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`gradio-rs` is a Gradio Client in Rust built by
[@JacobLinCool](https://github.com/JacobLinCool). You can find the repo
[here](https://github.com/JacobLinCool/gradio-rs), and more in depth API
documentation [here](https://docs.rs/gradio/latest/gradio/).
| Introduction | https://gradio.app/docs/third-party-clients/rust-client | Third Party Clients - Rust Client Docs |
Here is an example of using BS-RoFormer model to separate vocals and
background music from an audio file.
use gradio::{PredictionInput, Client, ClientOptions};
[tokio::main]
async fn main() {
if std::env::args().len() < 2 {
println!("Please provide an audio file path as an argument");
std::process::exit(1);
}
let args: Vec<String> = std::env::args().collect();
let file_path = &args[1];
println!("File: {}", file_path);
let client = Client::new("JacobLinCool/vocal-separation", ClientOptions::default())
.await
.unwrap();
let output = client
.predict(
"/separate",
vec![
PredictionInput::from_file(file_path),
PredictionInput::from_value("BS-RoFormer"),
],
)
.await
.unwrap();
println!(
"Vocals: {}",
output[0].clone().as_file().unwrap().url.unwrap()
);
println!(
"Background: {}",
output[1].clone().as_file().unwrap().url.unwrap()
);
}
You can find more examples [here](https://github.com/JacobLinCool/gradio-
rs/tree/main/examples).
| Usage | https://gradio.app/docs/third-party-clients/rust-client | Third Party Clients - Rust Client Docs |
cargo install gradio
gr --help
Take [stabilityai/stable-
diffusion-3-medium](https://huggingface.co/spaces/stabilityai/stable-
diffusion-3-medium) HF Space as an example:
> gr list stabilityai/stable-diffusion-3-medium
API Spec for stabilityai/stable-diffusion-3-medium:
/infer
Parameters:
prompt ( str )
negative_prompt ( str )
seed ( float ) numeric value between 0 and 2147483647
randomize_seed ( bool )
width ( float ) numeric value between 256 and 1344
height ( float ) numeric value between 256 and 1344
guidance_scale ( float ) numeric value between 0.0 and 10.0
num_inference_steps ( float ) numeric value between 1 and 50
Returns:
Result ( filepath )
Seed ( float ) numeric value between 0 and 2147483647
> gr run stabilityai/stable-diffusion-3-medium infer 'Rusty text "AI & CLI" on the snow.' '' 0 true 1024 1024 5 28
Result: https://stabilityai-stable-diffusion-3-medium.hf.space/file=/tmp/gradio/5735ca7775e05f8d56d929d8f57b099a675c0a01/image.webp
Seed: 486085626
For file input, simply use the file path as the argument:
gr run hf-audio/whisper-large-v3 predict 'test-audio.wav' 'transcribe'
output: " Did you know you can try the coolest model on your command line?"
| Command Line Interface | https://gradio.app/docs/third-party-clients/rust-client | Third Party Clients - Rust Client Docs |
Gradio applications support programmatic requests from many environments:
* The [Python Client](/docs/python-client): `gradio-client` allows you to make requests from Python environments.
* The [JavaScript Client](/docs/js-client): `@gradio/client` allows you to make requests in TypeScript from the browser or server-side.
* You can also query gradio apps [directly from cURL](/guides/querying-gradio-apps-with-curl).
| Gradio Clients | https://gradio.app/docs/third-party-clients/introduction | Third Party Clients - Introduction Docs |
We also encourage the development and use of third party clients built by
the community:
* [Rust Client](/docs/third-party-clients/rust-client): `gradio-rs` built by [@JacobLinCool](https://github.com/JacobLinCool) allows you to make requests in Rust.
* [Powershell Client](https://github.com/rrg92/powershai): `powershai` built by [@rrg92](https://github.com/rrg92) allows you to make requests to Gradio apps directly from Powershell. See [here for documentation](https://github.com/rrg92/powershai/blob/main/docs/en-US/providers/HUGGING-FACE.md)
| Community Clients | https://gradio.app/docs/third-party-clients/introduction | Third Party Clients - Introduction Docs |
The main Client class for the Python client. This class is used to connect
to a remote Gradio app and call its API endpoints.
| Description | https://gradio.app/docs/python-client/client | Python Client - Client Docs |
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)
| Example usage | https://gradio.app/docs/python-client/client | Python Client - Client Docs |
Parameters ▼
src: str
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 | Literal[False] | None
default `= False`
optional Hugging Face token to use to access private Spaces. By default, no
token is sent to the server. Set `hf_token=None` to use the locally saved
token if there is one (warning: only provide a token if you are loading a
trusted private Space as the token can be read by the Space you are loading).
Find your tokens here: https://huggingface.co/settings/tokens.
max_workers: int
default `= 40`
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.
auth: tuple[str, str] | None
default `= None`
httpx_kwargs: dict[str, Any] | None
default `= None`
additional keyword arguments to pass to `httpx.Client`, `httpx.stream`,
`httpx.get` and `httpx.post`. This can be used to set timeouts, proxies, http
auth, etc.
headers: dict[str, str] | None
default `= None`
additional headers to send to the remote Gradio app on every request. By
default only the HF authorization and user-agent headers are sent. This
parameter will override the default headers if they have the same keys.
download_files: str | Path | Literal[False]
default `= "/tmp/gradio"`
directory where the client should download output files on the local machine
from the remote API. By default, uses the value of the GRADIO_TEMP_DIR
environment variable which, if not set by the user, is a temporary directory
on your machine. If False, the client does not download files and returns a
FileData dataclass object with the filepath | Initialization | https://gradio.app/docs/python-client/client | Python Client - Client Docs |
IR
environment variable which, if not set by the user, is a temporary directory
on your machine. If False, the client does not download files and returns a
FileData dataclass object with the filepath on the remote machine instead.
ssl_verify: bool
default `= True`
if False, skips certificate validation which allows the client to connect to
Gradio apps that are using self-signed certificates.
analytics_enabled: bool
default `= True`
Whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED
environment variable or default to True.
| Initialization | https://gradio.app/docs/python-client/client | Python Client - Client Docs |
Description
Event listeners allow you to respond to user interactions with the UI
components you've defined in a Gradio Blocks app. When a user interacts with
an element, such as changing a slider value or uploading an image, a function
is called.
Supported Event Listeners
The Client component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listener | Description
---|---
`Client.predict(fn, ···)` | Calls the Gradio API and returns the result (this is a blocking call). Arguments can be provided as positional arguments or as keyword arguments (latter is recommended). <br>
`Client.submit(fn, ···)` | 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. Arguments can be provided as positional arguments or as keyword arguments (latter is recommended). <br>
`Client.view_api(fn, ···)` | 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. <br>
`Client.duplicate(fn, ···)` | 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. <br> 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 <br>
`Client.deploy_discord(fn, ···)` | Deploy | Event Listeners | https://gradio.app/docs/python-client/client | Python Client - Client Docs |
dware upgrades (beyond the basic CPU tier), you may be required to provide billing information on Hugging Face: https://huggingface.co/settings/billing <br>
`Client.deploy_discord(fn, ···)` | Deploy the upstream app as a discord bot. Currently only supports gr.ChatInterface.
Event Parameters
Parameters ▼
args: <class 'inspect._empty'>
The positional arguments to pass to the remote API endpoint. 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.
kwargs: <class 'inspect._empty'>
The keyword arguments to pass to the remote API endpoint.
| Event Listeners | https://gradio.app/docs/python-client/client | Python Client - Client Docs |
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.
| Description | https://gradio.app/docs/python-client/job | Python Client - Job Docs |
Parameters ▼
future: Future
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
| Initialization | https://gradio.app/docs/python-client/job | Python Client - Job Docs |
Description
Event listeners allow you to respond to user interactions with the UI
components you've defined in a Gradio Blocks app. When a user interacts with
an element, such as changing a slider value or uploading an image, a function
is called.
