Child"], 10, "Q"],
        ["third", True, 30, ["Child"], 20, "S"],
    ],
    interpretation="default",
    live=True,
)

if __name__ == "__main__":
    demo.launch()

Checkbox

gradio.Checkbox(···)

Creates a checkbox that can be set to `True` or `False`.

As input: passes the status of the checkbox as a bool into the function.

As output: expects a bool returned from the function and, if it is True, checks the checkbox.

Format expected for examples: a bool representing whether the box is checked.

import gradio as gr


def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose betwen 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` bool \| Callable default: False	if True, checked by default. If callable, the function will be called whenever the app loads to set the initial value of the component.
`label` str \| None default: None	component name in interface.
`info` str \| None default: None	additional component description.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, this checkbox can be checked; if False, checking will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Checkbox`	"checkbox"	Uses default values

Methods

style

gradio.Checkbox.style(···)

This method can be used to change the appearance of the component.

Parameter Description


                container

bool | None

default: None

If True, will place the component in a container - providing some extra padding around the border.

change

gradio.Checkbox.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

select

gradio.Checkbox.select(fn, ···)

Event listener for when the user selects or deselects Checkbox. Uses event data gradio.SelectData to carry `value` referring to label of checkbox, and `selected` to refer to state of checkbox. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about Checkbox

import gradio as gr


def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose betwen 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

if __name__ == "__main__":
    demo.launch()

import os

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

import gradio as gr

current_dir = os.path.dirname(os.path.realpath(__file__))
data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv"))


def encode_age(df):
    df.Age = df.Age.fillna(-0.5)
    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
    categories = pd.cut(df.Age, bins, labels=False)
    df.Age = categories
    return df


def encode_fare(df):
    df.Fare = df.Fare.fillna(-0.5)
    bins = (-1, 0, 8, 15, 31, 1000)
    categories = pd.cut(df.Fare, bins, labels=False)
    df.Fare = categories
    return df


def encode_df(df):
    df = encode_age(df)
    df = encode_fare(df)
    sex_mapping = {"male": 0, "female": 1}
    df = df.replace({"Sex": sex_mapping})
    embark_mapping = {"S": 1, "C": 2, "Q": 3}
    df = df.replace({"Embarked": embark_mapping})
    df.Embarked = df.Embarked.fillna(0)
    df["Company"] = 0
    df.loc[(df["SibSp"] > 0), "Company"] = 1
    df.loc[(df["Parch"] > 0), "Company"] = 2
    df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
    df = df[
        [
            "PassengerId",
            "Pclass",
            "Sex",
            "Age",
            "Fare",
            "Embarked",
            "Company",
            "Survived",
        ]
    ]
    return df


train = encode_df(data)

X_all = train.drop(["Survived", "PassengerId"], axis=1)
y_all = train["Survived"]

num_test = 0.20
X_train, X_test, y_train, y_test = train_test_split(
    X_all, y_all, test_size=num_test, random_state=23
)

clf = RandomForestClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)


def predict_survival(passenger_class, is_male, age, company, fare, embark_point):
    if passenger_class is None or embark_point is None:
        return None
    df = pd.DataFrame.from_dict(
        {
            "Pclass": [passenger_class + 1],
            "Sex": [0 if is_male else 1],
            "Age": [age],
            "Fare": [fare],
            "Embarked": [embark_point + 1],
            "Company": [
                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
            ]
        }
    )
    df = encode_age(df)
    df = encode_fare(df)
    pred = clf.predict_proba(df)[0]
    return {"Perishes": float(pred[0]), "Survives": float(pred[1])}


demo = gr.Interface(
    predict_survival,
    [
        gr.Dropdown(["first", "second", "third"], type="index"),
        "checkbox",
        gr.Slider(0, 80, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        gr.Radio(["S", "C", "Q"], type="index"),
    ],
    "label",
    examples=[
        ["first", True, 30, [], 50, "S"],
        ["second", False, 40, ["Sibling", "Child"], 10, "Q"],
        ["third", True, 30, ["Child"], 20, "S"],
    ],
    interpretation="default",
    live=True,
)

if __name__ == "__main__":
    demo.launch()

CheckboxGroup

gradio.CheckboxGroup(···)

Creates a set of checkboxes of which a subset can be checked.

As input: passes the list of checked checkboxes as a List[str] or their indices as a List[int] into the function, depending on `type`.

As output: expects a List[str], each element of which becomes a checked checkbox.

Format expected for examples: a List[str] representing the values to be checked.

import gradio as gr


def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose betwen 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`choices` list[str] \| None default: None	list of options to select from.
`value` list[str] \| str \| Callable \| None default: None	default selected list of options. If callable, the function will be called whenever the app loads to set the initial value of the component.
`type` str default: "value"	Type of value to be returned by component. "value" returns the list of strings of the choices selected, "index" returns the list of indicies of the choices selected.
`label` str \| None default: None	component name in interface.
`info` str \| None default: None	additional component description.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, choices in this checkbox group will be checkable; if False, checking will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.CheckboxGroup`	"checkboxgroup"	Uses default values

Methods

style

gradio.CheckboxGroup.style(···)

This method can be used to change the appearance of the CheckboxGroup.

Parameter Description


                item_container

bool | None

default: None

If True, will place the items in a container.


                container

bool | None

default: None

If True, will place the component in a container - providing some extra padding around the border.

change

gradio.CheckboxGroup.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

select

gradio.CheckboxGroup.select(fn, ···)

Event listener for when the user selects or deselects within CheckboxGroup. Uses event data gradio.SelectData to carry `value` referring to label of selected checkbox, `index` to refer to index, and `selected` to refer to state of checkbox. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

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Code

gradio.Code(···)

Creates a Code editor for entering, editing or viewing code.

As input: passes a str of code into the function.

As output: expects the function to return a str of code or a single-elment tuple: (string filepath,)

Parameter	Description
`value` str \| tuple[str] \| None default: None	Default value to show in the code editor. If callable, the function will be called whenever the app loads to set the initial value of the component.
`language` str \| None default: None	The language to display the code as. Supported languages listed in `gr.Code.languages`.
`lines` int default: 5
`label` str \| None default: None	component name in interface.
`interactive` bool \| None default: None	Whether user should be able to enter code or only view it.
`show_label` bool default: True	if True, will display label.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Code`	"code"	Uses default values

Methods

languages

gr.Code.languages

['python', 'markdown', 'json', 'html', 'css', 'javascript', 'typescript', 'yaml', 'dockerfile', 'shell', 'r', None]

change

gradio.Code.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

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import gradio as gr
import numpy as np
import os
from PIL import Image, ImageColor


def change_color(icon, color):

    """
    Function that given an icon in .png format changes its color
    Args:
        icon: Icon whose color needs to be changed.
        color: Chosen color with which to edit the input icon.
    Returns:
        edited_image: Edited icon.
    """
    img = icon.convert("LA")
    img = img.convert("RGBA")
    image_np = np.array(icon)
    _, _, _, alpha = image_np.T
    mask = alpha > 0
    image_np[..., :-1][mask.T] = ImageColor.getcolor(color, "RGB")
    edited_image = Image.fromarray(image_np)
    return edited_image


inputs = [
    gr.Image(label="icon", type="pil", image_mode="RGBA"),
    gr.ColorPicker(label="color"),
]
outputs = gr.Image(label="colored icon")

demo = gr.Interface(
    fn=change_color,
    inputs=inputs,
    outputs=outputs,
    examples=[
        [os.path.join(os.path.dirname(__file__), "rabbit.png"), "#ff0000"],
        [os.path.join(os.path.dirname(__file__), "rabbit.png"), "#0000FF"],
    ],
)

if __name__ == "__main__":
    demo.launch()

import gradio as gr
import cv2
import numpy as np
import random


# Convert decimal color to hexadecimal color
def RGB_to_Hex(rgb):
    color = "#"
    for i in rgb:
        num = int(i)
        color += str(hex(num))[-2:].replace("x", "0").upper()
    return color


# Randomly generate light or dark colors
def random_color(is_light=True):
    return (
        random.randint(0, 127) + int(is_light) * 128,
        random.randint(0, 127) + int(is_light) * 128,
        random.randint(0, 127) + int(is_light) * 128,
    )


def switch_color(color_style):
    if color_style == "light":
        is_light = True
    elif color_style == "dark":
        is_light = False
    back_color_ = random_color(is_light)  # Randomly generate colors
    back_color = RGB_to_Hex(back_color_)  # Convert to hexadecimal

    # Draw color pictures.
    w, h = 50, 50
    img = np.zeros((h, w, 3), np.uint8)
    cv2.rectangle(img, (0, 0), (w, h), back_color_, thickness=-1)

    return back_color, back_color, img


inputs = [gr.Radio(["light", "dark"], value="light")]

outputs = [
    gr.ColorPicker(label="color"),
    gr.Textbox(label="hexadecimal color"),
    gr.Image(type="numpy", label="color picture"),
]

title = "Color Generator"
description = (
    "Click the Submit button, and a dark or light color will be randomly generated."
)

demo = gr.Interface(
    fn=switch_color,
    inputs=inputs,
    outputs=outputs,
    title=title,
    description=description,
)

if __name__ == "__main__":
    demo.launch()

ColorPicker

gradio.ColorPicker(···)

Creates a color picker for user to select a color as string input.

As input: passes selected color value as a str into the function.

As output: expects a str returned from function and sets color picker value to it.

Format expected for examples: a str with a hexadecimal representation of a color, e.g. "#ff0000" for red.

import gradio as gr
import numpy as np
import os
from PIL import Image, ImageColor


def change_color(icon, color):

    """
    Function that given an icon in .png format changes its color
    Args:
        icon: Icon whose color needs to be changed.
        color: Chosen color with which to edit the input icon.
    Returns:
        edited_image: Edited icon.
    """
    img = icon.convert("LA")
    img = img.convert("RGBA")
    image_np = np.array(icon)
    _, _, _, alpha = image_np.T
    mask = alpha > 0
    image_np[..., :-1][mask.T] = ImageColor.getcolor(color, "RGB")
    edited_image = Image.fromarray(image_np)
    return edited_image


inputs = [
    gr.Image(label="icon", type="pil", image_mode="RGBA"),
    gr.ColorPicker(label="color"),
]
outputs = gr.Image(label="colored icon")

demo = gr.Interface(
    fn=change_color,
    inputs=inputs,
    outputs=outputs,
    examples=[
        [os.path.join(os.path.dirname(__file__), "rabbit.png"), "#ff0000"],
        [os.path.join(os.path.dirname(__file__), "rabbit.png"), "#0000FF"],
    ],
)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` str \| Callable \| None default: None	default text to provide in color picker. If callable, the function will be called whenever the app loads to set the initial value of the component.
`label` str \| None default: None	component name in interface.
`info` str \| None default: None	additional component description.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, will be rendered as an editable color picker; if False, editing will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.ColorPicker`	"colorpicker"	Uses default values

Methods

style

gradio.ColorPicker.style(···)

This method can be used to change the appearance of the component.

Parameter Description


                container

bool | None

default: None

If True, will place the component in a container - providing some extra padding around the border.

change

gradio.ColorPicker.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

submit

gradio.ColorPicker.submit(fn, ···)

This event is triggered when the user presses the Enter key while the component (e.g. a textbox) is focused. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

blur

gradio.ColorPicker.blur(fn, ···)

This event is triggered when the component's is unfocused/blurred (e.g. when the user clicks outside of a textbox). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about ColorPicker

import gradio as gr


def filter_records(records, gender):
    return records[records["gender"] == gender]


demo = gr.Interface(
    filter_records,
    [
        gr.Dataframe(
            headers=["name", "age", "gender"],
            datatype=["str", "number", "str"],
            row_count=5,
            col_count=(3, "fixed"),
        ),
        gr.Dropdown(["M", "F", "O"]),
    ],
    "dataframe",
    description="Enter gender as 'M', 'F', or 'O' for other.",
)

if __name__ == "__main__":
    demo.launch()

import numpy as np

import gradio as gr


def transpose(matrix):
    return matrix.T


demo = gr.Interface(
    transpose,
    gr.Dataframe(type="numpy", datatype="number", row_count=5, col_count=3),
    "numpy",
    examples=[
        [np.zeros((3, 3)).tolist()],
        [np.ones((2, 2)).tolist()],
        [np.random.randint(0, 10, (3, 10)).tolist()],
        [np.random.randint(0, 10, (10, 3)).tolist()],
        [np.random.randint(0, 10, (10, 10)).tolist()],
    ],
    cache_examples=False
)

if __name__ == "__main__":
    demo.launch()

import gradio as gr

def tax_calculator(income, marital_status, assets):
    tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]
    total_deductible = sum(assets["Cost"])
    taxable_income = income - total_deductible

    total_tax = 0
    for bracket, rate in tax_brackets:
        if taxable_income > bracket:
            total_tax += (taxable_income - bracket) * rate / 100

    if marital_status == "Married":
        total_tax *= 0.75
    elif marital_status == "Divorced":
        total_tax *= 0.8

    return round(total_tax)

demo = gr.Interface(
    tax_calculator,
    [
        "number",
        gr.Radio(["Single", "Married", "Divorced"]),
        gr.Dataframe(
            headers=["Item", "Cost"],
            datatype=["str", "number"],
            label="Assets Purchased this Year",
        ),
    ],
    "number",
    examples=[
        [10000, "Married", [["Suit", 5000], ["Laptop", 800], ["Car", 1800]]],
        [80000, "Single", [["Suit", 800], ["Watch", 1800], ["Car", 800]]],
    ],
)

demo.launch()

Dataframe

gradio.Dataframe(···)

Accepts or displays 2D input through a spreadsheet-like component for dataframes.

As input: passes the uploaded spreadsheet data as a pandas.DataFrame, numpy.array, List[List], or List depending on `type`

As output: expects a pandas.DataFrame, numpy.array, List[List], List, a Dict with keys `data` (and optionally `headers`), or str path to a csv, which is rendered in the spreadsheet.

Format expected for examples: a str filepath to a csv with data, a pandas dataframe, or a list of lists (excluding headers) where each sublist is a row of data.

import gradio as gr


def filter_records(records, gender):
    return records[records["gender"] == gender]


demo = gr.Interface(
    filter_records,
    [
        gr.Dataframe(
            headers=["name", "age", "gender"],
            datatype=["str", "number", "str"],
            row_count=5,
            col_count=(3, "fixed"),
        ),
        gr.Dropdown(["M", "F", "O"]),
    ],
    "dataframe",
    description="Enter gender as 'M', 'F', or 'O' for other.",
)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` list[list[Any]] \| Callable \| None default: None	Default value as a 2-dimensional list of values. If callable, the function will be called whenever the app loads to set the initial value of the component.
`headers` list[str] \| None default: None	List of str header names. If None, no headers are shown.
`row_count` int \| tuple[int, str] default: (1, 'dynamic')	Limit number of rows for input and decide whether user can create new rows. The first element of the tuple is an `int`, the row count; the second should be 'fixed' or 'dynamic', the new row behaviour. If an `int` is passed the rows default to 'dynamic'
`col_count` int \| tuple[int, str] \| None default: None	Limit number of columns for input and decide whether user can create new columns. The first element of the tuple is an `int`, the number of columns; the second should be 'fixed' or 'dynamic', the new column behaviour. If an `int` is passed the columns default to 'dynamic'
`datatype` str \| list[str] default: "str"	Datatype of values in sheet. Can be provided per column as a list of strings, or for the entire sheet as a single string. Valid datatypes are "str", "number", "bool", "date", and "markdown".
`type` str default: "pandas"	Type of value to be returned by component. "pandas" for pandas dataframe, "numpy" for numpy array, or "array" for a Python array.
`max_rows` int \| None default: 20	Maximum number of rows to display at once. Set to None for infinite.
`max_cols` int \| None default: None	Maximum number of columns to display at once. Set to None for infinite.
`overflow_row_behaviour` str default: "paginate"	If set to "paginate", will create pages for overflow rows. If set to "show_ends", will show initial and final rows and truncate middle rows.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, will allow users to edit the dataframe; if False, can only be used to display data. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
`wrap` bool default: False	if True text in table cells will wrap when appropriate, if False the table will scroll horiztonally. Defaults to False.

Class	Interface String Shortcut	Initialization
`gradio.Dataframe`	"dataframe"	Uses default values
`gradio.Numpy`	"numpy"	Uses type="numpy"
`gradio.Matrix`	"matrix"	Uses type="array"
`gradio.List`	"list"	Uses type="array", col_count=1

Methods

style

gradio.Dataframe.style(···)

This method can be used to change the appearance of the DataFrame component.

change

gradio.Dataframe.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

select

gradio.Dataframe.select(fn, ···)

Event listener for when the user selects cell within Dataframe. Uses event data gradio.SelectData to carry `value` referring to value of selected cell, and `index` tuple to refer to index row and column. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about Dataframe

Dataset

gr.Dataset(components, samples)

Used to create an output widget for showing datasets. Used to render the examples box.

As input: passes the selected sample either as a list of data (if type="value") or as an int index (if type="index")

As output: expects a list of lists corresponding to the dataset data.