Supported Event Listeners
The Job component supports the following event listeners. Each event listener
takes the same parameters, which are listed in the Event Parameters table
below.
Listener | Description
---|---
`Job.result(fn, ···)` | 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. <br>
`Job.outputs(fn, ···)` | Returns a list containing the latest outputs from the Job. <br> 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. <br> For endpoints that are queued, this list will contain the final job output even if that endpoint does not use a generator function. <br>
`Job.status(fn, ···)` | 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. <br> 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. <br>
Event Parameters
Parameters ▼
timeout: float | None
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.
| Event Listeners | https://gradio.app/docs/python-client/job | Python Client - Job Docs |
**Stream From a Gradio app in 5 lines**
Use the `submit` method to get a job you can iterate over.
In python:
from gradio_client import Client
client = Client("gradio/llm_stream")
for result in client.submit("What's the best UI framework in Python?"):
print(result)
In typescript:
import { Client } from "@gradio/client";
const client = await Client.connect("gradio/llm_stream")
const job = client.submit("/predict", {"text": "What's the best UI framework in Python?"})
for await (const msg of job) console.log(msg.data)
**Use the same keyword arguments as the app**
In the examples below, the upstream app has a function with parameters called
`message`, `system_prompt`, and `tokens`. We can see that the client `predict`
call uses the same arguments.
In python:
from gradio_client import Client
client = Client("http://127.0.0.1:7860/")
result = client.predict(
message="Hello!!",
system_prompt="You are helpful AI.",
tokens=10,
api_name="/chat"
)
print(result)
In typescript:
import { Client } from "@gradio/client";
const client = await Client.connect("http://127.0.0.1:7860/");
const result = await client.predict("/chat", {
message: "Hello!!",
system_prompt: "Hello!!",
tokens: 10,
});
console.log(result.data);
**Better Error Messages**
If something goes wrong in the upstream app, the client will raise the same
exception as the app provided that `show_error=True` in the original app's
`launch()` function, or it's a `gr.Error` exception.
| Ergonomic API 💆 | https://gradio.app/docs/python-client/version-1-release | Python Client - Version 1 Release Docs |
Anything you can do in the UI, you can do with the client:
* 🔐Authentication
* 🛑 Job Cancelling
* ℹ️ Access Queue Position and API
* 📕 View the API information
Here's an example showing how to display the queue position of a pending job:
from gradio_client import Client
client = Client("gradio/diffusion_model")
job = client.submit("A cute cat")
while not job.done():
status = job.status()
print(f"Current in position {status.rank} out of {status.queue_size}")
| Transparent Design 🪟 | https://gradio.app/docs/python-client/version-1-release | Python Client - Version 1 Release Docs |
The client can run from pretty much any python and javascript environment
(node, deno, the browser, Service Workers).
Here's an example using the client from a Flask server using gevent:
from gevent import monkey
monkey.patch_all()
from gradio_client import Client
from flask import Flask, send_file
import time
app = Flask(__name__)
imageclient = Client("gradio/diffusion_model")
@app.route("/gen")
def gen():
result = imageclient.predict(
"A cute cat",
api_name="/predict"
)
return send_file(result)
if __name__ == "__main__":
app.run(host="0.0.0.0", port=5000)
| Portable Design ⛺️ | https://gradio.app/docs/python-client/version-1-release | Python Client - Version 1 Release Docs |
Changes
**Python**
* The `serialize` argument of the `Client` class was removed and has no effect.
* The `upload_files` argument of the `Client` was removed.
* All filepaths must be wrapped in the `handle_file` method. For example, `caption = client.predict(handle_file('./dog.jpg'))`.
* The `output_dir` argument was removed. It is not specified in the `download_files` argument.
**Javascript**
The client has been redesigned entirely. It was refactored from a function
into a class. An instance can now be constructed by awaiting the `connect`
method.
const app = await Client.connect("gradio/whisper")
The app variable has the same methods as the python class (`submit`,
`predict`, `view_api`, `duplicate`).
| v1.0 Migration Guide and Breaking | https://gradio.app/docs/python-client/version-1-release | Python Client - Version 1 Release Docs |
ZeroGPU
ZeroGPU spaces are rate-limited to ensure that a single user does not hog all
of the available GPUs. The limit is controlled by a special token that the
Hugging Face Hub infrastructure adds to all incoming requests to Spaces. This
token is a request header called `X-IP-Token` and its value changes depending
on the user who makes a request to the ZeroGPU space.
Let’s say you want to create a space (Space A) that uses a ZeroGPU space
(Space B) programmatically. Simply calling Space B from Space A with the
python client will quickly exhaust your rate limit, as all the requests to the
ZeroGPU space will have the same token. So in order to avoid this, we need to
extract the token of the user using Space A before we call Space B
programmatically.
How to do this will be explained in the following section.
| Explaining Rate Limits for | https://gradio.app/docs/python-client/using-zero-gpu-spaces | Python Client - Using Zero Gpu Spaces Docs |
When a user presses enter in the textbox, we will extract their token from the
`X-IP-Token` header of the incoming request. We will use this header when
constructing the gradio client. The following hypothetical text-to-image
application shows how this is done.
import gradio as gr
from gradio_client import Client
def text_to_image(prompt, request: gr.Request):
x_ip_token = request.headers['x-ip-token']
client = Client("hysts/SDXL", headers={"x-ip-token": x_ip_token})
img = client.predict(prompt, api_name="/predict")
return img
with gr.Blocks() as demo:
image = gr.Image()
prompt = gr.Textbox(max_lines=1)
prompt.submit(text_to_image, [prompt], [image])
demo.launch()
| Avoiding Rate Limits | https://gradio.app/docs/python-client/using-zero-gpu-spaces | Python Client - Using Zero Gpu Spaces Docs |
If you already have a recent version of `gradio`, then the `gradio_client` is
included as a dependency. But note that this documentation reflects the latest
version of the `gradio_client`, so upgrade if you’re not sure!
The lightweight `gradio_client` package can be installed from pip (or pip3)
and is tested to work with **Python versions 3.9 or higher** :
$ pip install --upgrade gradio_client
| Installation | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
Spaces
Start by connecting instantiating a `Client` object and connecting it to a
Gradio app that is running on Hugging Face Spaces.
from gradio_client import Client
client = Client("abidlabs/en2fr") a Space that translates from English to French
You can also connect to private Spaces by passing in your HF token with the
`hf_token` parameter. You can get your HF token here:
<https://huggingface.co/settings/tokens>
from gradio_client import Client
client = Client("abidlabs/my-private-space", hf_token="...")
| Connecting to a Gradio App on Hugging Face | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
use
While you can use any public Space as an API, you may get rate limited by
Hugging Face if you make too many requests. For unlimited usage of a Space,
simply duplicate the Space to create a private Space, and then use it to make
as many requests as you’d like!
The `gradio_client` includes a class method: `Client.duplicate()` to make this
process simple (you’ll need to pass in your [Hugging Face
token](https://huggingface.co/settings/tokens) or be logged in using the
Hugging Face CLI):
import os
from gradio_client import Client, file
HF_TOKEN = os.environ.get("HF_TOKEN")
client = Client.duplicate("abidlabs/whisper", hf_token=HF_TOKEN)
client.predict(file("audio_sample.wav"))
>> "This is a test of the whisper speech recognition model."
If you have previously duplicated a Space, re-running `duplicate()` will _not_
create a new Space. Instead, the Client will attach to the previously-created
Space. So it is safe to re-run the `Client.duplicate()` method multiple times.
**Note:** if the original Space uses GPUs, your private Space will as well,
and your Hugging Face account will get billed based on the price of the GPU.
To minimize charges, your Space will automatically go to sleep after 1 hour of
inactivity. You can also set the hardware using the `hardware` parameter of
`duplicate()`.
| Duplicating a Space for private | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
app
If your app is running somewhere else, just provide the full URL instead,
including the “http://” or “https://“. Here’s an example of making predictions
to a Gradio app that is running on a share URL:
from gradio_client import Client
client = Client("https://bec81a83-5b5c-471e.gradio.live")
| Connecting a general Gradio | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
Once you have connected to a Gradio app, you can view the APIs that are
available to you by calling the `Client.view_api()` method. For the Whisper
Space, we see the following:
Client.predict() Usage Info
---------------------------
Named API endpoints: 1
- predict(audio, api_name="/predict") -> output
Parameters:
- [Audio] audio: filepath (required)
Returns:
- [Textbox] output: str
We see that we have 1 API endpoint in this space, and shows us how to use the
API endpoint to make a prediction: we should call the `.predict()` method
(which we will explore below), providing a parameter `input_audio` of type
`str`, which is a `filepath or URL`.
We should also provide the `api_name='/predict'` argument to the `predict()`
method. Although this isn’t necessary if a Gradio app has only 1 named
endpoint, it does allow us to call different endpoints in a single app if they
are available.
| Inspecting the API endpoints | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
As an alternative to running the `.view_api()` method, you can click on the
“Use via API” link in the footer of the Gradio app, which shows us the same
information, along with example usage.

The View API page also includes an “API Recorder” that lets you interact with
the Gradio UI normally and converts your interactions into the corresponding
code to run with the Python Client.
| The “View API” Page | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
The simplest way to make a prediction is simply to call the `.predict()`
function with the appropriate arguments:
from gradio_client import Client
client = Client("abidlabs/en2fr", api_name='/predict')
client.predict("Hello")
>> Bonjour
If there are multiple parameters, then you should pass them as separate
arguments to `.predict()`, like this:
from gradio_client import Client
client = Client("gradio/calculator")
client.predict(4, "add", 5)
>> 9.0
It is recommended to provide key-word arguments instead of positional
arguments:
from gradio_client import Client
client = Client("gradio/calculator")
client.predict(num1=4, operation="add", num2=5)
>> 9.0
This allows you to take advantage of default arguments. For example, this
Space includes the default value for the Slider component so you do not need
to provide it when accessing it with the client.
from gradio_client import Client
client = Client("abidlabs/image_generator")
client.predict(text="an astronaut riding a camel")
The default value is the initial value of the corresponding Gradio component.
If the component does not have an initial value, but if the corresponding
argument in the predict function has a default value of `None`, then that
parameter is also optional in the client. Of course, if you’d like to override
it, you can include it as well:
from gradio_client import Client
client = Client("abidlabs/image_generator")
client.predict(text="an astronaut riding a camel", steps=25)
For providing files or URLs as inputs, you should pass in the filepath or URL
to the file enclosed within `gradio_client.file()`. This takes care of
uploading the file to the Gradio server and ensures that the file is
preprocessed correctly:
from gradio_client import Client, file
client = Client("abidlabs/whisper")
client.predict(
| Making a prediction | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
to the Gradio server and ensures that the file is
preprocessed correctly:
from gradio_client import Client, file
client = Client("abidlabs/whisper")
client.predict(
audio=file("https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3")
)
>> "My thought I have nobody by a beauty and will as you poured. Mr. Rochester is serve in that so don't find simpus, and devoted abode, to at might in a r—"
| Making a prediction | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
Oe should note that `.predict()` is a _blocking_ operation as it waits for the
operation to complete before returning the prediction.
In many cases, you may be better off letting the job run in the background
until you need the results of the prediction. You can do this by creating a
`Job` instance using the `.submit()` method, and then later calling
`.result()` on the job to get the result. For example:
from gradio_client import Client
client = Client(space="abidlabs/en2fr")
job = client.submit("Hello", api_name="/predict") This is not blocking
Do something else
job.result() This is blocking
>> Bonjour
| Running jobs asynchronously | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
Alternatively, one can add one or more callbacks to perform actions after the
job has completed running, like this:
from gradio_client import Client
def print_result(x):
print("The translated result is: {x}")
client = Client(space="abidlabs/en2fr")
job = client.submit("Hello", api_name="/predict", result_callbacks=[print_result])
Do something else
>> The translated result is: Bonjour
| Adding callbacks | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
The `Job` object also allows you to get the status of the running job by
calling the `.status()` method. This returns a `StatusUpdate` object with the
following attributes: `code` (the status code, one of a set of defined strings
representing the status. See the `utils.Status` class), `rank` (the current
position of this job in the queue), `queue_size` (the total queue size), `eta`
(estimated time this job will complete), `success` (a boolean representing
whether the job completed successfully), and `time` (the time that the status
was generated).
from gradio_client import Client
client = Client(src="gradio/calculator")
job = client.submit(5, "add", 4, api_name="/predict")
job.status()
>> <Status.STARTING: 'STARTING'>
_Note_ : The `Job` class also has a `.done()` instance method which returns a
boolean indicating whether the job has completed.
| Status | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
The `Job` class also has a `.cancel()` instance method that cancels jobs that
have been queued but not started. For example, if you run:
client = Client("abidlabs/whisper")
job1 = client.submit(file("audio_sample1.wav"))
job2 = client.submit(file("audio_sample2.wav"))
job1.cancel() will return False, assuming the job has started
job2.cancel() will return True, indicating that the job has been canceled
If the first job has started processing, then it will not be canceled. If the
second job has not yet started, it will be successfully canceled and removed
from the queue.
| Cancelling Jobs | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
Some Gradio API endpoints do not return a single value, rather they return a
series of values. You can get the series of values that have been returned at
any time from such a generator endpoint by running `job.outputs()`:
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']
Note that running `job.result()` on a generator endpoint only gives you the
_first_ value returned by the endpoint.
The `Job` object is also iterable, which means you can use it to display the
results of a generator function as they are returned from the endpoint. Here’s
the equivalent example using the `Job` as a generator:
from gradio_client import Client
client = Client(src="gradio/count_generator")
job = client.submit(3, api_name="/count")
for o in job:
print(o)
>> 0
>> 1
>> 2
You can also cancel jobs that that have iterative outputs, in which case the
job will finish as soon as the current iteration finishes running.
from gradio_client import Client
import time
client = Client("abidlabs/test-yield")
job = client.submit("abcdef")
time.sleep(3)
job.cancel() job cancels after 2 iterations
| Generator Endpoints | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
Gradio demos can include [session state](https://www.gradio.app/guides/state-
in-blocks), which provides a way for demos to persist information from user
interactions within a page session.
For example, consider the following demo, which maintains a list of words that
a user has submitted in a `gr.State` component. When a user submits a new
word, it is added to the state, and the number of previous occurrences of that
word is displayed:
import gradio as gr
def count(word, list_of_words):
return list_of_words.count(word), list_of_words + [word]
with gr.Blocks() as demo:
words = gr.State([])
textbox = gr.Textbox()
number = gr.Number()
textbox.submit(count, inputs=[textbox, words], outputs=[number, words])
demo.launch()
If you were to connect this this Gradio app using the Python Client, you would
notice that the API information only shows a single input and output:
Client.predict() Usage Info
---------------------------
Named API endpoints: 1
- predict(word, api_name="/count") -> value_31
Parameters:
- [Textbox] word: str (required)
Returns:
- [Number] value_31: float
That is because the Python client handles state automatically for you — as you
make a series of requests, the returned state from one request is stored
internally and automatically supplied for the subsequent request. If you’d
like to reset the state, you can do that by calling `Client.reset_session()`.
| Demos with Session State | https://gradio.app/docs/python-client/introduction | Python Client - Introduction Docs |
A TabbedInterface is created by providing a list of Interfaces or Blocks,
each of which gets rendered in a separate tab. Only the components from the
Interface/Blocks will be rendered in the tab. Certain high-level attributes of
the Blocks (e.g. custom `css`, `js`, and `head` attributes) will not be
loaded.
| Description | https://gradio.app/docs/gradio/tabbedinterface | Gradio - Tabbedinterface Docs |
Parameters ▼
interface_list: list[Blocks]
A list of Interfaces (or Blocks) to be rendered in the 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`
The tab title to display when this demo is opened in a browser window.