Parameter	Description
`label` str \| None default: None
`components` list[IOComponent] \| list[str] required	Which component types to show in this dataset widget, can be passed in as a list of string names or Components instances. The following components are supported in a Dataset: Audio, Checkbox, CheckboxGroup, ColorPicker, Dataframe, Dropdown, File, HTML, Image, Markdown, Model3D, Number, Radio, Slider, Textbox, TimeSeries, Video
`samples` list[list[Any]] \| None default: None	a nested list of samples. Each sublist within the outer list represents a data sample, and each element within the sublist represents an value for each component
`headers` list[str] \| None default: None	Column headers in the Dataset widget, should be the same len as components. If not provided, inferred from component labels
`type` str default: "values"	'values' if clicking on a sample should pass the value of the sample, or "index" if it should pass the index of the sample
`samples_per_page` int default: 10	how many examples to show per page.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Dataset`	"dataset"	Uses default values

Methods

style

gradio.Dataset.style(···)

This method can be used to change the appearance of the Dataset component.

click

gradio.Dataset.click(fn, ···)

This event is triggered when the component (e.g. a button) is clicked. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

select

gradio.Dataset.select(fn, ···)

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about Dataset

import gradio as gr


def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose betwen 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

if __name__ == "__main__":
    demo.launch()

import os

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

import gradio as gr

current_dir = os.path.dirname(os.path.realpath(__file__))
data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv"))


def encode_age(df):
    df.Age = df.Age.fillna(-0.5)
    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
    categories = pd.cut(df.Age, bins, labels=False)
    df.Age = categories
    return df


def encode_fare(df):
    df.Fare = df.Fare.fillna(-0.5)
    bins = (-1, 0, 8, 15, 31, 1000)
    categories = pd.cut(df.Fare, bins, labels=False)
    df.Fare = categories
    return df


def encode_df(df):
    df = encode_age(df)
    df = encode_fare(df)
    sex_mapping = {"male": 0, "female": 1}
    df = df.replace({"Sex": sex_mapping})
    embark_mapping = {"S": 1, "C": 2, "Q": 3}
    df = df.replace({"Embarked": embark_mapping})
    df.Embarked = df.Embarked.fillna(0)
    df["Company"] = 0
    df.loc[(df["SibSp"] > 0), "Company"] = 1
    df.loc[(df["Parch"] > 0), "Company"] = 2
    df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
    df = df[
        [
            "PassengerId",
            "Pclass",
            "Sex",
            "Age",
            "Fare",
            "Embarked",
            "Company",
            "Survived",
        ]
    ]
    return df


train = encode_df(data)

X_all = train.drop(["Survived", "PassengerId"], axis=1)
y_all = train["Survived"]

num_test = 0.20
X_train, X_test, y_train, y_test = train_test_split(
    X_all, y_all, test_size=num_test, random_state=23
)

clf = RandomForestClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)


def predict_survival(passenger_class, is_male, age, company, fare, embark_point):
    if passenger_class is None or embark_point is None:
        return None
    df = pd.DataFrame.from_dict(
        {
            "Pclass": [passenger_class + 1],
            "Sex": [0 if is_male else 1],
            "Age": [age],
            "Fare": [fare],
            "Embarked": [embark_point + 1],
            "Company": [
                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
            ]
        }
    )
    df = encode_age(df)
    df = encode_fare(df)
    pred = clf.predict_proba(df)[0]
    return {"Perishes": float(pred[0]), "Survives": float(pred[1])}


demo = gr.Interface(
    predict_survival,
    [
        gr.Dropdown(["first", "second", "third"], type="index"),
        "checkbox",
        gr.Slider(0, 80, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        gr.Radio(["S", "C", "Q"], type="index"),
    ],
    "label",
    examples=[
        ["first", True, 30, [], 50, "S"],
        ["second", False, 40, ["Sibling", "Child"], 10, "Q"],
        ["third", True, 30, ["Child"], 20, "S"],
    ],
    interpretation="default",
    live=True,
)

if __name__ == "__main__":
    demo.launch()

gradio.Dropdown(···)

Creates a dropdown of choices from which entries can be selected.

As input: passes the value of the selected dropdown entry as a str or its index as an int into the function, depending on `type`.

As output: expects a str corresponding to the value of the dropdown entry to be selected.

Format expected for examples: a str representing the drop down value to select.

import gradio as gr


def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose betwen 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`choices` list[str] \| None default: None	list of options to select from.
`value` str \| list[str] \| Callable \| None default: None	default value(s) selected in dropdown. If None, no value is selected by default. If callable, the function will be called whenever the app loads to set the initial value of the component.
`type` str default: "value"	Type of value to be returned by component. "value" returns the string of the choice selected, "index" returns the index of the choice selected.
`multiselect` bool \| None default: None	if True, multiple choices can be selected.
`max_choices` int \| None default: None	maximum number of choices that can be selected. If None, no limit is enforced.
`label` str \| None default: None	component name in interface.
`info` str \| None default: None	additional component description.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, choices in this dropdown will be selectable; if False, selection will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
`allow_custom_value` bool default: False	If True, allows user to enter a custom value that is not in the list of choices.

Class	Interface String Shortcut	Initialization
`gradio.Dropdown`	"dropdown"	Uses default values

Methods

gradio.Dropdown.style(···)

This method can be used to change the appearance of the Dropdown.

Parameter Description


                container

bool | None

default: None

If True, will place the component in a container - providing some extra padding around the border.

gradio.Dropdown.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

gradio.Dropdown.blur(fn, ···)

This event is triggered when the component's is unfocused/blurred (e.g. when the user clicks outside of a textbox). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

gradio.Dropdown.select(fn, ···)

Event listener for when the user selects Dropdown option. Uses event data gradio.SelectData to carry `value` referring to label of selected option, and `index` to refer to index. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about Dropdown

from zipfile import ZipFile

import gradio as gr


def zip_to_json(file_obj):
    files = []
    with ZipFile(file_obj.name) as zfile:
        for zinfo in zfile.infolist():
            files.append(
                {
                    "name": zinfo.filename,
                    "file_size": zinfo.file_size,
                    "compressed_size": zinfo.compress_size,
                }
            )
    return files


demo = gr.Interface(zip_to_json, "file", "json")

if __name__ == "__main__":
    demo.launch()

import os
from zipfile import ZipFile

import gradio as gr


def zip_files(files):
    with ZipFile("tmp.zip", "w") as zipObj:
        for idx, file in enumerate(files):
            zipObj.write(file.name, file.name.split("/")[-1])
    return "tmp.zip"

demo = gr.Interface(
    zip_files,
    gr.File(file_count="multiple", file_types=["text", ".json", ".csv"]),
    "file",
    examples=[[[os.path.join(os.path.dirname(__file__),"files/titanic.csv"), 
    os.path.join(os.path.dirname(__file__),"files/titanic.csv"), 
    os.path.join(os.path.dirname(__file__),"files/titanic.csv")]]], 
    cache_examples=True
)

if __name__ == "__main__":
    demo.launch()

File

gradio.File(···)

Creates a file component that allows uploading generic file (when used as an input) and or displaying generic files (output).

As input: passes the uploaded file as a tempfile._TemporaryFileWrapper or List[tempfile._TemporaryFileWrapper] depending on `file_count` (or a bytes/Listbytes depending on `type`)

As output: expects function to return a str path to a file, or List[str] consisting of paths to files.

Format expected for examples: a str path to a local file that populates the component.

from zipfile import ZipFile

import gradio as gr


def zip_to_json(file_obj):
    files = []
    with ZipFile(file_obj.name) as zfile:
        for zinfo in zfile.infolist():
            files.append(
                {
                    "name": zinfo.filename,
                    "file_size": zinfo.file_size,
                    "compressed_size": zinfo.compress_size,
                }
            )
    return files


demo = gr.Interface(zip_to_json, "file", "json")

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` str \| list[str] \| Callable \| None default: None	Default file to display, given as str file path. If callable, the function will be called whenever the app loads to set the initial value of the component.
`file_count` str default: "single"	if single, allows user to upload one file. If "multiple", user uploads multiple files. If "directory", user uploads all files in selected directory. Return type will be list for each file in case of "multiple" or "directory".
`file_types` list[str] \| None default: None	List of file extensions or types of files to be uploaded (e.g. ['image', '.json', '.mp4']). "file" allows any file to be uploaded, "image" allows only image files to be uploaded, "audio" allows only audio files to be uploaded, "video" allows only video files to be uploaded, "text" allows only text files to be uploaded.
`type` str default: "file"	Type of value to be returned by component. "file" returns a temporary file object with the same base name as the uploaded file, whose full path can be retrieved by file_obj.name, "binary" returns an bytes object.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, will allow users to upload a file; if False, can only be used to display files. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.File`	"file"	Uses default values
`gradio.Files`	"files"	Uses file_count="multiple"

Methods

style

gradio.File.style(···)

This method can be used to change the appearance of the file component.

change

gradio.File.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

clear

gradio.File.clear(fn, ···)

This event is triggered when the user clears the component (e.g. image or audio) using the X button for the component. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

upload

gradio.File.upload(fn, ···)

This event is triggered when the user uploads a file into the component (e.g. when the user uploads a video into a video component). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

select

gradio.File.select(fn, ···)

Event listener for when the user selects file from list. Uses event data gradio.SelectData to carry `value` referring to name of selected file, and `index` to refer to index. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about File

# This demo needs to be run from the repo folder.
# python demo/fake_gan/run.py
import random

import gradio as gr


def fake_gan():
    images = [
        (random.choice(
            [
                "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
                "https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80",
                "https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80",
                "https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
                "https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80",
            ]
        ), f"label {i}" if i != 0 else "label" * 50)
        for i in range(3)
    ]
    return images


with gr.Blocks() as demo:
    with gr.Column(variant="panel"):
        with gr.Row(variant="compact"):
            text = gr.Textbox(
                label="Enter your prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
            ).style(
                container=False,
            )
            btn = gr.Button("Generate image").style(full_width=False)

        gallery = gr.Gallery(
            label="Generated images", show_label=False, elem_id="gallery"
        ).style(columns=[2], rows=[2], object_fit="contain", height="auto")

    btn.click(fake_gan, None, gallery)

if __name__ == "__main__":
    demo.launch()

Gallery

gradio.Gallery(···)

Used to display a list of images as a gallery that can be scrolled through.

As input: this component does *not* accept input.

As output: expects a list of images in any format, List[numpy.array | PIL.Image | str], or a List of (image, str caption) tuples and displays them.

# This demo needs to be run from the repo folder.
# python demo/fake_gan/run.py
import random

import gradio as gr


def fake_gan():
    images = [
        (random.choice(
            [
                "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
                "https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80",
                "https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80",
                "https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
                "https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80",
            ]
        ), f"label {i}" if i != 0 else "label" * 50)
        for i in range(3)
    ]
    return images


with gr.Blocks() as demo:
    with gr.Column(variant="panel"):
        with gr.Row(variant="compact"):
            text = gr.Textbox(
                label="Enter your prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
            ).style(
                container=False,
            )
            btn = gr.Button("Generate image").style(full_width=False)

        gallery = gr.Gallery(
            label="Generated images", show_label=False, elem_id="gallery"
        ).style(columns=[2], rows=[2], object_fit="contain", height="auto")

    btn.click(fake_gan, None, gallery)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` list[np.ndarray \| _Image.Image \| str \| tuple] \| Callable \| None default: None	List of images to display in the gallery by default. If callable, the function will be called whenever the app loads to set the initial value of the component.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Gallery`	"gallery"	Uses default values

Methods

style

gradio.Gallery.style(···)

This method can be used to change the appearance of the gallery component.

Parameter	Description
`grid` int \| tuple \| None default: None	('grid' has been renamed to 'columns') Represents the number of images that should be shown in one row, for each of the six standard screen sizes (<576px, <768px, <992px, <1200px, <1400px, >1400px). if fewer that 6 are given then the last will be used for all subsequent breakpoints columns: Represents the number of columns in the image grid, for each of the six standard screen sizes (<576px, <768px, <992px, <1200px, <1400px, >1400px). if fewer that 6 are given then the last will be used for all subsequent breakpoints
`columns` int \| tuple \| None default: None
`rows` int \| tuple \| None default: None	Represents the number of rows in the image grid, for each of the six standard screen sizes (<576px, <768px, <992px, <1200px, <1400px, >1400px). if fewer that 6 are given then the last will be used for all subsequent breakpoints
`height` str \| None default: None	Height of the gallery.
`container` bool \| None default: None	If True, will place gallery in a container - providing some extra padding around the border.
`preview` bool \| None default: None	If True, will display the Gallery in preview mode, which shows all of the images as thumbnails and allows the user to click on them to view them in full size.
`object_fit` str \| None default: None	CSS object-fit property for the thumbnail images in the gallery. Can be "contain", "cover", "fill", "none", or "scale-down".

select

gradio.Gallery.select(fn, ···)

Event listener for when the user selects image within Gallery. Uses event data gradio.SelectData to carry `value` referring to caption of selected image, and `index` to refer to index. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about Gallery

import gradio as gr
import os
os.system('python -m spacy download en_core_web_sm')
import spacy
from spacy import displacy

nlp = spacy.load("en_core_web_sm")

def text_analysis(text):
    doc = nlp(text)
    html = displacy.render(doc, style="dep", page=True)
    html = (
        ""
        + html
        + ""
    )
    pos_count = {
        "char_count": len(text),
        "token_count": 0,
    }
    pos_tokens = []

    for token in doc:
        pos_tokens.extend([(token.text, token.pos_), (" ", None)])

    return pos_tokens, pos_count, html

demo = gr.Interface(
    text_analysis,
    gr.Textbox(placeholder="Enter sentence here..."),
    ["highlight", "json", "html"],
    examples=[
        ["What a beautiful morning for a walk!"],
        ["It was the best of times, it was the worst of times."],
    ],
)

demo.launch()

HTML

gradio.HTML(···)

Used to display arbitrary HTML output.

As input: this component does *not* accept input.

As output: expects a valid HTML str.

import gradio as gr
import os
os.system('python -m spacy download en_core_web_sm')
import spacy
from spacy import displacy

nlp = spacy.load("en_core_web_sm")

def text_analysis(text):
    doc = nlp(text)
    html = displacy.render(doc, style="dep", page=True)
    html = (
        ""
        + html
        + ""
    )
    pos_count = {
        "char_count": len(text),
        "token_count": 0,
    }
    pos_tokens = []

    for token in doc:
        pos_tokens.extend([(token.text, token.pos_), (" ", None)])

    return pos_tokens, pos_count, html

demo = gr.Interface(
    text_analysis,
    gr.Textbox(placeholder="Enter sentence here..."),
    ["highlight", "json", "html"],
    examples=[
        ["What a beautiful morning for a walk!"],
        ["It was the best of times, it was the worst of times."],
    ],
)

demo.launch()

Parameter	Description
`value` str \| Callable default: ""	Default value. If callable, the function will be called whenever the app loads to set the initial value of the component.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.HTML`	"html"	Uses default values

Methods

change

gradio.HTML.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

Key Features

from difflib import Differ

import gradio as gr


def diff_texts(text1, text2):
    d = Differ()
    return [
        (token[2:], token[0] if token[0] != " " else None)
        for token in d.compare(text1, text2)
    ]


demo = gr.Interface(
    diff_texts,
    [
        gr.Textbox(
            label="Text 1",
            info="Initial text",
            lines=3,
            value="The quick brown fox jumped over the lazy dogs.",
        ),
        gr.Textbox(
            label="Text 2",
            info="Text to compare",
            lines=3,
            value="The fast brown fox jumps over lazy dogs.",
        ),
    ],
    gr.HighlightedText(
        label="Diff",
        combine_adjacent=True,
        show_legend=True,
    ).style(color_map={"+": "red", "-": "green"}),
    theme=gr.themes.Base()
)
if __name__ == "__main__":
    demo.launch()

import gradio as gr
import os
os.system('python -m spacy download en_core_web_sm')
import spacy
from spacy import displacy

nlp = spacy.load("en_core_web_sm")

def text_analysis(text):
    doc = nlp(text)
    html = displacy.render(doc, style="dep", page=True)
    html = (
        ""
        + html
        + ""
    )
    pos_count = {
        "char_count": len(text),
        "token_count": 0,
    }
    pos_tokens = []

    for token in doc:
        pos_tokens.extend([(token.text, token.pos_), (" ", None)])

    return pos_tokens, pos_count, html

demo = gr.Interface(
    text_analysis,
    gr.Textbox(placeholder="Enter sentence here..."),
    ["highlight", "json", "html"],
    examples=[
        ["What a beautiful morning for a walk!"],
        ["It was the best of times, it was the worst of times."],
    ],
)

demo.launch()

HighlightedText

gradio.HighlightedText(···)

Displays text that contains spans that are highlighted by category or numerical value.

As input: this component does *not* accept input.

As output: expects a List[Tuple[str, float | str]]] consisting of spans of text and their associated labels, or a Dict with two keys: (1) "text" whose value is the complete text, and "entities", which is a list of dictionaries, each of which have the keys: "entity" (consisting of the entity label), "start" (the character index where the label starts), and "end" (the character index where the label ends). Entities should not overlap.

from difflib import Differ

import gradio as gr


def diff_texts(text1, text2):
    d = Differ()
    return [
        (token[2:], token[0] if token[0] != " " else None)
        for token in d.compare(text1, text2)
    ]


demo = gr.Interface(
    diff_texts,
    [
        gr.Textbox(
            label="Text 1",
            info="Initial text",
            lines=3,
            value="The quick brown fox jumped over the lazy dogs.",
        ),
        gr.Textbox(
            label="Text 2",
            info="Text to compare",
            lines=3,
            value="The fast brown fox jumps over lazy dogs.",
        ),
    ],
    gr.HighlightedText(
        label="Diff",
        combine_adjacent=True,
        show_legend=True,
    ).style(color_map={"+": "red", "-": "green"}),
    theme=gr.themes.Base()
)
if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` list[tuple[str, str \| float \| None]] \| dict \| Callable \| None default: None	Default value to show. If callable, the function will be called whenever the app loads to set the initial value of the component.
`color_map` dict[str, str] \| None default: None
`show_legend` bool default: False	whether to show span categories in a separate legend or inline.
`combine_adjacent` bool default: False	If True, will merge the labels of adjacent tokens belonging to the same category.
`adjacent_separator` str default: ""	Specifies the separator to be used between tokens if combine_adjacent is True.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.HighlightedText`	"highlightedtext"	Uses default values

Methods

style

gradio.HighlightedText.style(···)

This method can be used to change the appearance of the HighlightedText component.

Parameter Description


                color_map

dict[str, str] | None

default: None

Map between category and respective colors.


                container

bool | None

default: None

If True, will place the component in a container - providing some extra padding around the border.

change

gradio.HighlightedText.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

select

gradio.HighlightedText.select(fn, ···)

Event listener for when the user selects Highlighted text span. Uses event data gradio.SelectData to carry `value` referring to selected [text, label] tuple, and `index` to refer to span index. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

Named Entity Recognition

import gradio as gr
import os


def image_mod(image):
    return image.rotate(45)


demo = gr.Interface(
    image_mod,
    gr.Image(type="pil"),
    "image",
    flagging_options=["blurry", "incorrect", "other"],
    examples=[
        os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"),
        os.path.join(os.path.dirname(__file__), "images/lion.jpg"),
        os.path.join(os.path.dirname(__file__), "images/logo.png"),
        os.path.join(os.path.dirname(__file__), "images/tower.jpg"),
    ],
)

if __name__ == "__main__":
    demo.launch()

import gradio as gr
import os


def image_mod(image):
    return image.rotate(45)


cheetah = os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg")

demo = gr.Interface(image_mod, gr.Image(type="pil", value=cheetah), "image",
    flagging_options=["blurry", "incorrect", "other"], examples=[
        os.path.join(os.path.dirname(__file__), "images/lion.jpg"),
        os.path.join(os.path.dirname(__file__), "images/logo.png")
        ])

if __name__ == "__main__":
    demo.launch()

Image

gradio.Image(···)

Creates an image component that can be used to upload/draw images (as an input) or display images (as an output).