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 Hugging Face 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.
css: str | None
default `= None`
Custom css as a string or path to a css file. This css will be included in the
demo webpage.
js: str | Literal[True] | None
default `= None`
Custom js as a string or path to a js file. The custom js should in the form
of a single js function. This function will automatically be executed when the
page loads. For more flexibility, use the head parameter to insert js inside
<script> tags.
head: str | None
default `= None`
Custom html to insert into the head of the demo webpage. This can be used to
add custom meta tags, multiple scripts, stylesheets, etc. to the page.
| Initialization | https://gradio.app/docs/gradio/tabbedinterface | Gradio - Tabbedinterface Docs |
tabbed_interface_lite
Open in 🎢 ↗ import gradio as gr hello_world = gr.Interface(lambda name: "Hello
" + name, "text", "text") bye_world = gr.Interface(lambda name: "Bye " + name,
"text", "text") chat = gr.ChatInterface(lambda *args: "Hello " + args[0]) demo
= gr.TabbedInterface([hello_world, bye_world, chat], ["Hello World", "Bye
World", "Chat"]) if __name__ == "__main__": demo.launch()
import gradio as gr
hello_world = gr.Interface(lambda name: "Hello " + name, "text", "text")
bye_world = gr.Interface(lambda name: "Bye " + name, "text", "text")
chat = gr.ChatInterface(lambda *args: "Hello " + args[0])
demo = gr.TabbedInterface([hello_world, bye_world, chat], ["Hello World", "Bye World", "Chat"])
if __name__ == "__main__":
demo.launch()
| Demos | https://gradio.app/docs/gradio/tabbedinterface | Gradio - Tabbedinterface Docs |
Creates a gallery or table to display data samples. This component is
primarily designed for internal use to display examples. However, it can also
be used directly to display a dataset and let users select examples.
| Description | https://gradio.app/docs/gradio/dataset | Gradio - Dataset Docs |
**As input component** : Passes the selected sample either as a `list` of
data corresponding to each input component (if `type` is "value") or as an
`int` index (if `type` is "index"), or as a `tuple` of the index and the data
(if `type` is "tuple").
Your function should accept one of these types:
def predict(
value: int | list | None
)
...
**As output component** : Expects an `int` index or `list` of sample data.
Returns the index of the sample in the dataset or `None` if the sample is not
found.
Your function should return one of these types:
def predict(···) -> list[list]
...
return value
| Behavior | https://gradio.app/docs/gradio/dataset | Gradio - Dataset Docs |
Parameters ▼
label: str | I18nData | None
default `= None`
the label for this component, appears above the component.
show_label: bool
default `= True`
If True, the label will be shown above the component.
components: list[Component] | list[str] | None
default `= None`
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
component_props: list[dict[str, Any]] | None
default `= None`
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: Literal['values', 'index', 'tuple']
default `= "values"`
"values" if clicking on a sample should pass the value of the sample, "index"
if it should pass the index of the sample, or "tuple" if it should pass both
the index and the value of the sample.
layout: Literal['gallery', 'table'] | None
default `= None`
"gallery" if the dataset should be displayed as a gallery with each sample in
a clickable card, or "table" if it should be displayed as a table with each
sample in a row. By default, "gallery" is used if there is a single component,
and "table" is used if there are more than one component. If there are more
than one component, the layout can only be "table".
samples_per_page: int
default `= 10`
how many examples to show per page.
visible: bool
default `= True`
| Initialization | https://gradio.app/docs/gradio/dataset | Gradio - Dataset Docs |
re more
than one component, the layout can only be "table".
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.
render: bool
default `= True`
If False, component will not render be rendered in the Blocks context. Should
be used if the intention is to assign event listeners now but render the
component later.
key: int | str | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
container: bool
default `= True`
If True, will place the component in a container - providing some extra
padding around the border.
scale: int | None
default `= None`
relative size compared to adjacent Components. For example if Components A and
B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide
as B. Should be an integer. scale applies in Rows, and to top-level Components
in Blocks where fill_height=True.
| Initialization | https://gradio.app/docs/gradio/dataset | Gradio - Dataset Docs |
in a Row, and A has scale=2, and B has scale=1, A will be twice as wide
as B. Should be an integer. scale applies in Rows, and to top-level Components
in Blocks where fill_height=True.
min_width: int
default `= 160`
minimum pixel width, will wrap if not sufficient screen space to satisfy this
value. If a certain scale value results in this Component being narrower than
min_width, the min_width parameter will be respected first.
proxy_url: str | None
default `= None`
The URL of the external Space used to load this component. Set automatically
when using `gr.load()`. This should not be set manually.
sample_labels: list[str] | None
default `= None`
A list of labels for each sample. If provided, the length of this list should
be the same as the number of samples, and these labels will be used in the UI
instead of rendering the sample values.
| Initialization | https://gradio.app/docs/gradio/dataset | Gradio - Dataset Docs |
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.Dataset` | "dataset" | Uses default values
| Shortcuts | https://gradio.app/docs/gradio/dataset | Gradio - Dataset Docs |
**Updating a Dataset**
In this example, we display a text dataset using `gr.Dataset` and then update
it when the user clicks a button:
import gradio as gr
philosophy_quotes = [
["I think therefore I am."],
["The unexamined life is not worth living."]
]
startup_quotes = [
["Ideas are easy. Implementation is hard"],
["Make mistakes faster."]
]
def show_startup_quotes():
return gr.Dataset(samples=startup_quotes)
with gr.Blocks() as demo:
textbox = gr.Textbox()
dataset = gr.Dataset(components=[textbox], samples=philosophy_quotes)
button = gr.Button()
button.click(show_startup_quotes, None, dataset)
demo.launch()
| Examples | https://gradio.app/docs/gradio/dataset | Gradio - Dataset Docs |
Description
Event listeners allow you to respond to user interactions with the UI
components you've defined in a Gradio Blocks app. When a user interacts with
an element, such as changing a slider value or uploading an image, a function
is called.
Supported Event Listeners
The Dataset component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listener | Description
---|---
`Dataset.click(fn, ···)` | Triggered when the Dataset is clicked.
`Dataset.select(fn, ···)` | Event listener for when the user selects or deselects the Dataset. Uses event data gradio.SelectData to carry `value` referring to the label of the Dataset, and `selected` to refer to state of the Dataset. See EventData documentation on how to use this event data
Event Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to call when this event is triggered. 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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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 | Literal[False]
default `= None`
defines how the endpoint appears in the API docs. Can be a string, None, or
False. If set to a string, the endpoint will be exposed in the API doc | Event Listeners | https://gradio.app/docs/gradio/dataset | Gradio - Dataset Docs |
_name: str | None | Literal[False]
default `= None`
defines how the endpoint appears in the API docs. Can be a string, None, or
False. If set to a string, the endpoint will be exposed in the API docs with
the given name. If None (default), the name of the function will be used as
the API endpoint. If False, the endpoint will not be exposed in the API docs
and downstream apps (including those that `gr.load` this app) will not be able
to use this event.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
default `= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
queue: bool
default `= True`
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=Tru | Event Listeners | https://gradio.app/docs/gradio/dataset | Gradio - Dataset Docs |
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 listener 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.
trigger_mode: Literal['once', 'multiple', 'always_last'] | None
default `= None`
If "once" (default for all events except `.change()`) would not allow any
submissions while an event is pending. If set to "multiple", unlimited
submissions are allowed while pending, and "always_last" (default for
`.change()` and `.key_up()` events) would allow a second submission after the
pending event is complete.
js: str | Literal[True] | None
default `= None`
Optional frontend js method to run before running 'fn'. Input arguments for js
method are values of 'inputs' and 'outputs', return should be a list of values
for output components.
concurrency_limit: int | None | Literal['default']
default `= "default"`
If set, this is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any number of
this event can be running simultaneously). Set to "default" to us | Event Listeners | https://gradio.app/docs/gradio/dataset | Gradio - Dataset Docs |
is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any number of
this event can be running simultaneously). Set to "default" to use the default
concurrency limit (defined by the `default_concurrency_limit` parameter in
`Blocks.queue()`, which itself is 1 by default).
concurrency_id: str | None
default `= None`
If set, this is the id of the concurrency group. Events with the same
concurrency_id will be limited by the lowest set concurrency_limit.
show_api: bool
default `= True`
whether to show this event in the "view API" page of the Gradio app, or in the
".view_api()" method of the Gradio clients. Unlike setting api_name to False,
setting show_api to False will still allow downstream apps as well as the
Clients to use this event. If fn is None, show_api will automatically be set
to False.
time_limit: int | None
default `= None`
stream_every: float
default `= 0.5`
like_user_message: bool
default `= False`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
| Event Listeners | https://gradio.app/docs/gradio/dataset | Gradio - Dataset Docs |
The gr.DownloadData class is a subclass of gr.EventData that specifically
carries information about the `.download()` event. When gr.DownloadData is
added as a type hint to an argument of an event listener method, a
gr.DownloadData object will automatically be passed as the value of that
argument. The attributes of this object contains information about the event
that triggered the listener.
| Description | https://gradio.app/docs/gradio/downloaddata | Gradio - Downloaddata Docs |
import gradio as gr
def on_download(download_data: gr.DownloadData):
return f"Downloaded file: {download_data.file.path}"
with gr.Blocks() as demo:
files = gr.File()
textbox = gr.Textbox()
files.download(on_download, None, textbox)
demo.launch()
| Example Usage | https://gradio.app/docs/gradio/downloaddata | Gradio - Downloaddata Docs |
Parameters ▼
file: FileData
The file that was downloaded, as a FileData object.
| Attributes | https://gradio.app/docs/gradio/downloaddata | Gradio - Downloaddata Docs |
Creates a textarea for user to enter string input or display string output.
| Description | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
**As input component** : Passes text value as a `str` into the function.
Your function should accept one of these types:
def predict(
value: str | None
)
...
**As output component** : Expects a `str` returned from function and sets
textarea value to it.
Your function should return one of these types:
def predict(···) -> str | None
...
return value
| Behavior | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
Parameters ▼
value: str | I18nData | Callable | None
default `= None`
text to show in textbox. If a function is provided, the function will be
called each time the app loads to set the initial value of this component.
type: Literal['text', 'password', 'email']
default `= "text"`
The type of textbox. One of: 'text' (which allows users to enter any text),
'password' (which masks text entered by the user), 'email' (which suggests
email input to the browser). For "password" and "email" types, `lines` must be
1 and `max_lines` must be None or 1.
lines: int
default `= 1`
minimum number of line rows to provide in textarea.
max_lines: int | None
default `= None`
maximum number of line rows to provide in textarea. Must be at least `lines`.
If not provided, the maximum number of lines is max(lines, 20) for "text"
type, and 1 for "password" and "email" types.
placeholder: str | I18nData | None
default `= None`
placeholder hint to provide behind textarea.
label: str | I18nData | None
default `= None`
the label for this component, displayed above the component if `show_label` is
`True` and is also used as the header if there are a table of examples for
this component. If None and used in a `gr.Interface`, the label will be the
name of the parameter this component corresponds to.
info: str | I18nData | None
default `= None`
additional component description, appears below the label in smaller font.
Supports markdown / HTML syntax.
every: Timer | float | None
default `= None`
continously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
components that are used as inputs to calculate `value` if `value` | Initialization | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
show_label: bool | None
default `= None`
if True, will display the label. If False, the copy button is hidden as well
as well as the label.
container: bool
default `= True`
if True, will place the component in a container - providing some extra
padding around the border.
scale: int | None
default `= None`
relative size compared to adjacent Components. For example if Components A and
B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide
as B. Should be an integer. scale applies in Rows, and to top-level Components
in Blocks where fill_height=True.
min_width: int
default `= 160`
minimum pixel width, will wrap if not sufficient screen space to satisfy this
value. If a certain scale value results in this Component being narrower than
min_width, the min_width parameter will be respected first.
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.
autofocus: bool
default `= False`
If True, will focus on the textbox when the page loads. Use this carefully, as
it can cause usability issues for sighted and non-sighted users.
autoscroll: bool
default `= True`
If True, will automatically scroll to the bottom of the textb | Initialization | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
se this carefully, as
it can cause usability issues for sighted and non-sighted users.
autoscroll: bool
default `= True`
If True, will automatically scroll to the bottom of the textbox when the value
changes, unless the user scrolls up. If False, will not scroll to the bottom
of the textbox when the value changes.
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.
render: bool
default `= True`
If False, component will not render be rendered in the Blocks context. Should
be used if the intention is to assign event listeners now but render the
component later.
key: int | str | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
text_align: Literal['left', 'right'] | None
default `= None`
How to align the text in the textbox, can be: "left", "right", or None
(default). If None, the alignment is left if `rtl` is False, or right if `rtl`
is True. Can only be changed if `type` is "text".
rtl: bool
default `= False`
If True and `type` is "text", sets the direction of the text to right-to-left
(cursor appears on the left of the text). Default is False, which renders
cursor on the right.
show_copy_button: bool
default `= False`
If Tru | Initialization | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
direction of the text to right-to-left
(cursor appears on the left of the text). Default is False, which renders
cursor on the right.
show_copy_button: bool
default `= False`
If True, includes a copy button to copy the text in the textbox. Only applies
if show_label is True.
max_length: int | None
default `= None`
maximum number of characters (including newlines) allowed in the textbox. If
None, there is no maximum length.
submit_btn: str | bool | None
default `= False`
If False, will not show a submit button. If True, will show a submit button
with an icon. If a string, will use that string as the submit button text.
When the submit button is shown, the border of the textbox will be removed,
which is useful for creating a chat interface.
stop_btn: str | bool | None
default `= False`
| Initialization | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.Textbox` | "textbox" | Uses default values
`gradio.TextArea` | "textarea" | Uses lines=7
| Shortcuts | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
hello_worlddiff_textssentence_builder
Open in 🎢 ↗ import gradio as gr def greet(name): return "Hello " + name + "!"
demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox") if __name__
== "__main__": demo.launch()
import gradio as gr
def greet(name):
return "Hello " + name + "!"
demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox")
if __name__ == "__main__":
demo.launch()
Open in 🎢 ↗ 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, color_map={"+": "red", "-":
"green"}), theme=gr.themes.Base() ) 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,
| Demos | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
value="The fast brown fox jumps over lazy dogs.",
),
],
gr.HighlightedText(
label="Diff",
combine_adjacent=True,
show_legend=True,
color_map={"+": "red", "-": "green"}),
theme=gr.themes.Base()
)
if __name__ == "__main__":
demo.launch()
Open in 🎢 ↗ 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 between 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 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( | Demos | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
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 between 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()
| Demos | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
Description
Event listeners allow you to respond to user interactions with the UI
components you've defined in a Gradio Blocks app. When a user interacts with
an element, such as changing a slider value or uploading an image, a function
is called.
Supported Event Listeners
The Textbox component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listener | Description
---|---
`Textbox.change(fn, ···)` | Triggered when the value of the Textbox changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.
`Textbox.input(fn, ···)` | This listener is triggered when the user changes the value of the Textbox.
`Textbox.select(fn, ···)` | Event listener for when the user selects or deselects the Textbox. Uses event data gradio.SelectData to carry `value` referring to the label of the Textbox, and `selected` to refer to state of the Textbox. See EventData documentation on how to use this event data
`Textbox.submit(fn, ···)` | This listener is triggered when the user presses the Enter key while the Textbox is focused.