As input: passes the uploaded image as a numpy.array, PIL.Image or str filepath depending on `type` -- unless `tool` is `sketch` AND source is one of `upload` or `webcam`. In these cases, a dict with keys `image` and `mask` is passed, and the format of the corresponding values depends on `type`.

As output: expects a numpy.array, PIL.Image or str or pathlib.Path filepath to an image and displays the image.

Format expected for examples: a str filepath to a local file that contains the image.

Supported events: check_streamable()

import gradio as gr
import os


def image_mod(image):
    return image.rotate(45)


demo = gr.Interface(
    image_mod,
    gr.Image(type="pil"),
    "image",
    flagging_options=["blurry", "incorrect", "other"],
    examples=[
        os.path.join(os.path.dirname(__file__), "images/cheetah1.jpg"),
        os.path.join(os.path.dirname(__file__), "images/lion.jpg"),
        os.path.join(os.path.dirname(__file__), "images/logo.png"),
        os.path.join(os.path.dirname(__file__), "images/tower.jpg"),
    ],
)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` str \| _Image.Image \| np.ndarray \| None default: None	A PIL Image, numpy array, path or URL for the default value that Image component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component.
`shape` tuple[int, int] \| None default: None	(width, height) shape to crop and resize image to; if None, matches input image size. Pass None for either width or height to only crop and resize the other.
`image_mode` str default: "RGB"	"RGB" if color, or "L" if black and white.
`invert_colors` bool default: False	whether to invert the image as a preprocessing step.
`source` str default: "upload"	Source of image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "canvas" defaults to a white image that can be edited and drawn upon with tools.
`tool` str \| None default: None	Tools used for editing. "editor" allows a full screen editor (and is the default if source is "upload" or "webcam"), "select" provides a cropping and zoom tool, "sketch" allows you to create a binary sketch (and is the default if source="canvas"), and "color-sketch" allows you to created a sketch in different colors. "color-sketch" can be used with source="upload" or "webcam" to allow sketching on an image. "sketch" can also be used with "upload" or "webcam" to create a mask over an image and in that case both the image and mask are passed into the function as a dictionary with keys "image" and "mask" respectively.
`type` str default: "numpy"	The format the image is converted to before being passed into the prediction function. "numpy" converts the image to a numpy array with shape (width, height, 3) and values from 0 to 255, "pil" converts the image to a PIL image object, "filepath" passes a str path to a temporary file containing the image.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`streaming` bool default: False	If True when used in a `live` interface, will automatically stream webcam feed. Only valid is source is 'webcam'.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
`mirror_webcam` bool default: True	If True webcam will be mirrored. Default is True.
`brush_radius` float \| None default: None	Size of the brush for Sketch. Default is None which chooses a sensible default

Class	Interface String Shortcut	Initialization
`gradio.Image`	"image"	Uses default values
`gradio.Webcam`	"webcam"	Uses source="webcam", interactive=True
`gradio.Sketchpad`	"sketchpad"	Uses image_mode="L", source="canvas", shape=(28, 28), invert_colors=True, interactive=True
`gradio.Paint`	"paint"	Uses source="canvas", tool="color-sketch", interactive=True
`gradio.ImageMask`	"imagemask"	Uses source="upload", tool="sketch", interactive=True
`gradio.ImagePaint`	"imagepaint"	Uses source="upload", tool="color-sketch", interactive=True
`gradio.Pil`	"pil"	Uses type="pil"

Methods

style

gradio.Image.style(···)

This method can be used to change the appearance of the Image component.

Parameter Description


                height

int | None

default: None

Height of the image.


                width

int | None

default: None

Width of the image.

change

gradio.Image.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

edit

gradio.Image.edit(fn, ···)

This event is triggered when the user edits the component (e.g. image) using the built-in editor. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

clear

gradio.Image.clear(fn, ···)

This event is triggered when the user clears the component (e.g. image or audio) using the X button for the component. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

stream

gradio.Image.stream(fn, ···)

This event is triggered when the user streams the component (e.g. a live webcam component). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

upload

gradio.Image.upload(fn, ···)

This event is triggered when the user uploads a file into the component (e.g. when the user uploads a video into a video component). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

select

gradio.Image.select(fn, ···)

Event listener for when the user clicks on a pixel within the image. Uses event data gradio.SelectData to carry `index` to refer to the [x, y] coordinates of the clicked pixel. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

Image Classification In Pytorch

Image Classification In Tensorflow

Image Classification With Vision Transformers

Create Your Own Friends With A Gan

Interpretation

gradio.Interpretation(component, ···)

Used to create an interpretation widget for a component.

As input: this component does *not* accept input.

As output: expects a dict with keys "original" and "interpretation".

Parameter	Description
`component` Component required	Which component to show in the interpretation widget.
`visible` bool default: True	Whether or not the interpretation is visible.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Interpretation`	"interpretation"	Uses default values

Step-by-step Guides

Custom Interpretations With Blocks

from zipfile import ZipFile

import gradio as gr


def zip_to_json(file_obj):
    files = []
    with ZipFile(file_obj.name) as zfile:
        for zinfo in zfile.infolist():
            files.append(
                {
                    "name": zinfo.filename,
                    "file_size": zinfo.file_size,
                    "compressed_size": zinfo.compress_size,
                }
            )
    return files


demo = gr.Interface(zip_to_json, "file", "json")

if __name__ == "__main__":
    demo.launch()

import gradio as gr
import random
import time

def xray_model(diseases, img):
    time.sleep(4)
    return [{disease: random.random() for disease in diseases}]


def ct_model(diseases, img):
    time.sleep(3)
    return [{disease: 0.1 for disease in diseases}]

with gr.Blocks() as demo:
    gr.Markdown(
        """
# Detect Disease From Scan
With this model you can lorem ipsum
- ipsum 1
- ipsum 2
"""
    )
    disease = gr.CheckboxGroup(
        info="Select the diseases you want to scan for.",
        choices=["Covid", "Malaria", "Lung Cancer"], label="Disease to Scan For"
    )
    slider = gr.Slider(0, 100)

    with gr.Tab("X-ray") as x_tab:
        with gr.Row():
            xray_scan = gr.Image()
            xray_results = gr.JSON()
        xray_run = gr.Button("Run")
        xray_run.click(
            xray_model,
            inputs=[disease, xray_scan],
            outputs=xray_results,
            api_name="xray_model"
        )

    with gr.Tab("CT Scan"):
        with gr.Row():
            ct_scan = gr.Image()
            ct_results = gr.JSON()
        ct_run = gr.Button("Run")
        ct_run.click(
            ct_model,
            inputs=[disease, ct_scan],
            outputs=ct_results,
            api_name="ct_model"
        )

    upload_btn = gr.Button("Upload Results", variant="primary")
    upload_btn.click(
        lambda ct, xr: time.sleep(5),
        inputs=[ct_results, xray_results],
        outputs=[],
    )

if __name__ == "__main__":
    demo.launch()

JSON

gradio.JSON(···)

Used to display arbitrary JSON output prettily.

As input: this component does *not* accept input.

As output: expects a str filepath to a file containing valid JSON -- or a list or dict that is valid JSON

from zipfile import ZipFile

import gradio as gr


def zip_to_json(file_obj):
    files = []
    with ZipFile(file_obj.name) as zfile:
        for zinfo in zfile.infolist():
            files.append(
                {
                    "name": zinfo.filename,
                    "file_size": zinfo.file_size,
                    "compressed_size": zinfo.compress_size,
                }
            )
    return files


demo = gr.Interface(zip_to_json, "file", "json")

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` str \| dict \| list \| Callable \| None default: None	Default value. If callable, the function will be called whenever the app loads to set the initial value of the component.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.JSON`	"json"	Uses default values

Methods

style

gradio.JSON.style(···)

This method can be used to change the appearance of the JSON component.

Parameter Description


                container

bool | None

default: None

If True, will place the JSON in a container - providing some extra padding around the border.

change

gradio.JSON.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about JSON

from math import log2, pow
import os

import numpy as np
from scipy.fftpack import fft

import gradio as gr

A4 = 440
C0 = A4 * pow(2, -4.75)
name = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]


def get_pitch(freq):
    h = round(12 * log2(freq / C0))
    n = h % 12
    return name[n]


def main_note(audio):
    rate, y = audio
    if len(y.shape) == 2:
        y = y.T[0]
    N = len(y)
    T = 1.0 / rate
    yf = fft(y)
    yf2 = 2.0 / N * np.abs(yf[0 : N // 2])
    xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2)

    volume_per_pitch = {}
    total_volume = np.sum(yf2)
    for freq, volume in zip(xf, yf2):
        if freq == 0:
            continue
        pitch = get_pitch(freq)
        if pitch not in volume_per_pitch:
            volume_per_pitch[pitch] = 0
        volume_per_pitch[pitch] += 1.0 * volume / total_volume
    volume_per_pitch = {k: float(v) for k, v in volume_per_pitch.items()}
    return volume_per_pitch


demo = gr.Interface(
    main_note,
    gr.Audio(source="microphone"),
    gr.Label(num_top_classes=4),
    examples=[
        [os.path.join(os.path.dirname(__file__),"audio/recording1.wav")],
        [os.path.join(os.path.dirname(__file__),"audio/cantina.wav")],
    ],
    interpretation="default",
)

if __name__ == "__main__":
    demo.launch()

import os

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

import gradio as gr

current_dir = os.path.dirname(os.path.realpath(__file__))
data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv"))


def encode_age(df):
    df.Age = df.Age.fillna(-0.5)
    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
    categories = pd.cut(df.Age, bins, labels=False)
    df.Age = categories
    return df


def encode_fare(df):
    df.Fare = df.Fare.fillna(-0.5)
    bins = (-1, 0, 8, 15, 31, 1000)
    categories = pd.cut(df.Fare, bins, labels=False)
    df.Fare = categories
    return df


def encode_df(df):
    df = encode_age(df)
    df = encode_fare(df)
    sex_mapping = {"male": 0, "female": 1}
    df = df.replace({"Sex": sex_mapping})
    embark_mapping = {"S": 1, "C": 2, "Q": 3}
    df = df.replace({"Embarked": embark_mapping})
    df.Embarked = df.Embarked.fillna(0)
    df["Company"] = 0
    df.loc[(df["SibSp"] > 0), "Company"] = 1
    df.loc[(df["Parch"] > 0), "Company"] = 2
    df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
    df = df[
        [
            "PassengerId",
            "Pclass",
            "Sex",
            "Age",
            "Fare",
            "Embarked",
            "Company",
            "Survived",
        ]
    ]
    return df


train = encode_df(data)

X_all = train.drop(["Survived", "PassengerId"], axis=1)
y_all = train["Survived"]

num_test = 0.20
X_train, X_test, y_train, y_test = train_test_split(
    X_all, y_all, test_size=num_test, random_state=23
)

clf = RandomForestClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)


def predict_survival(passenger_class, is_male, age, company, fare, embark_point):
    if passenger_class is None or embark_point is None:
        return None
    df = pd.DataFrame.from_dict(
        {
            "Pclass": [passenger_class + 1],
            "Sex": [0 if is_male else 1],
            "Age": [age],
            "Fare": [fare],
            "Embarked": [embark_point + 1],
            "Company": [
                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
            ]
        }
    )
    df = encode_age(df)
    df = encode_fare(df)
    pred = clf.predict_proba(df)[0]
    return {"Perishes": float(pred[0]), "Survives": float(pred[1])}


demo = gr.Interface(
    predict_survival,
    [
        gr.Dropdown(["first", "second", "third"], type="index"),
        "checkbox",
        gr.Slider(0, 80, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        gr.Radio(["S", "C", "Q"], type="index"),
    ],
    "label",
    examples=[
        ["first", True, 30, [], 50, "S"],
        ["second", False, 40, ["Sibling", "Child"], 10, "Q"],
        ["third", True, 30, ["Child"], 20, "S"],
    ],
    interpretation="default",
    live=True,
)

if __name__ == "__main__":
    demo.launch()

Label

gradio.Label(···)

Displays a classification label, along with confidence scores of top categories, if provided.

As input: this component does *not* accept input.

As output: expects a Dict[str, float] of classes and confidences, or str with just the class or an int/float for regression outputs, or a str path to a .json file containing a json dictionary in the structure produced by Label.postprocess().

from math import log2, pow
import os

import numpy as np
from scipy.fftpack import fft

import gradio as gr

A4 = 440
C0 = A4 * pow(2, -4.75)
name = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]


def get_pitch(freq):
    h = round(12 * log2(freq / C0))
    n = h % 12
    return name[n]


def main_note(audio):
    rate, y = audio
    if len(y.shape) == 2:
        y = y.T[0]
    N = len(y)
    T = 1.0 / rate
    yf = fft(y)
    yf2 = 2.0 / N * np.abs(yf[0 : N // 2])
    xf = np.linspace(0.0, 1.0 / (2.0 * T), N // 2)

    volume_per_pitch = {}
    total_volume = np.sum(yf2)
    for freq, volume in zip(xf, yf2):
        if freq == 0:
            continue
        pitch = get_pitch(freq)
        if pitch not in volume_per_pitch:
            volume_per_pitch[pitch] = 0
        volume_per_pitch[pitch] += 1.0 * volume / total_volume
    volume_per_pitch = {k: float(v) for k, v in volume_per_pitch.items()}
    return volume_per_pitch


demo = gr.Interface(
    main_note,
    gr.Audio(source="microphone"),
    gr.Label(num_top_classes=4),
    examples=[
        [os.path.join(os.path.dirname(__file__),"audio/recording1.wav")],
        [os.path.join(os.path.dirname(__file__),"audio/cantina.wav")],
    ],
    interpretation="default",
)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` dict[str, float] \| str \| float \| Callable \| None default: None	Default value to show in the component. If a str or number is provided, simply displays the string or number. If a {Dict[str, float]} of classes and confidences is provided, displays the top class on top and the `num_top_classes` below, along with their confidence bars. If callable, the function will be called whenever the app loads to set the initial value of the component.
`num_top_classes` int \| None default: None	number of most confident classes to show.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
`color` str \| None default: None	The background color of the label (either a valid css color name or hexadecimal string).

Class	Interface String Shortcut	Initialization
`gradio.Label`	"label"	Uses default values

Methods

style

gradio.Label.style(···)

This method can be used to change the appearance of the label component.

Parameter Description


                container

bool | None

default: None

If True, will add a container to the label - providing some extra padding around the border.

change

gradio.Label.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

select

gradio.Label.select(fn, ···)

Event listener for when the user selects a category from Label. Uses event data gradio.SelectData to carry `value` referring to name of selected category, and `index` to refer to index. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

Image Classification In Pytorch

Image Classification In Tensorflow

Image Classification With Vision Transformers

Building A Pictionary App

import gradio as gr

from scatter_plot_demo import scatter_plot
from line_plot_demo import line_plot
from bar_plot_demo import bar_plot


with gr.Blocks() as demo:
    with gr.Tabs():
        with gr.TabItem("Scatter Plot"):
            scatter_plot.render()
        with gr.TabItem("Line Plot"):
            line_plot.render()
        with gr.TabItem("Bar Plot"):
            bar_plot.render()

if __name__ == "__main__":
    demo.launch()

import math

import pandas as pd

import gradio as gr
import datetime
import numpy as np


def get_time():
    return datetime.datetime.now()


plot_end = 2 * math.pi


def get_plot(period=1):
    global plot_end
    x = np.arange(plot_end - 2 * math.pi, plot_end, 0.02)
    y = np.sin(2 * math.pi * period * x)
    update = gr.LinePlot.update(
        value=pd.DataFrame({"x": x, "y": y}),
        x="x",
        y="y",
        title="Plot (updates every second)",
        width=600,
        height=350,
    )
    plot_end += 2 * math.pi
    if plot_end > 1000:
        plot_end = 2 * math.pi
    return update


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            c_time2 = gr.Textbox(label="Current Time refreshed every second")
            gr.Textbox(
                "Change the value of the slider to automatically update the plot",
                label="",
            )
            period = gr.Slider(
                label="Period of plot", value=1, minimum=0, maximum=10, step=1
            )
            plot = gr.LinePlot(show_label=False)
        with gr.Column():
            name = gr.Textbox(label="Enter your name")
            greeting = gr.Textbox(label="Greeting")
            button = gr.Button(value="Greet")
            button.click(lambda s: f"Hello {s}", name, greeting)

    demo.load(lambda: datetime.datetime.now(), None, c_time2, every=1)
    dep = demo.load(get_plot, None, plot, every=1)
    period.change(get_plot, period, plot, every=1, cancels=[dep])

if __name__ == "__main__":
    demo.queue().launch()

LinePlot

gradio.LinePlot(···)

Create a line plot.

As input: this component does *not* accept input.

As output: expects a pandas dataframe with the data to plot.

import gradio as gr

from scatter_plot_demo import scatter_plot
from line_plot_demo import line_plot
from bar_plot_demo import bar_plot


with gr.Blocks() as demo:
    with gr.Tabs():
        with gr.TabItem("Scatter Plot"):
            scatter_plot.render()
        with gr.TabItem("Line Plot"):
            line_plot.render()
        with gr.TabItem("Bar Plot"):
            bar_plot.render()

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` pd.DataFrame \| Callable \| None default: None	The pandas dataframe containing the data to display in a scatter plot.
`x` str \| None default: None	Column corresponding to the x axis.
`y` str \| None default: None	Column corresponding to the y axis.
`color` str \| None default: None	The column to determine the point color. If the column contains numeric data, gradio will interpolate the column data so that small values correspond to light colors and large values correspond to dark values.
`stroke_dash` str \| None default: None	The column to determine the symbol used to draw the line, e.g. dashed lines, dashed lines with points.
`overlay_point` bool \| None default: None	Whether to draw a point on the line for each (x, y) coordinate pair.
`title` str \| None default: None	The title to display on top of the chart.
`tooltip` list[str] \| str \| None default: None	The column (or list of columns) to display on the tooltip when a user hovers a point on the plot.
`x_title` str \| None default: None	The title given to the x axis. By default, uses the value of the x parameter.
`y_title` str \| None default: None	The title given to the y axis. By default, uses the value of the y parameter.
`color_legend_title` str \| None default: None	The title given to the color legend. By default, uses the value of color parameter.
`stroke_dash_legend_title` str \| None default: None	The title given to the stroke_dash legend. By default, uses the value of the stroke_dash parameter.
`color_legend_position` str \| None default: None	The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
`stroke_dash_legend_position` str \| None default: None	The position of the stoke_dash legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
`height` int \| None default: None	The height of the plot in pixels.
`width` int \| None default: None	The width of the plot in pixels.
`x_lim` list[int] \| None default: None	A tuple or list containing the limits for the x-axis, specified as [x_min, x_max].
`y_lim` list[int] \| None default: None	A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
`caption` str \| None default: None	The (optional) caption to display below the plot.
`interactive` bool \| None default: True	Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
`label` str \| None default: None	The (optional) label to display on the top left corner of the plot.
`show_label` bool default: True	Whether the label should be displayed.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`visible` bool default: True	Whether the plot should be visible.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.LinePlot`	"lineplot"	Uses default values

Methods

change

gradio.LinePlot.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

clear

gradio.LinePlot.clear(fn, ···)

This event is triggered when the user clears the component (e.g. image or audio) using the X button for the component. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about LinePlot

import gradio as gr

def welcome(name):
    return f"Welcome to Gradio, {name}!"

with gr.Blocks() as demo:
    gr.Markdown(
    """
    # Hello World!
    Start typing below to see the output.
    """)
    inp = gr.Textbox(placeholder="What is your name?")
    out = gr.Textbox()
    inp.change(welcome, inp, out)

if __name__ == "__main__":
    demo.launch()

import pandas as pd
import numpy as np

import gradio as gr


def plot(v, a):
    g = 9.81
    theta = a / 180 * 3.14
    tmax = ((2 * v) * np.sin(theta)) / g
    timemat = tmax * np.linspace(0, 1, 40)

    x = (v * timemat) * np.cos(theta)
    y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))
    df = pd.DataFrame({"x": x, "y": y})
    return df


demo = gr.Blocks()

with demo:
    gr.Markdown(
        r"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \cdot \frac{\sin(2\theta)}{g}$"
    )

    with gr.Row():
        speed = gr.Slider(1, 30, 25, label="Speed")
        angle = gr.Slider(0, 90, 45, label="Angle")
    output = gr.LinePlot(
        x="x",
        y="y",
        overlay_point=True,
        tooltip=["x", "y"],
        x_lim=[0, 100],
        y_lim=[0, 60],
        width=350,
        height=300,
    )
    btn = gr.Button(value="Run")
    btn.click(plot, [speed, angle], output)

if __name__ == "__main__":
    demo.launch()

Markdown

gradio.Markdown(···)

Used to render arbitrary Markdown output. Can also render latex enclosed by dollar signs.

As input: this component does *not* accept input.

As output: expects a valid str that can be rendered as Markdown.

import gradio as gr

def welcome(name):
    return f"Welcome to Gradio, {name}!"

with gr.Blocks() as demo:
    gr.Markdown(
    """
    # Hello World!
    Start typing below to see the output.
    """)
    inp = gr.Textbox(placeholder="What is your name?")
    out = gr.Textbox()
    inp.change(welcome, inp, out)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` str \| Callable default: ""	Value to show in Markdown component. If callable, the function will be called whenever the app loads to set the initial value of the component.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Markdown`	"markdown"	Uses default values

Methods

change

gradio.Markdown.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

Key Features

import gradio as gr
import os


def load_mesh(mesh_file_name):
    return mesh_file_name


demo = gr.Interface(
    fn=load_mesh,
    inputs=gr.Model3D(),
    outputs=gr.Model3D(
            clear_color=[0.0, 0.0, 0.0, 0.0],  label="3D Model"),
    examples=[
        [os.path.join(os.path.dirname(__file__), "files/Bunny.obj")],
        [os.path.join(os.path.dirname(__file__), "files/Duck.glb")],
        [os.path.join(os.path.dirname(__file__), "files/Fox.gltf")],
        [os.path.join(os.path.dirname(__file__), "files/face.obj")],
    ],
)

if __name__ == "__main__":
    demo.launch()

Model3D

gradio.Model3D(···)

Component allows users to upload or view 3D Model files (.obj, .glb, or .gltf).

As input: This component passes the uploaded file as a str filepath.

As output: expects function to return a str path to a file of type (.obj, glb, or .gltf)

import gradio as gr
import os


def load_mesh(mesh_file_name):
    return mesh_file_name


demo = gr.Interface(
    fn=load_mesh,
    inputs=gr.Model3D(),
    outputs=gr.Model3D(
            clear_color=[0.0, 0.0, 0.0, 0.0],  label="3D Model"),
    examples=[
        [os.path.join(os.path.dirname(__file__), "files/Bunny.obj")],
        [os.path.join(os.path.dirname(__file__), "files/Duck.glb")],
        [os.path.join(os.path.dirname(__file__), "files/Fox.gltf")],
        [os.path.join(os.path.dirname(__file__), "files/face.obj")],
    ],
)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` str \| Callable \| None default: None	path to (.obj, glb, or .gltf) file to show in model3D viewer. If callable, the function will be called whenever the app loads to set the initial value of the component.
`clear_color` list[float] \| None default: None	background color of scene
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Model3D`	"model3d"	Uses default values

Methods

style

gradio.Model3D.style(···)

This method can be used to change the appearance of the Model3D component.

change

gradio.Model3D.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

edit

gradio.Model3D.edit(fn, ···)

This event is triggered when the user edits the component (e.g. image) using the built-in editor. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

clear

gradio.Model3D.clear(fn, ···)

This event is triggered when the user clears the component (e.g. image or audio) using the X button for the component. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

How To Use 3D Model Component

import gradio as gr

def tax_calculator(income, marital_status, assets):
    tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]
    total_deductible = sum(assets["Cost"])
    taxable_income = income - total_deductible

    total_tax = 0
    for bracket, rate in tax_brackets:
        if taxable_income > bracket:
            total_tax += (taxable_income - bracket) * rate / 100

    if marital_status == "Married":
        total_tax *= 0.75
    elif marital_status == "Divorced":
        total_tax *= 0.8

    return round(total_tax)

demo = gr.Interface(
    tax_calculator,
    [
        "number",
        gr.Radio(["Single", "Married", "Divorced"]),
        gr.Dataframe(
            headers=["Item", "Cost"],
            datatype=["str", "number"],
            label="Assets Purchased this Year",
        ),
    ],
    "number",
    examples=[
        [10000, "Married", [["Suit", 5000], ["Laptop", 800], ["Car", 1800]]],
        [80000, "Single", [["Suit", 800], ["Watch", 1800], ["Car", 800]]],
    ],
)

demo.launch()

import os

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

import gradio as gr

current_dir = os.path.dirname(os.path.realpath(__file__))
data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv"))


def encode_age(df):
    df.Age = df.Age.fillna(-0.5)
    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
    categories = pd.cut(df.Age, bins, labels=False)
    df.Age = categories
    return df


def encode_fare(df):
    df.Fare = df.Fare.fillna(-0.5)
    bins = (-1, 0, 8, 15, 31, 1000)
    categories = pd.cut(df.Fare, bins, labels=False)
    df.Fare = categories
    return df


def encode_df(df):
    df = encode_age(df)
    df = encode_fare(df)
    sex_mapping = {"male": 0, "female": 1}
    df = df.replace({"Sex": sex_mapping})
    embark_mapping = {"S": 1, "C": 2, "Q": 3}
    df = df.replace({"Embarked": embark_mapping})
    df.Embarked = df.Embarked.fillna(0)
    df["Company"] = 0
    df.loc[(df["SibSp"] > 0), "Company"] = 1
    df.loc[(df["Parch"] > 0), "Company"] = 2
    df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
    df = df[
        [
            "PassengerId",
            "Pclass",
            "Sex",
            "Age",
            "Fare",
            "Embarked",
            "Company",
            "Survived",
        ]
    ]
    return df


train = encode_df(data)

X_all = train.drop(["Survived", "PassengerId"], axis=1)
y_all = train["Survived"]

num_test = 0.20
X_train, X_test, y_train, y_test = train_test_split(
    X_all, y_all, test_size=num_test, random_state=23
)

clf = RandomForestClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)


def predict_survival(passenger_class, is_male, age, company, fare, embark_point):
    if passenger_class is None or embark_point is None:
        return None
    df = pd.DataFrame.from_dict(
        {
            "Pclass": [passenger_class + 1],
            "Sex": [0 if is_male else 1],
            "Age": [age],
            "Fare": [fare],
            "Embarked": [embark_point + 1],
            "Company": [
                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
            ]
        }
    )
    df = encode_age(df)
    df = encode_fare(df)
    pred = clf.predict_proba(df)[0]
    return {"Perishes": float(pred[0]), "Survives": float(pred[1])}


demo = gr.Interface(
    predict_survival,
    [
        gr.Dropdown(["first", "second", "third"], type="index"),
        "checkbox",
        gr.Slider(0, 80, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        gr.Radio(["S", "C", "Q"], type="index"),
    ],
    "label",
    examples=[
        ["first", True, 30, [], 50, "S"],
        ["second", False, 40, ["Sibling", "Child"], 10, "Q"],
        ["third", True, 30, ["Child"], 20, "S"],
    ],
    interpretation="default",
    live=True,
)

if __name__ == "__main__":
    demo.launch()

import gradio as gr

demo = gr.Blocks(css="""#btn {color: red} .abc {font-family: "Comic Sans MS", "Comic Sans", cursive !important}""")

with demo:
    default_json = {"a": "a"}

    num = gr.State(value=0)
    squared = gr.Number(value=0)
    btn = gr.Button("Next Square", elem_id="btn", elem_classes=["abc", "def"])

    stats = gr.State(value=default_json)
    table = gr.JSON()

    def increase(var, stats_history):
        var += 1
        stats_history[str(var)] = var**2
        return var, var**2, stats_history, stats_history

    btn.click(increase, [num, stats], [num, squared, stats, table])

if __name__ == "__main__":
    demo.launch()

Number

gradio.Number(···)

Creates a numeric field for user to enter numbers as input or display numeric output.

As input: passes field value as a float or int into the function, depending on `precision`.

As output: expects an int or float returned from the function and sets field value to it.

Format expected for examples: a float or int representing the number's value.

import gradio as gr

def tax_calculator(income, marital_status, assets):
    tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]
    total_deductible = sum(assets["Cost"])
    taxable_income = income - total_deductible

    total_tax = 0
    for bracket, rate in tax_brackets:
        if taxable_income > bracket:
            total_tax += (taxable_income - bracket) * rate / 100

    if marital_status == "Married":
        total_tax *= 0.75
    elif marital_status == "Divorced":
        total_tax *= 0.8

    return round(total_tax)

demo = gr.Interface(
    tax_calculator,
    [
        "number",
        gr.Radio(["Single", "Married", "Divorced"]),
        gr.Dataframe(
            headers=["Item", "Cost"],
            datatype=["str", "number"],
            label="Assets Purchased this Year",
        ),
    ],
    "number",
    examples=[
        [10000, "Married", [["Suit", 5000], ["Laptop", 800], ["Car", 1800]]],
        [80000, "Single", [["Suit", 800], ["Watch", 1800], ["Car", 800]]],
    ],
)

demo.launch()

Parameter	Description
`value` float \| Callable \| None default: None	default value. If callable, the function will be called whenever the app loads to set the initial value of the component.
`label` str \| None default: None	component name in interface.
`info` str \| None default: None	additional component description.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, will be editable; if False, editing will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
`precision` int \| None default: None	Precision to round input/output to. If set to 0, will round to nearest integer and convert type to int. If None, no rounding happens.

Class	Interface String Shortcut	Initialization
`gradio.Number`	"number"	Uses default values

Methods

style

gradio.Number.style(···)

This method can be used to change the appearance of the component.

Parameter Description


                container

bool | None

default: None

If True, will place the component in a container - providing some extra padding around the border.

change

gradio.Number.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

submit

gradio.Number.submit(fn, ···)

This event is triggered when the user presses the Enter key while the component (e.g. a textbox) is focused. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

blur

gradio.Number.blur(fn, ···)

This event is triggered when the component's is unfocused/blurred (e.g. when the user clicks outside of a textbox). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about Number

import altair as alt
import gradio as gr
import numpy as np
import pandas as pd
from vega_datasets import data


def make_plot(plot_type):
    if plot_type == "scatter_plot":
        cars = data.cars()
        return alt.Chart(cars).mark_point().encode(
            x='Horsepower',
            y='Miles_per_Gallon',
            color='Origin',
        )
    elif plot_type == "heatmap":
        # Compute x^2 + y^2 across a 2D grid
        x, y = np.meshgrid(range(-5, 5), range(-5, 5))
        z = x ** 2 + y ** 2

        # Convert this grid to columnar data expected by Altair
        source = pd.DataFrame({'x': x.ravel(),
                            'y': y.ravel(),
                            'z': z.ravel()})
        return alt.Chart(source).mark_rect().encode(
            x='x:O',
            y='y:O',
            color='z:Q'
        )
    elif plot_type == "us_map":
        states = alt.topo_feature(data.us_10m.url, 'states')
        source = data.income.url

        return alt.Chart(source).mark_geoshape().encode(
            shape='geo:G',
            color='pct:Q',
            tooltip=['name:N', 'pct:Q'],
            facet=alt.Facet('group:N', columns=2),
        ).transform_lookup(
            lookup='id',
            from_=alt.LookupData(data=states, key='id'),
            as_='geo'
        ).properties(
            width=300,
            height=175,
        ).project(
            type='albersUsa'
        )
    elif plot_type == "interactive_barplot":
        source = data.movies.url

        pts = alt.selection(type="single", encodings=['x'])

        rect = alt.Chart(data.movies.url).mark_rect().encode(
            alt.X('IMDB_Rating:Q', bin=True),
            alt.Y('Rotten_Tomatoes_Rating:Q', bin=True),
            alt.Color('count()',
                scale=alt.Scale(scheme='greenblue'),
                legend=alt.Legend(title='Total Records')
            )
        )

        circ = rect.mark_point().encode(
            alt.ColorValue('grey'),
            alt.Size('count()',
                legend=alt.Legend(title='Records in Selection')
            )
        ).transform_filter(
            pts
        )

        bar = alt.Chart(source).mark_bar().encode(
            x='Major_Genre:N',
            y='count()',
            color=alt.condition(pts, alt.ColorValue("steelblue"), alt.ColorValue("grey"))
        ).properties(
            width=550,
            height=200
        ).add_selection(pts)

        plot = alt.vconcat(
            rect + circ,
            bar
        ).resolve_legend(
            color="independent",
            size="independent"
        )
        return plot
    elif plot_type == "radial":
        source = pd.DataFrame({"values": [12, 23, 47, 6, 52, 19]})

        base = alt.Chart(source).encode(
            theta=alt.Theta("values:Q", stack=True),
            radius=alt.Radius("values", scale=alt.Scale(type="sqrt", zero=True, rangeMin=20)),
            color="values:N",
        )

        c1 = base.mark_arc(innerRadius=20, stroke="#fff")

        c2 = base.mark_text(radiusOffset=10).encode(text="values:Q")

        return c1 + c2
    elif plot_type == "multiline":
        source = data.stocks()

        highlight = alt.selection(type='single', on='mouseover',
                                fields=['symbol'], nearest=True)

        base = alt.Chart(source).encode(
            x='date:T',
            y='price:Q',
            color='symbol:N'
        )

        points = base.mark_circle().encode(
            opacity=alt.value(0)
        ).add_selection(
            highlight
        ).properties(
            width=600
        )

        lines = base.mark_line().encode(
            size=alt.condition(~highlight, alt.value(1), alt.value(3))
        )

        return points + lines


with gr.Blocks() as demo:
    button = gr.Radio(label="Plot type",
                      choices=['scatter_plot', 'heatmap', 'us_map',
                               'interactive_barplot', "radial", "multiline"], value='scatter_plot')
    plot = gr.Plot(label="Plot")
    button.change(make_plot, inputs=button, outputs=[plot])
    demo.load(make_plot, inputs=[button], outputs=[plot])


if __name__ == "__main__":
    demo.launch()

import altair

import gradio as gr
from math import sqrt
import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px
import pandas as pd


def outbreak(plot_type, r, month, countries, social_distancing):
    months = ["January", "February", "March", "April", "May"]
    m = months.index(month)
    start_day = 30 * m
    final_day = 30 * (m + 1)
    x = np.arange(start_day, final_day + 1)
    pop_count = {"USA": 350, "Canada": 40, "Mexico": 300, "UK": 120}
    if social_distancing:
        r = sqrt(r)
    df = pd.DataFrame({"day": x})
    for country in countries:
        df[country] = x ** (r) * (pop_count[country] + 1)

    if plot_type == "Matplotlib":
        fig = plt.figure()
        plt.plot(df["day"], df[countries].to_numpy())
        plt.title("Outbreak in " + month)
        plt.ylabel("Cases")
        plt.xlabel("Days since Day 0")
        plt.legend(countries)
        return fig
    elif plot_type == "Plotly":
        fig = px.line(df, x="day", y=countries)
        fig.update_layout(
            title="Outbreak in " + month,
            xaxis_title="Cases",
            yaxis_title="Days Since Day 0",
        )
        return fig
    elif plot_type == "Altair":
        df = df.melt(id_vars="day").rename(columns={"variable": "country"})
        fig = altair.Chart(df).mark_line().encode(x="day", y='value', color='country')
        return fig
    else:
        raise ValueError("A plot type must be selected")


inputs = [
    gr.Dropdown(["Matplotlib", "Plotly", "Altair"], label="Plot Type"),
    gr.Slider(1, 4, 3.2, label="R"),
    gr.Dropdown(["January", "February", "March", "April", "May"], label="Month"),
    gr.CheckboxGroup(
        ["USA", "Canada", "Mexico", "UK"], label="Countries", value=["USA", "Canada"]
    ),
    gr.Checkbox(label="Social Distancing?"),
]
outputs = gr.Plot()

demo = gr.Interface(
    fn=outbreak,
    inputs=inputs,
    outputs=outputs,
    examples=[
        ["Matplotlib", 2, "March", ["Mexico", "UK"], True],
        ["Altair", 2, "March", ["Mexico", "Canada"], True],
        ["Plotly", 3.6, "February", ["Canada", "Mexico", "UK"], False],
    ],
    cache_examples=True,
)

if __name__ == "__main__":
    demo.launch()

import pandas as pd
import numpy as np

import gradio as gr


def plot(v, a):
    g = 9.81
    theta = a / 180 * 3.14
    tmax = ((2 * v) * np.sin(theta)) / g
    timemat = tmax * np.linspace(0, 1, 40)

    x = (v * timemat) * np.cos(theta)
    y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))
    df = pd.DataFrame({"x": x, "y": y})
    return df


demo = gr.Blocks()

with demo:
    gr.Markdown(
        r"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \cdot \frac{\sin(2\theta)}{g}$"
    )