`Textbox.focus(fn, ···)` | This listener is triggered when the Textbox is focused.
`Textbox.blur(fn, ···)` | This listener is triggered when the Textbox is unfocused/blurred.
`Textbox.stop(fn, ···)` | This listener is triggered when the user reaches the end of the media playing in the Textbox.
`Textbox.copy(fn, ···)` | This listener is triggered when the user copies content from the Textbox. Uses event data gradio.CopyData to carry information about the copied content. See EventData documentation on how to use this event data
Event Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to cal | Event Listeners | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
EventData documentation on how to use this event data
Event Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to call when this event is triggered. 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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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 | Literal[False]
default `= None`
defines how the endpoint appears in the API docs. Can be a string, None, or
False. If set to a string, the endpoint will be exposed in the API docs with
the given name. If None (default), the name of the function will be used as
the API endpoint. If False, the endpoint will not be exposed in the API docs
and downstream apps (including those that `gr.load` this app) will not be able
to use this event.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
default `= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | Non | Event Listeners | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
s a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
queue: bool
default `= True`
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 listener 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 curren | Event Listeners | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
ck_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.
trigger_mode: Literal['once', 'multiple', 'always_last'] | None
default `= None`
If "once" (default for all events except `.change()`) would not allow any
submissions while an event is pending. If set to "multiple", unlimited
submissions are allowed while pending, and "always_last" (default for
`.change()` and `.key_up()` events) would allow a second submission after the
pending event is complete.
js: str | Literal[True] | None
default `= None`
Optional frontend js method to run before running 'fn'. Input arguments for js
method are values of 'inputs' and 'outputs', return should be a list of values
for output components.
concurrency_limit: int | None | Literal['default']
default `= "default"`
If set, this is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any number of
this event can be running simultaneously). Set to "default" to use the default
concurrency limit (defined by the `default_concurrency_limit` parameter in
`Blocks.queue()`, which itself is 1 by default).
concurrency_id: str | None
default `= None`
If set, this is the id of the concurrency group. Events with the same
concurrency_id will be limited by the lowest set concurrency_limit.
show_api: bool
default `= True`
whether to show this event in the "view API" page of the Gradio app, or in the
".view_api()" method of the Gradio clients. Unlike setting api_name to False,
setting show_api to False will still allow downstream apps as well as the
Clients to use this event. If fn is None, show_api will automatically be set
to False.
time_limit: int | None
default `= None`
stream_every | Event Listeners | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
nstream apps as well as the
Clients to use this event. If fn is None, show_api will automatically be set
to False.
time_limit: int | None
default `= None`
stream_every: float
default `= 0.5`
like_user_message: bool
default `= False`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
| Event Listeners | https://gradio.app/docs/gradio/textbox | Gradio - Textbox Docs |
Displays an interactive table of parameters and their descriptions and
default values with syntax highlighting. For each parameter, the user should
provide a type (e.g. a `str`), a human-readable description, and a default
value. As this component does not accept user input, it is rarely used as an
input component. Internally, this component is used to display the parameters
of components in the Custom Component Gallery (https://www.gradio.app/custom-
components/gallery).
| Description | https://gradio.app/docs/gradio/paramviewer | Gradio - Paramviewer Docs |
**As input component** : (Rarely used) passes value as a `dict[str, dict]`.
The key in the outer dictionary is the parameter name, while the inner
dictionary has keys "type", "description", and "default" for each parameter.
Your function should accept one of these types:
def predict(
value: dict[str, Parameter]
)
...
**As output component** : Expects value as a `dict[str, dict]`. The key in
the outer dictionary is the parameter name, while the inner dictionary has
keys "type", "description", and "default" for each parameter.
Your function should return one of these types:
def predict(···) -> dict[str, Parameter]
...
return value
| Behavior | https://gradio.app/docs/gradio/paramviewer | Gradio - Paramviewer Docs |
Parameters ▼
value: dict[str, Parameter] | None
default `= None`
A dictionary of dictionaries. The key in the outer dictionary is the parameter
name, while the inner dictionary has keys "type", "description", and "default"
for each parameter. Markdown links are supported in "description".
language: Literal['python', 'typescript']
default `= "python"`
The language to display the code in. One of "python" or "typescript".
linkify: list[str] | None
default `= None`
A list of strings to linkify. If any of these strings is found in the
description, it will be rendered as a link.
every: Timer | float | None
default `= None`
Continously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
Components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
render: bool
default `= True`
If False, component will not render be rendered in the Blocks context. Should
be used if the intention is to assign event listeners now but render the
component later.
key: int | str | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re | Initialization | https://gradio.app/docs/gradio/paramviewer | Gradio - Paramviewer Docs |
er()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
header: str | None
default `= "Parameters"`
The header to display above the table of parameters, also includes a toggle
button that closes/opens all details at once. If None, no header will be
displayed.
anchor_links: bool | str
default `= False`
If True, creates anchor links for each parameter that can be used to link
directly to that parameter. If a string, creates anchor links with the given
string as the prefix to prevent conflicts with other ParamViewer components.
| Initialization | https://gradio.app/docs/gradio/paramviewer | Gradio - Paramviewer Docs |
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.ParamViewer` | "paramviewer" | Uses default values
| Shortcuts | https://gradio.app/docs/gradio/paramviewer | Gradio - Paramviewer Docs |
Description
Event listeners allow you to respond to user interactions with the UI
components you've defined in a Gradio Blocks app. When a user interacts with
an element, such as changing a slider value or uploading an image, a function
is called.
Supported Event Listeners
The ParamViewer component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listener | Description
---|---
`ParamViewer.change(fn, ···)` | Triggered when the value of the ParamViewer changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.
`ParamViewer.upload(fn, ···)` | This listener is triggered when the user uploads a file into the ParamViewer.
Event Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to call when this event is triggered. 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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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 | Literal[False]
default `= None`
defines how the endpoint appears in the API docs. Can be a string, None, o | Event Listeners | https://gradio.app/docs/gradio/paramviewer | Gradio - Paramviewer Docs |
on returns no
outputs, this should be an empty list.
api_name: str | None | Literal[False]
default `= None`
defines how the endpoint appears in the API docs. Can be a string, None, or
False. If set to a string, the endpoint will be exposed in the API docs with
the given name. If None (default), the name of the function will be used as
the API endpoint. If False, the endpoint will not be exposed in the API docs
and downstream apps (including those that `gr.load` this app) will not be able
to use this event.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
default `= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
queue: bool
default `= True`
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 bat | Event Listeners | https://gradio.app/docs/gradio/paramviewer | Gradio - Paramviewer Docs |
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 listener 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.
trigger_mode: Literal['once', 'multiple', 'always_last'] | None
default `= None`
If "once" (default for all events except `.change()`) would not allow any
submissions while an event is pending. If set to "multiple", unlimited
submissions are allowed while pending, and "always_last" (default for
`.change()` and `.key_up()` events) would allow a second submission after the
pending event is complete.
js: str | Literal[True] | None
default `= None`
Optional frontend js method to run before running 'fn'. Input arguments for js
method are values of 'inputs' and 'outputs', return should be a list of values
for output components.
concurrency_limit: int | None | Literal['default']
default `= "default"`
If set, this is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any num | Event Listeners | https://gradio.app/docs/gradio/paramviewer | Gradio - Paramviewer Docs |
t: int | None | Literal['default']
default `= "default"`
If set, this is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any number of
this event can be running simultaneously). Set to "default" to use the default
concurrency limit (defined by the `default_concurrency_limit` parameter in
`Blocks.queue()`, which itself is 1 by default).
concurrency_id: str | None
default `= None`
If set, this is the id of the concurrency group. Events with the same
concurrency_id will be limited by the lowest set concurrency_limit.
show_api: bool
default `= True`
whether to show this event in the "view API" page of the Gradio app, or in the
".view_api()" method of the Gradio clients. Unlike setting api_name to False,
setting show_api to False will still allow downstream apps as well as the
Clients to use this event. If fn is None, show_api will automatically be set
to False.
time_limit: int | None
default `= None`
stream_every: float
default `= 0.5`
like_user_message: bool
default `= False`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
| Event Listeners | https://gradio.app/docs/gradio/paramviewer | Gradio - Paramviewer Docs |
Creates a color picker for user to select a color as string input. Can be
used as an input to pass a color value to a function or as an output to
display a color value.
| Description | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
**As input component** : Passes selected color value as a hex `str` into
the function.