    with gr.Row():
        speed = gr.Slider(1, 30, 25, label="Speed")
        angle = gr.Slider(0, 90, 45, label="Angle")
    output = gr.LinePlot(
        x="x",
        y="y",
        overlay_point=True,
        tooltip=["x", "y"],
        x_lim=[0, 100],
        y_lim=[0, 60],
        width=350,
        height=300,
    )
    btn = gr.Button(value="Run")
    btn.click(plot, [speed, angle], output)

if __name__ == "__main__":
    demo.launch()

import matplotlib.pyplot as plt
import numpy as np

import gradio as gr


def plot_forecast(final_year, companies, noise, show_legend, point_style):
    start_year = 2020
    x = np.arange(start_year, final_year + 1)
    year_count = x.shape[0]
    plt_format = ({"cross": "X", "line": "-", "circle": "o--"})[point_style]
    fig = plt.figure()
    ax = fig.add_subplot(111)
    for i, company in enumerate(companies):
        series = np.arange(0, year_count, dtype=float)
        series = series**2 * (i + 1)
        series += np.random.rand(year_count) * noise
        ax.plot(x, series, plt_format)
    if show_legend:
        plt.legend(companies)
    return fig


demo = gr.Interface(
    plot_forecast,
    [
        gr.Radio([2025, 2030, 2035, 2040], label="Project to:"),
        gr.CheckboxGroup(["Google", "Microsoft", "Gradio"], label="Company Selection"),
        gr.Slider(1, 100, label="Noise Level"),
        gr.Checkbox(label="Show Legend"),
        gr.Dropdown(["cross", "line", "circle"], label="Style"),
    ],
    gr.Plot(label="forecast"),
)

if __name__ == "__main__":
    demo.launch()

import gradio as gr
import plotly.graph_objects as go
from datasets import load_dataset

dataset = load_dataset("gradio/NYC-Airbnb-Open-Data", split="train")
df = dataset.to_pandas()

def filter_map(min_price, max_price, boroughs):

    filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) & 
          (df['price'] > min_price) & (df['price'] < max_price)]
    names = filtered_df["name"].tolist()
    prices = filtered_df["price"].tolist()
    text_list = [(names[i], prices[i]) for i in range(0, len(names))]
    fig = go.Figure(go.Scattermapbox(
            customdata=text_list,
            lat=filtered_df['latitude'].tolist(),
            lon=filtered_df['longitude'].tolist(),
            mode='markers',
            marker=go.scattermapbox.Marker(
                size=6
            ),
            hoverinfo="text",
            hovertemplate='Name: %{customdata[0]}
Price: $%{customdata[1]}'
        ))

    fig.update_layout(
        mapbox_style="open-street-map",
        hovermode='closest',
        mapbox=dict(
            bearing=0,
            center=go.layout.mapbox.Center(
                lat=40.67,
                lon=-73.90
            ),
            pitch=0,
            zoom=9
        ),
    )

    return fig

with gr.Blocks() as demo:
    with gr.Column():
        with gr.Row():
            min_price = gr.Number(value=250, label="Minimum Price")
            max_price = gr.Number(value=1000, label="Maximum Price")
        boroughs = gr.CheckboxGroup(choices=["Queens", "Brooklyn", "Manhattan", "Bronx", "Staten Island"], value=["Queens", "Brooklyn"], label="Select Boroughs:")
        btn = gr.Button(value="Update Filter")
        map = gr.Plot().style()
    demo.load(filter_map, [min_price, max_price, boroughs], map)
    btn.click(filter_map, [min_price, max_price, boroughs], map)

if __name__ == "__main__":
    demo.launch()

Plot

gradio.Plot(···)

Used to display various kinds of plots (matplotlib, plotly, or bokeh are supported)

As input: this component does *not* accept input.

As output: expects either a matplotlib.figure.Figure, a plotly.graph_objects._figure.Figure, or a dict corresponding to a bokeh plot (json_item format)

import altair as alt
import gradio as gr
import numpy as np
import pandas as pd
from vega_datasets import data


def make_plot(plot_type):
    if plot_type == "scatter_plot":
        cars = data.cars()
        return alt.Chart(cars).mark_point().encode(
            x='Horsepower',
            y='Miles_per_Gallon',
            color='Origin',
        )
    elif plot_type == "heatmap":
        # Compute x^2 + y^2 across a 2D grid
        x, y = np.meshgrid(range(-5, 5), range(-5, 5))
        z = x ** 2 + y ** 2

        # Convert this grid to columnar data expected by Altair
        source = pd.DataFrame({'x': x.ravel(),
                            'y': y.ravel(),
                            'z': z.ravel()})
        return alt.Chart(source).mark_rect().encode(
            x='x:O',
            y='y:O',
            color='z:Q'
        )
    elif plot_type == "us_map":
        states = alt.topo_feature(data.us_10m.url, 'states')
        source = data.income.url

        return alt.Chart(source).mark_geoshape().encode(
            shape='geo:G',
            color='pct:Q',
            tooltip=['name:N', 'pct:Q'],
            facet=alt.Facet('group:N', columns=2),
        ).transform_lookup(
            lookup='id',
            from_=alt.LookupData(data=states, key='id'),
            as_='geo'
        ).properties(
            width=300,
            height=175,
        ).project(
            type='albersUsa'
        )
    elif plot_type == "interactive_barplot":
        source = data.movies.url

        pts = alt.selection(type="single", encodings=['x'])

        rect = alt.Chart(data.movies.url).mark_rect().encode(
            alt.X('IMDB_Rating:Q', bin=True),
            alt.Y('Rotten_Tomatoes_Rating:Q', bin=True),
            alt.Color('count()',
                scale=alt.Scale(scheme='greenblue'),
                legend=alt.Legend(title='Total Records')
            )
        )

        circ = rect.mark_point().encode(
            alt.ColorValue('grey'),
            alt.Size('count()',
                legend=alt.Legend(title='Records in Selection')
            )
        ).transform_filter(
            pts
        )

        bar = alt.Chart(source).mark_bar().encode(
            x='Major_Genre:N',
            y='count()',
            color=alt.condition(pts, alt.ColorValue("steelblue"), alt.ColorValue("grey"))
        ).properties(
            width=550,
            height=200
        ).add_selection(pts)

        plot = alt.vconcat(
            rect + circ,
            bar
        ).resolve_legend(
            color="independent",
            size="independent"
        )
        return plot
    elif plot_type == "radial":
        source = pd.DataFrame({"values": [12, 23, 47, 6, 52, 19]})

        base = alt.Chart(source).encode(
            theta=alt.Theta("values:Q", stack=True),
            radius=alt.Radius("values", scale=alt.Scale(type="sqrt", zero=True, rangeMin=20)),
            color="values:N",
        )

        c1 = base.mark_arc(innerRadius=20, stroke="#fff")

        c2 = base.mark_text(radiusOffset=10).encode(text="values:Q")

        return c1 + c2
    elif plot_type == "multiline":
        source = data.stocks()

        highlight = alt.selection(type='single', on='mouseover',
                                fields=['symbol'], nearest=True)

        base = alt.Chart(source).encode(
            x='date:T',
            y='price:Q',
            color='symbol:N'
        )

        points = base.mark_circle().encode(
            opacity=alt.value(0)
        ).add_selection(
            highlight
        ).properties(
            width=600
        )

        lines = base.mark_line().encode(
            size=alt.condition(~highlight, alt.value(1), alt.value(3))
        )

        return points + lines


with gr.Blocks() as demo:
    button = gr.Radio(label="Plot type",
                      choices=['scatter_plot', 'heatmap', 'us_map',
                               'interactive_barplot', "radial", "multiline"], value='scatter_plot')
    plot = gr.Plot(label="Plot")
    button.change(make_plot, inputs=button, outputs=[plot])
    demo.load(make_plot, inputs=[button], outputs=[plot])


if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` Callable \| None \| pd.DataFrame default: None	Optionally, supply a default plot object to display, must be a matplotlib, plotly, altair, or bokeh figure, or a callable. If callable, the function will be called whenever the app loads to set the initial value of the component.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Plot`	"plot"	Uses default values

Methods

change

gradio.Plot.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

clear

gradio.Plot.clear(fn, ···)

This event is triggered when the user clears the component (e.g. image or audio) using the X button for the component. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

Plot Component For Maps

import gradio as gr


def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose betwen 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

if __name__ == "__main__":
    demo.launch()

import os

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

import gradio as gr

current_dir = os.path.dirname(os.path.realpath(__file__))
data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv"))


def encode_age(df):
    df.Age = df.Age.fillna(-0.5)
    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
    categories = pd.cut(df.Age, bins, labels=False)
    df.Age = categories
    return df


def encode_fare(df):
    df.Fare = df.Fare.fillna(-0.5)
    bins = (-1, 0, 8, 15, 31, 1000)
    categories = pd.cut(df.Fare, bins, labels=False)
    df.Fare = categories
    return df


def encode_df(df):
    df = encode_age(df)
    df = encode_fare(df)
    sex_mapping = {"male": 0, "female": 1}
    df = df.replace({"Sex": sex_mapping})
    embark_mapping = {"S": 1, "C": 2, "Q": 3}
    df = df.replace({"Embarked": embark_mapping})
    df.Embarked = df.Embarked.fillna(0)
    df["Company"] = 0
    df.loc[(df["SibSp"] > 0), "Company"] = 1
    df.loc[(df["Parch"] > 0), "Company"] = 2
    df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
    df = df[
        [
            "PassengerId",
            "Pclass",
            "Sex",
            "Age",
            "Fare",
            "Embarked",
            "Company",
            "Survived",
        ]
    ]
    return df


train = encode_df(data)

X_all = train.drop(["Survived", "PassengerId"], axis=1)
y_all = train["Survived"]

num_test = 0.20
X_train, X_test, y_train, y_test = train_test_split(
    X_all, y_all, test_size=num_test, random_state=23
)

clf = RandomForestClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)


def predict_survival(passenger_class, is_male, age, company, fare, embark_point):
    if passenger_class is None or embark_point is None:
        return None
    df = pd.DataFrame.from_dict(
        {
            "Pclass": [passenger_class + 1],
            "Sex": [0 if is_male else 1],
            "Age": [age],
            "Fare": [fare],
            "Embarked": [embark_point + 1],
            "Company": [
                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
            ]
        }
    )
    df = encode_age(df)
    df = encode_fare(df)
    pred = clf.predict_proba(df)[0]
    return {"Perishes": float(pred[0]), "Survives": float(pred[1])}


demo = gr.Interface(
    predict_survival,
    [
        gr.Dropdown(["first", "second", "third"], type="index"),
        "checkbox",
        gr.Slider(0, 80, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        gr.Radio(["S", "C", "Q"], type="index"),
    ],
    "label",
    examples=[
        ["first", True, 30, [], 50, "S"],
        ["second", False, 40, ["Sibling", "Child"], 10, "Q"],
        ["third", True, 30, ["Child"], 20, "S"],
    ],
    interpretation="default",
    live=True,
)

if __name__ == "__main__":
    demo.launch()

import gradio as gr


def change_textbox(choice):
    if choice == "short":
        return gr.Textbox.update(lines=2, visible=True)
    elif choice == "long":
        return gr.Textbox.update(lines=8, visible=True)
    else:
        return gr.Textbox.update(visible=False)


with gr.Blocks() as demo:
    radio = gr.Radio(
        ["short", "long", "none"], label="What kind of essay would you like to write?"
    )
    text = gr.Textbox(lines=2, interactive=True).style(show_copy_button=True)

    radio.change(fn=change_textbox, inputs=radio, outputs=text)


if __name__ == "__main__":
    demo.launch()

Radio

gradio.Radio(···)

Creates a set of radio buttons of which only one can be selected.

As input: passes the value of the selected radio button as a str or its index as an int into the function, depending on `type`.

As output: expects a str corresponding to the value of the radio button to be selected.

Format expected for examples: a str representing the radio option to select.

import gradio as gr


def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose betwen 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`choices` list[str] \| None default: None	list of options to select from.
`value` str \| Callable \| None default: None	the button selected by default. If None, no button is selected by default. If callable, the function will be called whenever the app loads to set the initial value of the component.
`type` str default: "value"	Type of value to be returned by component. "value" returns the string of the choice selected, "index" returns the index of the choice selected.
`label` str \| None default: None	component name in interface.
`info` str \| None default: None	additional component description.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, choices in this radio group will be selectable; if False, selection will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Radio`	"radio"	Uses default values

Methods

style

gradio.Radio.style(···)

This method can be used to change the appearance of the radio component.

Parameter Description


                item_container

bool | None

default: None

If True, will place items in a container.


                container

bool | None

default: None

If True, will place the component in a container - providing some extra padding around the border.

change

gradio.Radio.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

select

gradio.Radio.select(fn, ···)

Event listener for when the user selects Radio option. Uses event data gradio.SelectData to carry `value` referring to label of selected option, and `index` to refer to index. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about Radio

import gradio as gr

from scatter_plot_demo import scatter_plot
from line_plot_demo import line_plot
from bar_plot_demo import bar_plot


with gr.Blocks() as demo:
    with gr.Tabs():
        with gr.TabItem("Scatter Plot"):
            scatter_plot.render()
        with gr.TabItem("Line Plot"):
            line_plot.render()
        with gr.TabItem("Bar Plot"):
            bar_plot.render()

if __name__ == "__main__":
    demo.launch()

ScatterPlot

gradio.ScatterPlot(···)

Create a scatter plot.

As input: this component does *not* accept input.

As output: expects a pandas dataframe with the data to plot.

import gradio as gr

from scatter_plot_demo import scatter_plot
from line_plot_demo import line_plot
from bar_plot_demo import bar_plot


with gr.Blocks() as demo:
    with gr.Tabs():
        with gr.TabItem("Scatter Plot"):
            scatter_plot.render()
        with gr.TabItem("Line Plot"):
            line_plot.render()
        with gr.TabItem("Bar Plot"):
            bar_plot.render()

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` pd.DataFrame \| Callable \| None default: None	The pandas dataframe containing the data to display in a scatter plot, or a callable. If callable, the function will be called whenever the app loads to set the initial value of the component.
`x` str \| None default: None	Column corresponding to the x axis.
`y` str \| None default: None	Column corresponding to the y axis.
`color` str \| None default: None	The column to determine the point color. If the column contains numeric data, gradio will interpolate the column data so that small values correspond to light colors and large values correspond to dark values.
`size` str \| None default: None	The column used to determine the point size. Should contain numeric data so that gradio can map the data to the point size.
`shape` str \| None default: None	The column used to determine the point shape. Should contain categorical data. Gradio will map each unique value to a different shape.
`title` str \| None default: None	The title to display on top of the chart.
`tooltip` list[str] \| str \| None default: None	The column (or list of columns) to display on the tooltip when a user hovers a point on the plot.
`x_title` str \| None default: None	The title given to the x axis. By default, uses the value of the x parameter.
`y_title` str \| None default: None	The title given to the y axis. By default, uses the value of the y parameter.
`color_legend_title` str \| None default: None	The title given to the color legend. By default, uses the value of color parameter.
`size_legend_title` str \| None default: None	The title given to the size legend. By default, uses the value of the size parameter.
`shape_legend_title` str \| None default: None	The title given to the shape legend. By default, uses the value of the shape parameter.
`color_legend_position` str \| None default: None	The position of the color legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
`size_legend_position` str \| None default: None	The position of the size legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
`shape_legend_position` str \| None default: None	The position of the shape legend. If the string value 'none' is passed, this legend is omitted. For other valid position values see: https://vega.github.io/vega/docs/legends/#orientation.
`height` int \| None default: None	The height of the plot in pixels.
`width` int \| None default: None	The width of the plot in pixels.
`x_lim` list[int \| float] \| None default: None	A tuple or list containing the limits for the x-axis, specified as [x_min, x_max].
`y_lim` list[int \| float] \| None default: None	A tuple of list containing the limits for the y-axis, specified as [y_min, y_max].
`caption` str \| None default: None	The (optional) caption to display below the plot.
`interactive` bool \| None default: True	Whether users should be able to interact with the plot by panning or zooming with their mouse or trackpad.
`label` str \| None default: None	The (optional) label to display on the top left corner of the plot.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	Whether the label should be displayed.
`visible` bool default: True	Whether the plot should be visible.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.ScatterPlot`	"scatterplot"	Uses default values

Methods

change

gradio.ScatterPlot.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

clear

gradio.ScatterPlot.clear(fn, ···)

This event is triggered when the user clears the component (e.g. image or audio) using the X button for the component. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

Creating A Dashboard From Bigquery Data

import gradio as gr


def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose betwen 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

if __name__ == "__main__":
    demo.launch()

import gradio as gr


def identity(x, state):
    state += 1
    return x, state, state


with gr.Blocks() as demo:
    slider = gr.Slider(0, 100, step=0.1)
    state = gr.State(value=0)
    with gr.Row():
        number = gr.Number(label="On release")
        number2 = gr.Number(label="Number of events fired")
    slider.release(identity, inputs=[slider, state], outputs=[number, state, number2], api_name="predict")

if __name__ == "__main__":
    print("here")
    demo.launch()
    print(demo.server_port)

import numpy as np
import gradio as gr

notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]

def generate_tone(note, octave, duration):
    sr = 48000
    a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)
    frequency = a4_freq * 2 ** (tones_from_a4 / 12)
    duration = int(duration)
    audio = np.linspace(0, duration, duration * sr)
    audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)
    return sr, audio

demo = gr.Interface(
    generate_tone,
    [
        gr.Dropdown(notes, type="index"),
        gr.Slider(4, 6, step=1),
        gr.Textbox(value=1, label="Duration in seconds"),
    ],
    "audio",
)
if __name__ == "__main__":
    demo.launch()

import os

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

import gradio as gr

current_dir = os.path.dirname(os.path.realpath(__file__))
data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv"))


def encode_age(df):
    df.Age = df.Age.fillna(-0.5)
    bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
    categories = pd.cut(df.Age, bins, labels=False)
    df.Age = categories
    return df


def encode_fare(df):
    df.Fare = df.Fare.fillna(-0.5)
    bins = (-1, 0, 8, 15, 31, 1000)
    categories = pd.cut(df.Fare, bins, labels=False)
    df.Fare = categories
    return df


def encode_df(df):
    df = encode_age(df)
    df = encode_fare(df)
    sex_mapping = {"male": 0, "female": 1}
    df = df.replace({"Sex": sex_mapping})
    embark_mapping = {"S": 1, "C": 2, "Q": 3}
    df = df.replace({"Embarked": embark_mapping})
    df.Embarked = df.Embarked.fillna(0)
    df["Company"] = 0
    df.loc[(df["SibSp"] > 0), "Company"] = 1
    df.loc[(df["Parch"] > 0), "Company"] = 2
    df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
    df = df[
        [
            "PassengerId",
            "Pclass",
            "Sex",
            "Age",
            "Fare",
            "Embarked",
            "Company",
            "Survived",
        ]
    ]
    return df


train = encode_df(data)

X_all = train.drop(["Survived", "PassengerId"], axis=1)
y_all = train["Survived"]

num_test = 0.20
X_train, X_test, y_train, y_test = train_test_split(
    X_all, y_all, test_size=num_test, random_state=23
)

clf = RandomForestClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)


def predict_survival(passenger_class, is_male, age, company, fare, embark_point):
    if passenger_class is None or embark_point is None:
        return None
    df = pd.DataFrame.from_dict(
        {
            "Pclass": [passenger_class + 1],
            "Sex": [0 if is_male else 1],
            "Age": [age],
            "Fare": [fare],
            "Embarked": [embark_point + 1],
            "Company": [
                (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
            ]
        }
    )
    df = encode_age(df)
    df = encode_fare(df)
    pred = clf.predict_proba(df)[0]
    return {"Perishes": float(pred[0]), "Survives": float(pred[1])}


demo = gr.Interface(
    predict_survival,
    [
        gr.Dropdown(["first", "second", "third"], type="index"),
        "checkbox",
        gr.Slider(0, 80, value=25),
        gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
        gr.Number(value=20),
        gr.Radio(["S", "C", "Q"], type="index"),
    ],
    "label",
    examples=[
        ["first", True, 30, [], 50, "S"],
        ["second", False, 40, ["Sibling", "Child"], 10, "Q"],
        ["third", True, 30, ["Child"], 20, "S"],
    ],
    interpretation="default",
    live=True,
)

if __name__ == "__main__":
    demo.launch()

import gradio as gr


def func(slider_1, slider_2, *args):
    return slider_1 + slider_2 * 5


demo = gr.Interface(
    func,
    [
        gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label="Random Big Range"),
        gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label="Random only multiple of 0.05 allowed"),
        gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label="Random only multiples of 0.25 allowed"),
        gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label="Random between -100 and 100 step 3"),
        gr.Slider(minimum=-100, maximum=100, randomize=True, label="Random between -100 and 100"),
        gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),
    ],
    "number",
    interpretation="default"
)

if __name__ == "__main__":
    demo.launch()


import gradio as gr


def func(slider_1, slider_2):
    return slider_1 * 5 + slider_2


with gr.Blocks() as demo:
    slider = gr.Slider(minimum=-10.2, maximum=15, label="Random Slider (Static)", randomize=True)
    slider_1 = gr.Slider(minimum=100, maximum=200, label="Random Slider (Input 1)", randomize=True)
    slider_2 = gr.Slider(minimum=10, maximum=23.2, label="Random Slider (Input 2)", randomize=True)
    slider_3 = gr.Slider(value=3, label="Non random slider")
    btn = gr.Button("Run")
    btn.click(func, inputs=[slider_1, slider_2], outputs=gr.Number())

if __name__ == "__main__":
    demo.launch()

Slider

gradio.Slider(···)

Creates a slider that ranges from `minimum` to `maximum` with a step size of `step`.

As input: passes slider value as a float into the function.

As output: expects an int or float returned from function and sets slider value to it as long as it is within range.

Format expected for examples: A float or int representing the slider's value.

import gradio as gr


def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose betwen 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`minimum` float default: 0	minimum value for slider.
`maximum` float default: 100	maximum value for slider.
`value` float \| Callable \| None default: None	default value. If callable, the function will be called whenever the app loads to set the initial value of the component. Ignored if randomized=True.
`step` float \| None default: None	increment between slider values.
`label` str \| None default: None	component name in interface.
`info` str \| None default: None	additional component description.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, slider will be adjustable; if False, adjusting will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
`randomize` bool default: False	If True, the value of the slider when the app loads is taken uniformly at random from the range given by the minimum and maximum.

Class	Interface String Shortcut	Initialization
`gradio.Slider`	"slider"	Uses default values

Methods

style

gradio.Slider.style(···)

This method can be used to change the appearance of the slider.

Parameter Description


                container

bool | None

default: None

If True, will place the component in a container - providing some extra padding around the border.

change

gradio.Slider.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

release

gradio.Slider.release(fn, ···)

This event is triggered when the user releases the mouse on this component (e.g. when the user releases the slider). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

Create Your Own Friends With A Gan

import gradio as gr

demo = gr.Blocks(css="""#btn {color: red} .abc {font-family: "Comic Sans MS", "Comic Sans", cursive !important}""")

with demo:
    default_json = {"a": "a"}

    num = gr.State(value=0)
    squared = gr.Number(value=0)
    btn = gr.Button("Next Square", elem_id="btn", elem_classes=["abc", "def"])

    stats = gr.State(value=default_json)
    table = gr.JSON()

    def increase(var, stats_history):
        var += 1
        stats_history[str(var)] = var**2
        return var, var**2, stats_history, stats_history

    btn.click(increase, [num, stats], [num, squared, stats, table])

if __name__ == "__main__":
    demo.launch()

State

gradio.State(···)

Special hidden component that stores session state across runs of the demo by the same user. The value of the State variable is cleared when the user refreshes the page.

As input: No preprocessing is performed

As output: No postprocessing is performed

import gradio as gr

demo = gr.Blocks(css="""#btn {color: red} .abc {font-family: "Comic Sans MS", "Comic Sans", cursive !important}""")

with demo:
    default_json = {"a": "a"}

    num = gr.State(value=0)
    squared = gr.Number(value=0)
    btn = gr.Button("Next Square", elem_id="btn", elem_classes=["abc", "def"])

    stats = gr.State(value=default_json)
    table = gr.JSON()

    def increase(var, stats_history):
        var += 1
        stats_history[str(var)] = var**2
        return var, var**2, stats_history, stats_history

    btn.click(increase, [num, stats], [num, squared, stats, table])

if __name__ == "__main__":
    demo.launch()

Parameter Description


                value

Any

default: None

the initial value (of abitrary type) of the state. The provided argument is deepcopied. If a callable is provided, the function will be called whenever the app loads to set the initial value of the state.

Step-by-step Guides

Real Time Speech Recognition

import gradio as gr

def greet(name):
    return "Hello " + name + "!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
    
if __name__ == "__main__":
    demo.launch()

from difflib import Differ

import gradio as gr


def diff_texts(text1, text2):
    d = Differ()
    return [
        (token[2:], token[0] if token[0] != " " else None)
        for token in d.compare(text1, text2)
    ]


demo = gr.Interface(
    diff_texts,
    [
        gr.Textbox(
            label="Text 1",
            info="Initial text",
            lines=3,
            value="The quick brown fox jumped over the lazy dogs.",
        ),
        gr.Textbox(
            label="Text 2",
            info="Text to compare",
            lines=3,
            value="The fast brown fox jumps over lazy dogs.",
        ),
    ],
    gr.HighlightedText(
        label="Diff",
        combine_adjacent=True,
        show_legend=True,
    ).style(color_map={"+": "red", "-": "green"}),
    theme=gr.themes.Base()
)
if __name__ == "__main__":
    demo.launch()

import gradio as gr


def sentence_builder(quantity, animal, countries, place, activity_list, morning):
    return f"""The {quantity} {animal}s from {" and ".join(countries)} went to the {place} where they {" and ".join(activity_list)} until the {"morning" if morning else "night"}"""


demo = gr.Interface(
    sentence_builder,
    [
        gr.Slider(2, 20, value=4, label="Count", info="Choose betwen 2 and 20"),
        gr.Dropdown(
            ["cat", "dog", "bird"], label="Animal", info="Will add more animals later!"
        ),
        gr.CheckboxGroup(["USA", "Japan", "Pakistan"], label="Countries", info="Where are they from?"),
        gr.Radio(["park", "zoo", "road"], label="Location", info="Where did they go?"),
        gr.Dropdown(
            ["ran", "swam", "ate", "slept"], value=["swam", "slept"], multiselect=True, label="Activity", info="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl."
        ),
        gr.Checkbox(label="Morning", info="Did they do it in the morning?"),
    ],
    "text",
    examples=[
        [2, "cat", ["Japan", "Pakistan"], "park", ["ate", "swam"], True],
        [4, "dog", ["Japan"], "zoo", ["ate", "swam"], False],
        [10, "bird", ["USA", "Pakistan"], "road", ["ran"], False],
        [8, "cat", ["Pakistan"], "zoo", ["ate"], True],
    ]
)

if __name__ == "__main__":
    demo.launch()

Textbox

gradio.Textbox(···)

Creates a textarea for user to enter string input or display string output.

As input: passes textarea value as a str into the function.

As output: expects a str returned from function and sets textarea value to it.

Format expected for examples: a str representing the textbox input.

import gradio as gr

def greet(name):
    return "Hello " + name + "!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
    
if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` str \| Callable \| None default: ""	default text to provide in textarea. If callable, the function will be called whenever the app loads to set the initial value of the component.
`lines` int default: 1	minimum number of line rows to provide in textarea.
`max_lines` int default: 20	maximum number of line rows to provide in textarea.
`placeholder` str \| None default: None	placeholder hint to provide behind textarea.
`label` str \| None default: None	component name in interface.
`info` str \| None default: None	additional component description.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, will be rendered as an editable textbox; if False, editing will be disabled. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
`type` str default: "text"	The type of textbox. One of: 'text', 'password', 'email', Default is 'text'.

Class	Interface String Shortcut	Initialization
`gradio.Textbox`	"textbox"	Uses default values
`gradio.TextArea`	"textarea"	Uses lines=7

Methods

style

gradio.Textbox.style(···)

This method can be used to change the appearance of the Textbox component.

Parameter Description


                show_copy_button

bool | None

default: None

If True, includes a copy button to copy the text in the textbox. Only applies if show_label is True.


                container

bool | None

default: None

If True, will place the component in a container - providing some extra padding around the border.

change

gradio.Textbox.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

submit

gradio.Textbox.submit(fn, ···)

This event is triggered when the user presses the Enter key while the component (e.g. a textbox) is focused. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

blur

gradio.Textbox.blur(fn, ···)

This event is triggered when the component's is unfocused/blurred (e.g. when the user clicks outside of a textbox). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

select

gradio.Textbox.select(fn, ···)

Event listener for when the user selects text in the Textbox. Uses event data gradio.SelectData to carry `value` referring to selected subtring, and `index` tuple referring to selected range endpoints. See EventData documentation on how to use this event data.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

Creating A Chatbot

Real Time Speech Recognition

import random
import os
import gradio as gr


def fraud_detector(card_activity, categories, sensitivity):
    activity_range = random.randint(0, 100)
    drop_columns = [
        column for column in ["retail", "food", "other"] if column not in categories
    ]
    if len(drop_columns):
        card_activity.drop(columns=drop_columns, inplace=True)
    return (
        card_activity,
        card_activity,
        {"fraud": activity_range / 100.0, "not fraud": 1 - activity_range / 100.0},
    )


demo = gr.Interface(
    fraud_detector,
    [
        gr.Timeseries(x="time", y=["retail", "food", "other"]),
        gr.CheckboxGroup(
            ["retail", "food", "other"], value=["retail", "food", "other"]
        ),
        gr.Slider(1, 3),
    ],
    [
        "dataframe",
        gr.Timeseries(x="time", y=["retail", "food", "other"]),
        gr.Label(label="Fraud Level"),
    ],
    examples=[
        [os.path.join(os.path.dirname(__file__), "fraud.csv"), ["retail", "food", "other"], 1.0],
    ],
)
if __name__ == "__main__":
    demo.launch()

Timeseries

gradio.Timeseries(···)

Creates a component that can be used to upload/preview timeseries csv files or display a dataframe consisting of a time series graphically.

As input: passes the uploaded timeseries data as a pandas.DataFrame into the function

As output: expects a pandas.DataFrame or str path to a csv to be returned, which is then displayed as a timeseries graph

Format expected for examples: a str filepath of csv data with time series data.

import random
import os
import gradio as gr


def fraud_detector(card_activity, categories, sensitivity):
    activity_range = random.randint(0, 100)
    drop_columns = [
        column for column in ["retail", "food", "other"] if column not in categories
    ]
    if len(drop_columns):
        card_activity.drop(columns=drop_columns, inplace=True)
    return (
        card_activity,
        card_activity,
        {"fraud": activity_range / 100.0, "not fraud": 1 - activity_range / 100.0},
    )


demo = gr.Interface(
    fraud_detector,
    [
        gr.Timeseries(x="time", y=["retail", "food", "other"]),
        gr.CheckboxGroup(
            ["retail", "food", "other"], value=["retail", "food", "other"]
        ),
        gr.Slider(1, 3),
    ],
    [
        "dataframe",
        gr.Timeseries(x="time", y=["retail", "food", "other"]),
        gr.Label(label="Fraud Level"),
    ],
    examples=[
        [os.path.join(os.path.dirname(__file__), "fraud.csv"), ["retail", "food", "other"], 1.0],
    ],
)
if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` str \| Callable \| None default: None	File path for the timeseries csv file. If callable, the function will be called whenever the app loads to set the initial value of the component.
`x` str \| None default: None	Column name of x (time) series. None if csv has no headers, in which case first column is x series.
`y` str \| list[str] \| None default: None	Column name of y series, or list of column names if multiple series. None if csv has no headers, in which case every column after first is a y series.
`colors` list[str] \| None default: None	an ordered list of colors to use for each line plot
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, will allow users to upload a timeseries csv; if False, can only be used to display timeseries data. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.

Class	Interface String Shortcut	Initialization
`gradio.Timeseries`	"timeseries"	Uses default values

Methods

style

gradio.Timeseries.style(···)

This method can be used to change the appearance of the TimeSeries component.

change

gradio.Timeseries.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about Timeseries

import gradio as gr

def upload_file(files):
    file_paths = [file.name for file in files]
    return file_paths

with gr.Blocks() as demo:
    file_output = gr.File()
    upload_button = gr.UploadButton("Click to Upload a File", file_types=["image", "video"], file_count="multiple")
    upload_button.upload(upload_file, upload_button, file_output)

demo.launch()

UploadButton

gradio.UploadButton(···)

Used to create an upload button, when cicked allows a user to upload files that satisfy the specified file type or generic files (if file_type not set).

As input: passes the uploaded file as a file-object or List[file-object] depending on `file_count` (or a bytes/Listbytes depending on `type`)

As output: expects function to return a str path to a file, or List[str] consisting of paths to files.

Format expected for examples: a str path to a local file that populates the component.

import gradio as gr

def upload_file(files):
    file_paths = [file.name for file in files]
    return file_paths

with gr.Blocks() as demo:
    file_output = gr.File()
    upload_button = gr.UploadButton("Click to Upload a File", file_types=["image", "video"], file_count="multiple")
    upload_button.upload(upload_file, upload_button, file_output)

demo.launch()

Parameter	Description
`label` str default: "Upload a File"	Text to display on the button. Defaults to "Upload a File".
`value` str \| list[str] \| Callable \| None default: None	Default text for the button to display.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
`type` str default: "file"	Type of value to be returned by component. "file" returns a temporary file object with the same base name as the uploaded file, whose full path can be retrieved by file_obj.name, "binary" returns an bytes object.
`file_count` str default: "single"	if single, allows user to upload one file. If "multiple", user uploads multiple files. If "directory", user uploads all files in selected directory. Return type will be list for each file in case of "multiple" or "directory".
`file_types` list[str] \| None default: None	List of type of files to be uploaded. "file" allows any file to be uploaded, "image" allows only image files to be uploaded, "audio" allows only audio files to be uploaded, "video" allows only video files to be uploaded, "text" allows only text files to be uploaded.

Class	Interface String Shortcut	Initialization
`gradio.UploadButton`	"uploadbutton"	Uses default values

Methods

style

gradio.UploadButton.style(···)

This method can be used to change the appearance of the button component.

Parameter Description


                full_width

bool | None

default: None

If True, will expand to fill parent container.


                size

Literal['sm'] | Literal['lg'] | None

default: None

Size of the button. Can be "sm" or "lg".

click

gradio.UploadButton.click(fn, ···)

This event is triggered when the component (e.g. a button) is clicked. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

upload

gradio.UploadButton.upload(fn, ···)

This event is triggered when the user uploads a file into the component (e.g. when the user uploads a video into a video component). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about UploadButton

import gradio as gr
import os


def video_identity(video):
    return video


demo = gr.Interface(video_identity, 
                    gr.Video(), 
                    "playable_video", 
                    examples=[
                        os.path.join(os.path.dirname(__file__), 
                                     "video/video_sample.mp4")], 
                    cache_examples=True)

if __name__ == "__main__":
    demo.launch()

import gradio as gr
import os

a = os.path.join(os.path.dirname(__file__), "files/a.mp4")  # Video
b = os.path.join(os.path.dirname(__file__), "files/b.mp4")  # Video
s1 = os.path.join(os.path.dirname(__file__), "files/s1.srt")  # Subtitle
s2 = os.path.join(os.path.dirname(__file__), "files/s2.vtt")  # Subtitle


def video_demo(video, subtitle=None):
    if subtitle is None:
        return video

    return [video, subtitle.name]


demo = gr.Interface(
    fn=video_demo,
    inputs=[
        gr.Video(type="file", label="In", interactive=True),
        gr.File(label="Subtitle", file_types=[".srt", ".vtt"]),
    ],
    outputs=gr.Video(label="Out"),
    examples=[
        [a, s1],
        [b, s2],
        [a, None],
    ],
)

if __name__ == "__main__":
    demo.launch()

Video

gradio.Video(···)

Creates a video component that can be used to upload/record videos (as an input) or display videos (as an output). For the video to be playable in the browser it must have a compatible container and codec combination. Allowed combinations are .mp4 with h264 codec, .ogg with theora codec, and .webm with vp9 codec. If the component detects that the output video would not be playable in the browser it will attempt to convert it to a playable mp4 video. If the conversion fails, the original video is returned.

As input: passes the uploaded video as a str filepath or URL whose extension can be modified by `format`.

As output: expects a str filepath to a video which is displayed, or a Tuple[str, str] where the first element is a filepath to a video and the second element is a filepath to a subtitle file.