Your function should accept one of these types:
def predict(
value: str | None
)
...
**As output component** : Expects a hex `str` returned from function and
sets color picker value to it.
Your function should return one of these types:
def predict(···) -> str | None
...
return value
| Behavior | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
Parameters ▼
value: str | Callable | None
default `= None`
default color hex code to provide in color picker. If a function is provided,
the function will be called each time the app loads to set the initial value
of this component.
label: str | I18nData | None
default `= None`
the label for this component, displayed above the component if `show_label` is
`True` and is also used as the header if there are a table of examples for
this component. If None and used in a `gr.Interface`, the label will be the
name of the parameter this component corresponds to.
info: str | I18nData | None
default `= None`
additional component description, appears below the label in smaller font.
Supports markdown / HTML syntax.
every: Timer | float | None
default `= None`
Continously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
Components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
show_label: bool | None
default `= None`
if True, will display label.
container: bool
default `= True`
If True, will place the component in a container - providing some extra
padding around the border.
scale: int | None
default `= None`
relative size compared to adjacent Components. For example if Components A and
B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide
as B. Should be an integer. scale applies in Rows, and to top-level Components
in Blocks where fill_height=True.
min_width: int
default `= 160`
minimum pixel width, will wrap if not sufficient screen space to satisfy this
value. If a cer | Initialization | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
to top-level Components
in Blocks where fill_height=True.
min_width: int
default `= 160`
minimum pixel width, will wrap if not sufficient screen space to satisfy this
value. If a certain scale value results in this Component being narrower than
min_width, the min_width parameter will be respected first.
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.
render: bool
default `= True`
If False, component will not render be rendered in the Blocks context. Should
be used if the intention is to assign event listeners now but render the
component later.
key: int | str | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
| Initialization | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
| Initialization | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.ColorPicker` | "colorpicker" | Uses default values
| Shortcuts | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
color_picker
Open in 🎢 ↗ import gradio as gr import numpy as np 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 ) if __name__
== "__main__": demo.launch()
import gradio as gr
import numpy as np
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
)
if __name__ == "__main__":
demo.launch()
| Demos | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
Description
Event listeners allow you to respond to user interactions with the UI
components you've defined in a Gradio Blocks app. When a user interacts with
an element, such as changing a slider value or uploading an image, a function
is called.
Supported Event Listeners
The ColorPicker component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listener | Description
---|---
`ColorPicker.change(fn, ···)` | Triggered when the value of the ColorPicker changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.
`ColorPicker.input(fn, ···)` | This listener is triggered when the user changes the value of the ColorPicker.
`ColorPicker.submit(fn, ···)` | This listener is triggered when the user presses the Enter key while the ColorPicker is focused.
`ColorPicker.focus(fn, ···)` | This listener is triggered when the ColorPicker is focused.
`ColorPicker.blur(fn, ···)` | This listener is triggered when the ColorPicker is unfocused/blurred.
Event Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to call when this event is triggered. 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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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 | BlockContext | list[Component | Bloc | Event Listeners | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
default `= None`
List of gradio.components to use as inputs. If the function takes no inputs,
this should be an empty list.
outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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 | Literal[False]
default `= None`
defines how the endpoint appears in the API docs. Can be a string, None, or
False. If set to a string, the endpoint will be exposed in the API docs with
the given name. If None (default), the name of the function will be used as
the API endpoint. If False, the endpoint will not be exposed in the API docs
and downstream apps (including those that `gr.load` this app) will not be able
to use this event.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
default `= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
queue: bool
default `= True`
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 | Event Listeners | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
he 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 listener 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.
trigger_mode: Literal['once', 'multiple', 'always_last'] | None
default `= None`
If "once" (default for all events except `.change()`) would not allow any
submissions while an event is pending. If set to "multiple", unlimited
submissions are allowed while pending, and "always_last" (default for
`.change()` and `.key_up()` events) would allow a second submission after the
pending event is complete.
js: str | Literal[True] | None
default `= None`
Optional frontend js method to run before running 'fn'. Input arguments for js
method a | Event Listeners | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
ubmission after the
pending event is complete.
js: str | Literal[True] | None
default `= None`
Optional frontend js method to run before running 'fn'. Input arguments for js
method are values of 'inputs' and 'outputs', return should be a list of values
for output components.
concurrency_limit: int | None | Literal['default']
default `= "default"`
If set, this is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any number of
this event can be running simultaneously). Set to "default" to use the default
concurrency limit (defined by the `default_concurrency_limit` parameter in
`Blocks.queue()`, which itself is 1 by default).
concurrency_id: str | None
default `= None`
If set, this is the id of the concurrency group. Events with the same
concurrency_id will be limited by the lowest set concurrency_limit.
show_api: bool
default `= True`
whether to show this event in the "view API" page of the Gradio app, or in the
".view_api()" method of the Gradio clients. Unlike setting api_name to False,
setting show_api to False will still allow downstream apps as well as the
Clients to use this event. If fn is None, show_api will automatically be set
to False.
time_limit: int | None
default `= None`
stream_every: float
default `= 0.5`
like_user_message: bool
default `= False`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
| Event Listeners | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
Creates a textarea for users to enter string input or display string output
and also allows for the uploading of multimedia files.
| Description | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
**As input component** : Passes text value and list of file(s) as a `dict`
into the function.
Your function should accept one of these types:
def predict(
value: MultimodalValue | None
)
...
**As output component** : Expects a `dict` with "text" and "files", both
optional. The files array is a list of file paths or URLs.
Your function should return one of these types:
def predict(···) -> MultimodalValue | None
...
return value
| Behavior | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
Parameters ▼
value: str | dict[str, str | list] | Callable | None
default `= None`
Default value to show in MultimodalTextbox. A string value, or a dictionary of
the form {"text": "sample text", "files": [{path: "files/file.jpg", orig_name:
"file.jpg", url: "http://image_url.jpg", size: 100}]}. If a function is
provided, the function will be called each time the app loads to set the
initial value of this component.
sources: list[Literal['upload', 'microphone']] | Literal['upload', 'microphone'] | None
default `= None`
A list of sources permitted. "upload" creates a button where users can click
to upload or drop files, "microphone" creates a microphone input. If None,
defaults to ["upload"].
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.
file_count: Literal['single', 'multiple', 'directory']
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".
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 | I18nData | None
default `= None`
the label for this component, displayed above the component if `show_label` is
`True` and is also used as the header if there are a table of examples for
this component. If None and used in a `gr. | Initialization | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
e`
the label for this component, displayed above the component if `show_label` is
`True` and is also used as the header if there are a table of examples for
this component. If None and used in a `gr.Interface`, the label will be the
name of the parameter this component corresponds to.
info: str | I18nData | None
default `= None`
additional component description, appears below the label in smaller font.
Supports markdown / HTML syntax.
every: Timer | float | None
default `= None`
Continously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
Components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
show_label: bool | None
default `= None`
if True, will display label.
container: bool
default `= True`
If True, will place the component in a container - providing some extra
padding around the border.
scale: int | None
default `= None`
relative size compared to adjacent Components. For example if Components A and
B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide
as B. Should be an integer. scale applies in Rows, and to top-level Components
in Blocks where fill_height=True.
min_width: int
default `= 160`
minimum pixel width, will wrap if not sufficient screen space to satisfy this
value. If a certain scale value results in this Component being narrower than
min_width, the min_width parameter will be respected first.