Format expected for examples: a str filepath to a local file that contains the video, or a Tuple[str, str] where the first element is a filepath to a video file and the second element is a filepath to a subtitle file.

import gradio as gr
import os


def video_identity(video):
    return video


demo = gr.Interface(video_identity, 
                    gr.Video(), 
                    "playable_video", 
                    examples=[
                        os.path.join(os.path.dirname(__file__), 
                                     "video/video_sample.mp4")], 
                    cache_examples=True)

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`value` str \| tuple[str, str \| None] \| Callable \| None default: None	A path or URL for the default value that Video component is going to take. Can also be a tuple consisting of (video filepath, subtitle filepath). If a subtitle file is provided, it should be of type .srt or .vtt. Or can be callable, in which case the function will be called whenever the app loads to set the initial value of the component.
`format` str \| None default: None	Format of video format to be returned by component, such as 'avi' or 'mp4'. Use 'mp4' to ensure browser playability. If set to None, video will keep uploaded format.
`source` str default: "upload"	Source of video. "upload" creates a box where user can drop an video file, "webcam" allows user to record a video from their webcam.
`label` str \| None default: None	component name in interface.
`every` float \| None default: None	If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute.
`show_label` bool default: True	if True, will display label.
`interactive` bool \| None default: None	if True, will allow users to upload a video; if False, can only be used to display videos. If not provided, this is inferred based on whether the component is used as an input or output.
`visible` bool default: True	If False, component will be hidden.
`elem_id` str \| None default: None	An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
`elem_classes` list[str] \| str \| None default: None	An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
`mirror_webcam` bool default: True	If True webcam will be mirrored. Default is True.
`include_audio` bool \| None default: None	Whether the component should record/retain the audio track for a video. By default, audio is excluded for webcam videos and included for uploaded videos.

Class	Interface String Shortcut	Initialization
`gradio.Video`	"video"	Uses default values
`gradio.PlayableVideo`	"playablevideo"	Uses format="mp4"

Methods

style

gradio.Video.style(···)

This method can be used to change the appearance of the video component.

Parameter Description


                height

int | None

default: None

Height of the video.


                width

int | None

default: None

Width of the video.

change

gradio.Video.change(fn, ···)

This event is triggered when the component's input value changes (e.g. when the user types in a textbox or uploads an image). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

clear

gradio.Video.clear(fn, ···)

This event is triggered when the user clears the component (e.g. image or audio) using the X button for the component. This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

play

gradio.Video.play(fn, ···)

This event is triggered when the user plays the component (e.g. audio or video). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

pause

gradio.Video.pause(fn, ···)

This event is triggered when the user pauses the component (e.g. audio or video). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

stop

gradio.Video.stop(fn, ···)

This event is triggered when the user stops the component (e.g. audio or video). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

upload

gradio.Video.upload(fn, ···)

This event is triggered when the user uploads a file into the component (e.g. when the user uploads a video into a video component). This method can be used when this component is in a Gradio Blocks.

Parameter	Description
`fn` Callable \| None required	the function to wrap an interface around. Often a machine learning model's prediction function. Each parameter of the function corresponds to one input component, and the function should return a single value or a tuple of values, with each element in the tuple corresponding to one output component.
`inputs` Component \| list[Component] \| set[Component] \| None default: None	List of gradio.components to use as inputs. If the function takes no inputs, this should be an empty list.
`outputs` Component \| list[Component] \| None default: None	List of gradio.components to use as outputs. If the function returns no outputs, this should be an empty list.
`api_name` str \| None default: None	Defining this parameter exposes the endpoint in the api docs
`status_tracker` StatusTracker \| None default: None
`scroll_to_output` bool default: False	If True, will scroll to output component on completion
`show_progress` bool \| None default: None	If True, will show progress animation while pending
`queue` bool \| None default: None	If True, will place the request on the queue, if the queue has been enabled. If False, will not put this event on the queue, even if the queue has been enabled. If None, will use the queue setting of the gradio app.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. The lists should be of equal length (and be up to length `max_batch_size`). The function is then required to return a tuple of lists (even if there is only 1 output component), with each list in the tuple corresponding to one output component.
`max_batch_size` int default: 4	Maximum number of inputs to batch together if this is called from the queue (only relevant if batch=True)
`preprocess` bool default: True	If False, will not run preprocessing of component data before running 'fn' (e.g. leaving it as a base64 string if this method is called with the `Image` component).
`postprocess` bool default: True	If False, will not run postprocessing of component data before returning 'fn' output to the browser.
`cancels` dict[str, Any] \| list[dict[str, Any]] \| None default: None	A list of other events to cancel when this event is triggered. For example, setting cancels=[click_event] will cancel the click_event, where click_event is the return value of another components .click method. Functions that have not yet run (or generators that are iterating) will be cancelled, but functions that are currently running will be allowed to finish.
`every` float \| None default: None	Run this event 'every' number of seconds while the client connection is open. Interpreted in seconds. Queue must be enabled.

Step-by-step Guides

No guides yet, contribute a guide about Video

Helpers

Gradio includes helper classes and methods that interact with existing components. The goal of these classes and methods is to help you add common functionality to your app without having to rewrite common functions.

import gradio as gr

def calculator(num1, operation, num2):
    if operation == "add":
        return num1 + num2
    elif operation == "subtract":
        return num1 - num2
    elif operation == "multiply":
        return num1 * num2
    elif operation == "divide":
        if num2 == 0:
            raise gr.Error("Cannot divide by zero!")
        return num1 / num2

demo = gr.Interface(
    calculator,
    [
        "number", 
        gr.Radio(["add", "subtract", "multiply", "divide"]),
        "number"
    ],
    "number",
    examples=[
        [5, "add", 3],
        [4, "divide", 2],
        [-4, "multiply", 2.5],
        [0, "subtract", 1.2],
    ],
    title="Toy Calculator",
    description="Here's a sample toy calculator. Allows you to calculate things like $2+2=4$",
)
if __name__ == "__main__":
    demo.launch()

Error

gradio.Error(message, ···)

This class allows you to pass custom error messages to the user. You can do so by raising a gr.Error("custom message") anywhere in the code, and when that line is executed the custom message will appear in a modal on the demo.

import gradio as gr

def calculator(num1, operation, num2):
    if operation == "add":
        return num1 + num2
    elif operation == "subtract":
        return num1 - num2
    elif operation == "multiply":
        return num1 * num2
    elif operation == "divide":
        if num2 == 0:
            raise gr.Error("Cannot divide by zero!")
        return num1 / num2

demo = gr.Interface(
    calculator,
    [
        "number", 
        gr.Radio(["add", "subtract", "multiply", "divide"]),
        "number"
    ],
    "number",
    examples=[
        [5, "add", 3],
        [4, "divide", 2],
        [-4, "multiply", 2.5],
        [0, "subtract", 1.2],
    ],
    title="Toy Calculator",
    description="Here's a sample toy calculator. Allows you to calculate things like $2+2=4$",
)
if __name__ == "__main__":
    demo.launch()

Parameter Description


                message

required

The error message to be displayed to the user.

Step-by-step Guides

No guides yet, contribute a guide about Error

load

gradio.load(name, ···)

Method that constructs a Blocks from a Hugging Face repo. Can accept model repos (if src is "models") or Space repos (if src is "spaces"). The input and output components are automatically loaded from the repo.

Example Usage

import gradio as gr
demo = gr.load("gradio/question-answering", src="spaces")
demo.launch()

Parameter	Description
`name` str required	the name of the model (e.g. "gpt2" or "facebook/bart-base") or space (e.g. "flax-community/spanish-gpt2"), can include the `src` as prefix (e.g. "models/facebook/bart-base")
`src` str \| None default: None	the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
`api_key` str \| None default: None	Deprecated. Please use the `hf_token` parameter instead.
`hf_token` str \| None default: None	optional access token for loading private Hugging Face Hub models or spaces. Find your token here: https://huggingface.co/settings/tokens
`alias` str \| None default: None	optional string used as the name of the loaded model instead of the default name (only applies if loading a Space running Gradio 2.x)

Step-by-step Guides

No guides yet, contribute a guide about load

import gradio as gr
import os


def combine(a, b):
    return a + " " + b


def mirror(x):
    return x


with gr.Blocks() as demo:

    txt = gr.Textbox(label="Input", lines=2)
    txt_2 = gr.Textbox(label="Input 2")
    txt_3 = gr.Textbox(value="", label="Output")
    btn = gr.Button(value="Submit")
    btn.click(combine, inputs=[txt, txt_2], outputs=[txt_3])

    with gr.Row():
        im = gr.Image()
        im_2 = gr.Image()

    btn = gr.Button(value="Mirror Image")
    btn.click(mirror, inputs=[im], outputs=[im_2])

    gr.Markdown("## Text Examples")
    gr.Examples(
        [["hi", "Adam"], ["hello", "Eve"]],
        [txt, txt_2],
        txt_3,
        combine,
        cache_examples=True,
    )
    gr.Markdown("## Image Examples")
    gr.Examples(
        examples=[os.path.join(os.path.dirname(__file__), "lion.jpg")],
        inputs=im,
        outputs=im_2,
        fn=mirror,
        cache_examples=True,
    )

if __name__ == "__main__":
    demo.launch()

# This demo needs to be run from the repo folder.
# python demo/fake_gan/run.py
import random

import gradio as gr


def fake_gan():
    images = [
        (random.choice(
            [
                "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
                "https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80",
                "https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80",
                "https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
                "https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80",
            ]
        ), f"label {i}" if i != 0 else "label" * 50)
        for i in range(3)
    ]
    return images


with gr.Blocks() as demo:
    with gr.Column(variant="panel"):
        with gr.Row(variant="compact"):
            text = gr.Textbox(
                label="Enter your prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
            ).style(
                container=False,
            )
            btn = gr.Button("Generate image").style(full_width=False)

        gallery = gr.Gallery(
            label="Generated images", show_label=False, elem_id="gallery"
        ).style(columns=[2], rows=[2], object_fit="contain", height="auto")

    btn.click(fake_gan, None, gallery)

if __name__ == "__main__":
    demo.launch()

Examples

gradio.Examples(examples, inputs, ···)

This class is a wrapper over the Dataset component and can be used to create Examples for Blocks / Interfaces. Populates the Dataset component with examples and assigns event listener so that clicking on an example populates the input/output components. Optionally handles example caching for fast inference.

import gradio as gr
import os


def combine(a, b):
    return a + " " + b


def mirror(x):
    return x


with gr.Blocks() as demo:

    txt = gr.Textbox(label="Input", lines=2)
    txt_2 = gr.Textbox(label="Input 2")
    txt_3 = gr.Textbox(value="", label="Output")
    btn = gr.Button(value="Submit")
    btn.click(combine, inputs=[txt, txt_2], outputs=[txt_3])

    with gr.Row():
        im = gr.Image()
        im_2 = gr.Image()

    btn = gr.Button(value="Mirror Image")
    btn.click(mirror, inputs=[im], outputs=[im_2])

    gr.Markdown("## Text Examples")
    gr.Examples(
        [["hi", "Adam"], ["hello", "Eve"]],
        [txt, txt_2],
        txt_3,
        combine,
        cache_examples=True,
    )
    gr.Markdown("## Image Examples")
    gr.Examples(
        examples=[os.path.join(os.path.dirname(__file__), "lion.jpg")],
        inputs=im,
        outputs=im_2,
        fn=mirror,
        cache_examples=True,
    )

if __name__ == "__main__":
    demo.launch()

Parameter	Description
`examples` list[Any] \| list[list[Any]] \| str required	example inputs that can be clicked to populate specific components. Should be nested list, in which the outer list consists of samples and each inner list consists of an input corresponding to each input component. A string path to a directory of examples can also be provided but it should be within the directory with the python file running the gradio app. If there are multiple input components and a directory is provided, a log.csv file must be present in the directory to link corresponding inputs.
`inputs` IOComponent \| list[IOComponent] required	the component or list of components corresponding to the examples
`outputs` IOComponent \| list[IOComponent] \| None default: None	optionally, provide the component or list of components corresponding to the output of the examples. Required if `cache` is True.
`fn` Callable \| None default: None	optionally, provide the function to run to generate the outputs corresponding to the examples. Required if `cache` is True.
`cache_examples` bool default: False	if True, caches examples for fast runtime. If True, then `fn` and `outputs` need to be provided
`examples_per_page` int default: 10	how many examples to show per page.
`label` str \| None default: "Examples"	the label to use for the examples component (by default, "Examples")
`elem_id` str \| None default: None	an optional string that is assigned as the id of this component in the HTML DOM.
`run_on_click` bool default: False	if cache_examples is False, clicking on an example does not run the function when an example is clicked. Set this to True to run the function when an example is clicked. Has no effect if cache_examples is True.
`preprocess` bool default: True	if True, preprocesses the example input before running the prediction function and caching the output. Only applies if cache_examples is True.
`postprocess` bool default: True	if True, postprocesses the example output after running the prediction function and before caching. Only applies if cache_examples is True.
`batch` bool default: False	If True, then the function should process a batch of inputs, meaning that it should accept a list of input values for each parameter. Used only if cache_examples is True.

Step-by-step Guides

Using Hugging Face Integrations

Image Classification In Pytorch

Image Classification In Tensorflow

Image Classification With Vision Transformers

Create Your Own Friends With A Gan

import gradio as gr
import random
import time
import tqdm
from datasets import load_dataset
import shutil
from uuid import uuid4

with gr.Blocks() as demo:
    with gr.Row():
        text = gr.Textbox()
        textb = gr.Textbox()
    with gr.Row():
        load_set_btn = gr.Button("Load Set")
        load_nested_set_btn = gr.Button("Load Nested Set")
        load_random_btn = gr.Button("Load Random")
        clean_imgs_btn = gr.Button("Clean Images")
        wait_btn = gr.Button("Wait")
        do_all_btn = gr.Button("Do All")
        track_tqdm_btn = gr.Button("Bind TQDM")
        bind_internal_tqdm_btn = gr.Button("Bind Internal TQDM")

    text2 = gr.Textbox()

    # track list
    def load_set(text, text2, progress=gr.Progress()):
        imgs = [None] * 24
        for img in progress.tqdm(imgs, desc="Loading from list"):
            time.sleep(0.1)
        return "done"
    load_set_btn.click(load_set, [text, textb], text2)

    # track nested list
    def load_nested_set(text, text2, progress=gr.Progress()):
        imgs = [[None] * 8] * 3
        for img_set in progress.tqdm(imgs, desc="Nested list"):
            time.sleep(2)
            for img in progress.tqdm(img_set, desc="inner list"):
                time.sleep(0.1)
        return "done"
    load_nested_set_btn.click(load_nested_set, [text, textb], text2)

    # track iterable of unknown length
    def load_random(data, progress=gr.Progress()):
        def yielder():
            for i in range(0, random.randint(15, 20)):
                time.sleep(0.1)
                yield None
        for img in progress.tqdm(yielder()):
            pass
        return "done"
    load_random_btn.click(load_random, {text, textb}, text2)
        
    # manual progress
    def clean_imgs(text, progress=gr.Progress()):
        progress(0.2, desc="Collecting Images")
        time.sleep(1)
        progress(0.5, desc="Cleaning Images")
        time.sleep(1.5)
        progress(0.8, desc="Sending Images")
        time.sleep(1.5)
        return "done"
    clean_imgs_btn.click(clean_imgs, text, text2)

    # no progress
    def wait(text):
        time.sleep(4)
        return "done"
    wait_btn.click(wait, text, text2)

    # multiple progressions
    def do_all(data, progress=gr.Progress()):
        load_set(data[text], data[textb], progress)
        load_random(data, progress)
        clean_imgs(data[text], progress)
        progress(None)
        wait(text)
        return "done"
    do_all_btn.click(do_all, {text, textb}, text2)

    def track_tqdm(data, progress=gr.Progress(track_tqdm=True)):
        for i in tqdm.tqdm(range(5), desc="outer"):
            for j in tqdm.tqdm(range(4), desc="inner"):
                time.sleep(1)
        return "done"
    track_tqdm_btn.click(track_tqdm, {text, textb}, text2)

    def bind_internal_tqdm(data, progress=gr.Progress(track_tqdm=True)):
        outdir = "__tmp/" + str(uuid4())
        load_dataset("beans", split="train", cache_dir=outdir)
        shutil.rmtree(outdir)
        return "done"
    bind_internal_tqdm_btn.click(bind_internal_tqdm, {text, textb}, text2)


if __name__ == "__main__":
    demo.queue(concurrency_count=20).launch()

Progress

gradio.Progress(···)

The Progress class provides a custom progress tracker that is used in a function signature. To attach a Progress tracker to a function, simply add a parameter right after the input parameters that has a default value set to a `gradio.Progress()` instance. The Progress tracker can then be updated in the function by calling the Progress object or using the `tqdm` method on an Iterable. The Progress tracker is currently only available with `queue()`.

Example Usage

import gradio as gr
import time
def my_function(x, progress=gr.Progress()):
    progress(0, desc="Starting...")
    time.sleep(1)
    for i in progress.tqdm(range(100)):
        time.sleep(0.1)
    return x
gr.Interface(my_function, gr.Textbox(), gr.Textbox()).queue().launch()

Parameter Description


                track_tqdm

bool

default: False

If True, the Progress object will track any tqdm.tqdm iterations with the tqdm library in the function.

Methods

call

gradio.Progress(progress, ···)

Updates progress tracker with progress and message text.

Parameter	Description
`progress` float \| tuple[int, int \| None] \| None required	If float, should be between 0 and 1 representing completion. If Tuple, first number represents steps completed, and second value represents total steps or None if unknown. If None, hides progress bar.
`desc` str \| None default: None	description to display.
`total` int \| None default: None	estimated total number of steps.
`unit` str default: "steps"	unit of iterations.

tqdm

gradio.Progress.tqdm(iterable, ···)

Attaches progress tracker to iterable, like tqdm.

Parameter	Description
`iterable` Iterable \| None required	iterable to attach progress tracker to.
`desc` str \| None default: None	description to display.
`total` int \| None default: None	estimated total number of steps.
`unit` str default: "steps"	unit of iterations.

Step-by-step Guides

No guides yet, contribute a guide about Progress

import gradio as gr


def change_textbox(choice):
    if choice == "short":
        return gr.Textbox.update(lines=2, visible=True)
    elif choice == "long":
        return gr.Textbox.update(lines=8, visible=True)
    else:
        return gr.Textbox.update(visible=False)


with gr.Blocks() as demo:
    radio = gr.Radio(
        ["short", "long", "none"], label="What kind of essay would you like to write?"
    )
    text = gr.Textbox(lines=2, interactive=True).style(show_copy_button=True)

    radio.change(fn=change_textbox, inputs=radio, outputs=text)


if __name__ == "__main__":
    demo.launch()

import gradio as gr

with gr.Blocks() as demo:
    gr.Markdown(
        """
    # Animal Generator
    Once you select a species, the detail panel should be visible.
    """
    )

    species = gr.Radio(label="Animal Class", choices=["Mammal", "Fish", "Bird"])
    animal = gr.Dropdown(label="Animal", choices=[])

    with gr.Column(visible=False) as details_col:
        weight = gr.Slider(0, 20)
        details = gr.Textbox(label="Extra Details")
        generate_btn = gr.Button("Generate")
        output = gr.Textbox(label="Output")

    species_map = {
        "Mammal": ["Elephant", "Giraffe", "Hamster"],
        "Fish": ["Shark", "Salmon", "Tuna"],
        "Bird": ["Chicken", "Eagle", "Hawk"],
    }

    def filter_species(species):
        return gr.Dropdown.update(
            choices=species_map[species], value=species_map[species][1]
        ), gr.update(visible=True)

    species.change(filter_species, species, [animal, details_col])

    def filter_weight(animal):
        if animal in ("Elephant", "Shark", "Giraffe"):
            return gr.update(maximum=100)
        else:
            return gr.update(maximum=20)

    animal.change(filter_weight, animal, weight)
    weight.change(lambda w: gr.update(lines=int(w / 10) + 1), weight, details)

    generate_btn.click(lambda x: x, details, output)


if __name__ == "__main__":
    demo.launch()

import gradio as gr

def change_textbox(choice):
    if choice == "short":
        return gr.update(lines=2, visible=True, value="Short story: ")
    elif choice == "long":
        return gr.update(lines=8, visible=True, value="Long story...")
    else:
        return gr.update(visible=False)

with gr.Blocks() as demo:
    radio = gr.Radio(
        ["short", "long", "none"], label="Essay Length to Write?"
    )
    text = gr.Textbox(lines=2, interactive=True)
    radio.change(fn=change_textbox, inputs=radio, outputs=text)

if __name__ == "__main__":
    demo.launch()

update

gradio.update(kwargs, ···)

Updates component properties. When a function passed into a Gradio Interface or a Blocks events returns a typical value, it updates the value of the output component. But it is also possible to update the properties of an output component (such as the number of lines of a `Textbox` or the visibility of an `Image`) by returning the component's `update()` function, which takes as parameters any of the constructor parameters for that component. This is a shorthand for using the update method on a component. For example, rather than using gr.Number.update(...) you can just use gr.update(...). Note that your editor's autocompletion will suggest proper parameters if you use the update method on the component.

Example Usage

# Blocks Example
import gradio as gr
with gr.Blocks() as demo:
    radio = gr.Radio([1, 2, 4], label="Set the value of the number")
    number = gr.Number(value=2, interactive=True)
    radio.change(fn=lambda value: gr.update(value=value), inputs=radio, outputs=number)
demo.launch()

# Interface example
import gradio as gr
def change_textbox(choice):
  if choice == "short":
      return gr.Textbox.update(lines=2, visible=True)
  elif choice == "long":
      return gr.Textbox.update(lines=8, visible=True)
  else:
      return gr.Textbox.update(visible=False)
gr.Interface(
  change_textbox,
  gr.Radio(
      ["short", "long", "none"], label="What kind of essay would you like to write?"
  ),
  gr.Textbox(lines=2),
  live=True,
).launch()

Parameter Description


                kwargs

required

Key-word arguments used to update the component's properties.

Step-by-step Guides

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make_waveform

gradio.make_waveform(audio, ···)

Generates a waveform video from an audio file. Useful for creating an easy to share audio visualization. The output should be passed into a `gr.Video` component.

Parameter	Description
`audio` str \| tuple[int, np.ndarray] required	Audio file path or tuple of (sample_rate, audio_data)
`bg_color` str default: "#f3f4f6"	Background color of waveform (ignored if bg_image is provided)
`bg_image` str \| None default: None	Background image of waveform
`fg_alpha` float default: 0.75	Opacity of foreground waveform
`bars_color` str \| tuple[str, str] default: ('#fbbf24', '#ea580c')	Color of waveform bars. Can be a single color or a tuple of (start_color, end_color) of gradient
`bar_count` int default: 50	Number of bars in waveform
`bar_width` float default: 0.6	Width of bars in waveform. 1 represents full width, 0.5 represents half width, etc.

Step-by-step Guides

No guides yet, contribute a guide about make_waveform

import gradio as gr
import numpy as np

with gr.Blocks() as demo:
    imgs = gr.State()
    gallery = gr.Gallery()

    def generate_images():
        images = []
        for _ in range(9):
            image = np.ones((100, 100, 3), dtype=np.uint8) * np.random.randint(
                0, 255, 3
            )  # image is a solid single color
            images.append(image)
        return images, images

    demo.load(generate_images, None, [gallery, imgs])

    with gr.Row():
        selected = gr.Number(show_label=False, placeholder="Selected")
        darken_btn = gr.Button("Darken selected")

    def get_select_index(evt: gr.SelectData):
        return evt.index

    gallery.select(get_select_index, None, selected)

    def darken_img(imgs, index):
        index = int(index)
        imgs[index] = np.round(imgs[index] * 0.8).astype(np.uint8)
        return imgs, imgs

    darken_btn.click(darken_img, [imgs, selected], [imgs, gallery])

if __name__ == "__main__":
    demo.launch()

import gradio as gr

with gr.Blocks() as demo:
    turn = gr.Textbox("X", interactive=False, label="Turn")
    board = gr.Dataframe(value=[["", "", ""]] * 3, interactive=False, type="array")

    def place(board, turn, evt: gr.SelectData):
        if evt.value:
            return board, turn
        board[evt.index[0]][evt.index[1]] = turn
        turn = "O" if turn == "X" else "X"
        return board, turn

    board.select(place, [board, turn], [board, turn])

if __name__ == "__main__":
    demo.launch()

EventData

gradio.EventData(target, ···)

When a subclass of EventData is added as a type hint to an argument of an event listener method, this object will be passed as that argument. It contains information about the event that triggered the listener, such the target object, and other data related to the specific event that are attributes of the subclass.

Example Usage

table = gr.Dataframe([[1, 2, 3], [4, 5, 6]])
gallery = gr.Gallery([("cat.jpg", "Cat"), ("dog.jpg", "Dog")])
textbox = gr.Textbox("Hello World!")

statement = gr.Textbox()

def on_select(evt: gr.SelectData):  # SelectData is a subclass of EventData
    return f"You selected {evt.value} at {evt.index} from {evt.target}"

table.select(on_select, None, statement)
gallery.select(on_select, None, statement)
textbox.select(on_select, None, statement)

Parameter Description


                target

Block | None

required

The target object that triggered the event. Can be used to distinguish if multiple components are bound to the same listener.

Step-by-step Guides

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Routes

Gradio includes some helper functions for exposing and interacting with the FastAPI app used to run your demo.

Request

gradio.Request(···)

A Gradio request object that can be used to access the request headers, cookies, query parameters and other information about the request from within the prediction function. The class is a thin wrapper around the fastapi.Request class. Attributes of this class include: `headers`, `client`, `query_params`, and `path_params`. If auth is enabled, the `username` attribute can be used to get the logged in user.

Example Usage

import gradio as gr
def echo(name, request: gr.Request):
    print("Request headers dictionary:", request.headers)
    print("IP address:", request.client.host)
    return name
io = gr.Interface(echo, "textbox", "textbox").launch()

Parameter Description


                request

fastapi.Request | None

default: None

A fastapi.Request


                username

str | None

default: None

mount_gradio_app

gradio.mount_gradio_app(app, blocks, path, ···)

Mount a gradio.Blocks to an existing FastAPI application.

Example Usage

from fastapi import FastAPI
import gradio as gr
app = FastAPI()
@app.get("/")
def read_main():
    return {"message": "This is your main app"}
io = gr.Interface(lambda x: "Hello, " + x + "!", "textbox", "textbox")
app = gr.mount_gradio_app(app, io, path="/gradio")
# Then run `uvicorn run:app` from the terminal and navigate to http://localhost:8000/gradio.

Parameter	Description
`app` fastapi.FastAPI required	The parent FastAPI application.
`blocks` gradio.Blocks required	The blocks object we want to mount to the parent app.
`path` str required	The path at which the gradio application will be mounted.
`gradio_api_url` str \| None default: None	The full url at which the gradio app will run. This is only needed if deploying to Huggingface spaces of if the websocket endpoints of your deployed app are on a different network location than the gradio app. If deploying to spaces, set gradio_api_url to 'http://localhost:7860/'

Client libraries

The lightweight Gradio client libraries make it easy to use any Gradio app as an API. We currently support a Python client libraries and are developing client libraries in other languages.

Python client library

The Python client library is `gradio_client`. It is included in the latest versions of the `gradio` package, but for a more lightweight experience, you can install it using `pip` without having to install `gradio`:

pip install gradio_client

Here are the key classes and methods in the Python client library:

Client

gradio_client.Client(src, ···)

The main Client class for the Python client. This class is used to connect to a remote Gradio app and call its API endpoints.

Example Usage

from gradio_client import Client

client = Client("abidlabs/whisper-large-v2")  # connecting to a Hugging Face Space
client.predict("test.mp4", api_name="/predict")
>> What a nice recording! # returns the result of the remote API call

client = Client("https://bec81a83-5b5c-471e.gradio.live")  # connecting to a temporary Gradio share URL
job = client.submit("hello", api_name="/predict")  # runs the prediction in a background thread
job.result()
>> 49 # returns the result of the remote API call (blocking call)

Parameter	Description
`src` str required	Either the name of the Hugging Face Space to load, (e.g. "abidlabs/whisper-large-v2") or the full URL (including "http" or "https") of the hosted Gradio app to load (e.g. "http://mydomain.com/app" or "https://bec81a83-5b5c-471e.gradio.live/").
`hf_token` str \| None default: None	The Hugging Face token to use to access private Spaces. Automatically fetched if you are logged in via the Hugging Face Hub CLI. Obtain from: https://huggingface.co/settings/token
`max_workers` int default: 40	The maximum number of thread workers that can be used to make requests to the remote Gradio app simultaneously.
`serialize` bool default: True	Whether the client should serialize the inputs and deserialize the outputs of the remote API. If set to False, the client will pass the inputs and outputs as-is, without serializing/deserializing them. E.g. you if you set this to False, you'd submit an image in base64 format instead of a filepath, and you'd get back an image in base64 format from the remote API instead of a filepath.
`verbose` bool default: True	Whether the client should print statements to the console.

Methods

predict

gradio_client.Client.predict(args, ···)

Calls the Gradio API and returns the result (this is a blocking call).

Example Usage

from gradio_client import Client
client = Client(src="gradio/calculator")
client.predict(5, "add", 4, api_name="/predict")
>> 9.0

Parameter Description


                args

required

The arguments to pass to the remote API. The order of the arguments must match the order of the inputs in the Gradio app.


                api_name

str | None

default: None

The name of the API endpoint to call starting with a leading slash, e.g. "/predict". Does not need to be provided if the Gradio app has only one named API endpoint.


                fn_index

int | None

default: None

As an alternative to api_name, this parameter takes the index of the API endpoint to call, e.g. 0. Both api_name and fn_index can be provided, but if they conflict, api_name will take precedence.

submit

gradio_client.Client.submit(args, ···)

Creates and returns a Job object which calls the Gradio API in a background thread. The job can be used to retrieve the status and result of the remote API call.

Example Usage

from gradio_client import Client
client = Client(src="gradio/calculator")
job = client.submit(5, "add", 4, api_name="/predict")
job.status()
>> 
job.result()  # blocking call
>> 9.0

Parameter	Description
`args` required	The arguments to pass to the remote API. The order of the arguments must match the order of the inputs in the Gradio app.
`api_name` str \| None default: None	The name of the API endpoint to call starting with a leading slash, e.g. "/predict". Does not need to be provided if the Gradio app has only one named API endpoint.
`fn_index` int \| None default: None	As an alternative to api_name, this parameter takes the index of the API endpoint to call, e.g. 0. Both api_name and fn_index can be provided, but if they conflict, api_name will take precedence.
`result_callbacks` Callable \| list[Callable] \| None default: None	A callback function, or list of callback functions, to be called when the result is ready. If a list of functions is provided, they will be called in order. The return values from the remote API are provided as separate parameters into the callback. If None, no callback will be called.

view_api

gradio_client.Client.view_api(···)

Prints the usage info for the API. If the Gradio app has multiple API endpoints, the usage info for each endpoint will be printed separately. If return_format="dict" the info is returned in dictionary format, as shown in the example below.

Example Usage

from gradio_client import Client
client = Client(src="gradio/calculator")
client.view_api(return_format="dict")
>> {
    'named_endpoints': {
        '/predict': {
            'parameters': [
                {
                    'label': 'num1',
                    'type_python': 'int | float',
                    'type_description': 'numeric value',
                    'component': 'Number',
                    'example_input': '5'
                },
                {
                    'label': 'operation',
                    'type_python': 'str',
                    'type_description': 'string value',
                    'component': 'Radio',
                    'example_input': 'add'
                },
                {
                    'label': 'num2',
                    'type_python': 'int | float',
                    'type_description': 'numeric value',
                    'component': 'Number',
                    'example_input': '5'
                },
            ],
            'returns': [
                {
                    'label': 'output',
                    'type_python': 'int | float',
                    'type_description': 'numeric value',
                    'component': 'Number',
                },
            ]
        },
        '/flag': {
            'parameters': [
                ...
                ],
            'returns': [
                ...
                ]
            }
        }
    'unnamed_endpoints': {
        2: {
            'parameters': [
                ...
                ],
            'returns': [
                ...
                ]
            }
        }
    }
}

Parameter Description


                all_endpoints

bool | None

default: None

If True, prints information for both named and unnamed endpoints in the Gradio app. If False, will only print info about named endpoints. If None (default), will only print info about unnamed endpoints if there are no named endpoints.


                print_info

bool

default: True

If True, prints the usage info to the console. If False, does not print the usage info.


                return_format

Literal['dict', 'str'] | None

default: None

If None, nothing is returned. If "str", returns the same string that would be printed to the console. If "dict", returns the usage info as a dictionary that can be programmatically parsed, and *all endpoints are returned in the dictionary* regardless of the value of `all_endpoints`. The format of the dictionary is in the docstring of this method.

duplicate

gradio_client.Client.duplicate(from_id, ···)

Duplicates a Hugging Face Space under your account and returns a Client object for the new Space. No duplication is created if the Space already exists in your account (to override this, provide a new name for the new Space using `to_id`). To use this method, you must provide an `hf_token` or be logged in via the Hugging Face Hub CLI.
The new Space will be private by default and use the same hardware as the original Space. This can be changed by using the `private` and `hardware` parameters. For hardware upgrades (beyond the basic CPU tier), you may be required to provide billing information on Hugging Face: https://huggingface.co/settings/billing

Example Usage

import os
from gradio_client import Client
HF_TOKEN = os.environ.get("HF_TOKEN")
client = Client.duplicate("abidlabs/whisper", hf_token=HF_TOKEN)
client.predict("audio_sample.wav")
>> "This is a test of the whisper speech recognition model."

Parameter	Description
`from_id` str required	The name of the Hugging Face Space to duplicate in the format "{username}/{space_id}", e.g. "gradio/whisper".
`to_id` str \| None default: None	The name of the new Hugging Face Space to create, e.g. "abidlabs/whisper-duplicate". If not provided, the new Space will be named "{your_HF_username}/{space_id}".
`hf_token` str \| None default: None	The Hugging Face token to use to access private Spaces. Automatically fetched if you are logged in via the Hugging Face Hub CLI. Obtain from: https://huggingface.co/settings/token
`private` bool default: True	Whether the new Space should be private (True) or public (False). Defaults to True.
`hardware` str \| None default: None	The hardware tier to use for the new Space. Defaults to the same hardware tier as the original Space. Options include "cpu-basic", "cpu-upgrade", "t4-small", "t4-medium", "a10g-small", "a10g-large", "a100-large", subject to availability.
`secrets` dict[str, str] \| None default: None	A dictionary of (secret key, secret value) to pass to the new Space. Defaults to None. Secrets are only used when the Space is duplicated for the first time, and are not updated if the duplicated Space already exists.
`sleep_timeout` int default: 5	The number of minutes after which the duplicate Space will be puased if no requests are made to it (to minimize billing charges). Defaults to 5 minutes.
`max_workers` int default: 40	The maximum number of thread workers that can be used to make requests to the remote Gradio app simultaneously.
`verbose` bool default: True	Whether the client should print statements to the console.

Job

gradio_client.Job(future, ···)

A Job is a wrapper over the Future class that represents a prediction call that has been submitted by the Gradio client. This class is not meant to be instantiated directly, but rather is created by the Client.submit() method.
A Job object includes methods to get the status of the prediction call, as well to get the outputs of the prediction call. Job objects are also iterable, and can be used in a loop to get the outputs of prediction calls as they become available for generator endpoints.

Parameter	Description
`future` Future required	The future object that represents the prediction call, created by the Client.submit() method
`communicator` Communicator \| None default: None	The communicator object that is used to communicate between the client and the background thread running the job
`verbose` bool default: True	Whether to print any status-related messages to the console
`space_id` str \| None default: None	The space ID corresponding to the Client object that created this Job object

Methods

result

gradio_client.Job.result(···)

Return the result of the call that the future represents. Raises CancelledError: If the future was cancelled, TimeoutError: If the future didn't finish executing before the given timeout, and Exception: If the call raised then that exception will be raised.

Example Usage

from gradio_client import Client
calculator = Client(src="gradio/calculator")
job = calculator.submit("foo", "add", 4, fn_index=0)
job.result(timeout=5)
>> 9

Parameter Description


                timeout

default: None

The number of seconds to wait for the result if the future isn't done. If None, then there is no limit on the wait time.

outputs

gradio_client.Job.outputs(···)

Returns a list containing the latest outputs from the Job.
If the endpoint has multiple output components, the list will contain a tuple of results. Otherwise, it will contain the results without storing them in tuples.
For endpoints that are queued, this list will contain the final job output even if that endpoint does not use a generator function.

Example Usage

from gradio_client import Client
client = Client(src="gradio/count_generator")
job = client.submit(3, api_name="/count")
while not job.done():
    time.sleep(0.1)
job.outputs()
>> ['0', '1', '2']

status

gradio_client.Job.status(···)

Returns the latest status update from the Job in the form of a StatusUpdate object, which contains the following fields: code, rank, queue_size, success, time, eta, and progress_data.
progress_data is a list of updates emitted by the gr.Progress() tracker of the event handler. Each element of the list has the following fields: index, length, unit, progress, desc. If the event handler does not have a gr.Progress() tracker, the progress_data field will be None.

Example Usage

from gradio_client import Client
client = Client(src="gradio/calculator")
job = client.submit(5, "add", 4, api_name="/predict")
job.status()
>> 
job.status().eta
>> 43.241  # seconds