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 | Initialization | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
cted first.
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.
autofocus: bool
default `= False`
If True, will focus on the textbox when the page loads. Use this carefully, as
it can cause usability issues for sighted and non-sighted users.
autoscroll: bool
default `= True`
If True, will automatically scroll to the bottom of the textbox when the value
changes, unless the user scrolls up. If False, will not scroll to the bottom
of the textbox when the value changes.
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.
render: bool
default `= True`
If False, component will not render be rendered in the Blocks context. Should
be used if the intention is to assign event listeners now but render the
component later.
key: int | str | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re | Initialization | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
er()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
text_align: Literal['left', 'right'] | None
default `= None`
How to align the text in the textbox, can be: "left", "right", or None
(default). If None, the alignment is left if `rtl` is False, or right if `rtl`
is True. Can only be changed if `type` is "text".
rtl: bool
default `= False`
If True and `type` is "text", sets the direction of the text to right-to-left
(cursor appears on the left of the text). Default is False, which renders
cursor on the right.
submit_btn: str | bool | None
default `= True`
If False, will not show a submit button. If a string, will use that string as
the submit button text.
stop_btn: str | bool | None
default `= False`
If True, will show a stop button (useful for streaming demos). If a string,
will use that string as the stop button text.
max_plain_text_length: int
default `= 1000`
Maximum length of plain text in the textbox. If the text exceeds this length,
the text will be pasted as a file. Default is 1000.
| Initialization | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
Class | Interface String Shortcut | Initialization
---|---|---
`gradio.MultimodalTextbox` | "multimodaltextbox" | Uses default values
| Shortcuts | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
chatbot_multimodal
Open in 🎢 ↗ import gradio as gr import time Chatbot demo with multimodal
input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows
support for streaming text. def print_like_dislike(x: gr.LikeData):
print(x.index, x.value, x.liked) def add_message(history, message): for x in
message["files"]: history.append({"role": "user", "content": {"path": x}}) if
message["text"] is not None: history.append({"role": "user", "content":
message["text"]}) return history, gr.MultimodalTextbox(value=None,
interactive=False) def bot(history: list): response = "**That's cool!**"
history.append({"role": "assistant", "content": ""}) for character in
response: history[-1]["content"] += character time.sleep(0.05) yield history
with gr.Blocks() as demo: chatbot = gr.Chatbot(elem_id="chatbot",
bubble_full_width=False, type="messages") chat_input = gr.MultimodalTextbox(
interactive=True, file_count="multiple", placeholder="Enter message or upload
file...", show_label=False, sources=["microphone", "upload"], ) chat_msg =
chat_input.submit( add_message, [chatbot, chat_input], [chatbot, chat_input] )
bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response")
bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None,
[chat_input]) chatbot.like(print_like_dislike, None, None,
like_user_message=True) if __name__ == "__main__": demo.launch()
import gradio as gr
import time
Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.
def print_like_dislike(x: gr.LikeData):
print(x.index, x.value, x.liked)
def add_message(history, message):
for x in message["files"]:
history.append({"role": "user", "content": {"path": x}})
if message["text"] is not None:
history.append({"role": "user", "content": message["text"]})
return history, gr.MultimodalTextbox(valu | Demos | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
le": "user", "content": {"path": x}})
if message["text"] is not None:
history.append({"role": "user", "content": message["text"]})
return history, gr.MultimodalTextbox(value=None, interactive=False)
def bot(history: list):
response = "**That's cool!**"
history.append({"role": "assistant", "content": ""})
for character in response:
history[-1]["content"] += character
time.sleep(0.05)
yield history
with gr.Blocks() as demo:
chatbot = gr.Chatbot(elem_id="chatbot", bubble_full_width=False, type="messages")
chat_input = gr.MultimodalTextbox(
interactive=True,
file_count="multiple",
placeholder="Enter message or upload file...",
show_label=False,
sources=["microphone", "upload"],
)
chat_msg = chat_input.submit(
add_message, [chatbot, chat_input], [chatbot, chat_input]
)
bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name="bot_response")
bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])
chatbot.like(print_like_dislike, None, None, like_user_message=True)
if __name__ == "__main__":
demo.launch()
| Demos | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
Description
Event listeners allow you to respond to user interactions with the UI
components you've defined in a Gradio Blocks app. When a user interacts with
an element, such as changing a slider value or uploading an image, a function
is called.
Supported Event Listeners
The MultimodalTextbox component supports the following event listeners. Each
event listener takes the same parameters, which are listed in the Event
Parameters table below.
Listener | Description
---|---
`MultimodalTextbox.change(fn, ···)` | Triggered when the value of the MultimodalTextbox changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.
`MultimodalTextbox.input(fn, ···)` | This listener is triggered when the user changes the value of the MultimodalTextbox.
`MultimodalTextbox.select(fn, ···)` | Event listener for when the user selects or deselects the MultimodalTextbox. Uses event data gradio.SelectData to carry `value` referring to the label of the MultimodalTextbox, and `selected` to refer to state of the MultimodalTextbox. See EventData documentation on how to use this event data
`MultimodalTextbox.submit(fn, ···)` | This listener is triggered when the user presses the Enter key while the MultimodalTextbox is focused.
`MultimodalTextbox.focus(fn, ···)` | This listener is triggered when the MultimodalTextbox is focused.
`MultimodalTextbox.blur(fn, ···)` | This listener is triggered when the MultimodalTextbox is unfocused/blurred.
`MultimodalTextbox.stop(fn, ···)` | This listener is triggered when the user reaches the end of the media playing in the MultimodalTextbox.
Event Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to call when this event is triggered. Often a machine learning
model's pred | Event Listeners | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
t Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to call when this event is triggered. 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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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 | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | 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 | Literal[False]
default `= None`
defines how the endpoint appears in the API docs. Can be a string, None, or
False. If set to a string, the endpoint will be exposed in the API docs with
the given name. If None (default), the name of the function will be used as
the API endpoint. If False, the endpoint will not be exposed in the API docs
and downstream apps (including those that `gr.load` this app) will not be able
to use this event.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
default `= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the prog | Event Listeners | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
he runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
queue: bool
default `= True`
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 listener 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.
trigger_mode: | Event Listeners | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
.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.
trigger_mode: Literal['once', 'multiple', 'always_last'] | None
default `= None`
If "once" (default for all events except `.change()`) would not allow any
submissions while an event is pending. If set to "multiple", unlimited
submissions are allowed while pending, and "always_last" (default for
`.change()` and `.key_up()` events) would allow a second submission after the
pending event is complete.
js: str | Literal[True] | None
default `= None`
Optional frontend js method to run before running 'fn'. Input arguments for js
method are values of 'inputs' and 'outputs', return should be a list of values
for output components.
concurrency_limit: int | None | Literal['default']
default `= "default"`
If set, this is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any number of
this event can be running simultaneously). Set to "default" to use the default
concurrency limit (defined by the `default_concurrency_limit` parameter in
`Blocks.queue()`, which itself is 1 by default).
concurrency_id: str | None
default `= None`
If set, this is the id of the concurrency group. Events with the same
concurrency_id will be limited by the lowest set concurrency_limit.
show_api: bool
default `= True`
whether to show this event in the "view API" page of the Gradio app, or in the
".view_api()" method of the Gradio clients. Unlike setting api_name to False,
setting show_api to False will still allow downstream apps as well as the
Clients to use this event. If fn is None, show_api will automatically be set
to False.
time_limit: int | None
default `= None`
stream_every: float
default `= 0.5`
like_user_message: bool
def | Event Listeners | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
show_api will automatically be set
to False.
time_limit: int | None
default `= None`
stream_every: float
default `= 0.5`
like_user_message: bool
default `= False`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
| Event Listeners | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
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