Spaces:

njain1978
/

llmops

Running

App Files Files Community

llmops / app.py

njain1978

Upload 4 files

62a27fc verified 4 months ago

raw history blame contribute delete

No virus

5.36 kB

	# Import streamlit and requests
	from src.utils import (get_output, show_output)
	from src.constants import TASKS
	import streamlit as st
	import json

	with st.sidebar:
	st.markdown(
	"## How to use\n"
	"1. Enter your [Huggingface API token](https://huggingface.co/settings/tokens) (Recommended, as some model won't work)\n"
	"2. Choose a task to perform \n"
	"3. Choose a LLM model correspong to task \n"
	"4. Enter a different LLM model id from Huggingface hub (Optional) \n"
	)

	st.write("##")

	# Create a text input for accepting the Huggingface API Token
	huggingface_api_token = st.text_input("Huggingface API Token (Optional)",
	key="hf_api_token",
	type="password",
	help="You can get your API token from https://huggingface.co/settings/tokens."
	)

	# Create a select box for choosing the ML task
	task = st.selectbox("Choose a task", list(TASKS.keys()))

	# Create a select box for choosing the model based on the task
	select_model = st.selectbox("Choose a model", TASKS[task])

	# Create a text box for putting the model id (from Huggingface hub) based on the selected task
	optional_model = st.text_input("Enter Huggingface model (Optional)")

	if (optional_model != ""):
	model = optional_model
	else:
	model = select_model

	# Create a title for the app
	st.header("Huggingface Hub Model Explorer")

	# Create a header for displaying the input and output widgets
	st.subheader(f"{task} with {model}")

	# Create different input and output widgets based on the task
	if task == "Text Generation":
	# Create a text input for entering the text to generate from
	text_input = st.text_input("Enter some text to generate from")

	# Create a button for generating the text
	generate_button = st.button("Generate")

	# If the button is clicked and the input is not empty
	if generate_button and text_input:
	# Create a payload with the input text
	data = json.dumps({"inputs": text_input})

	# Get the output from the API
	output = get_output(task, model, data, api_token=huggingface_api_token)

	# Display the output text on the output widget
	show_output(output)

	elif task == "Text Summarization":
	# Create a text area for entering the text to summarize
	text_area = st.text_area("Enter some text to summarize")

	# Create a button for summarizing the text
	summarize_button = st.button("Summarize")

	# If the button is clicked and the input is not empty
	if summarize_button and text_area:
	# Create a payload with the input text
	data = json.dumps({"inputs": text_area})

	# Get the output from the API
	output = get_output(task, model, data, api_token=huggingface_api_token)

	# Display the output summary on the output widget
	show_output(output)

	elif task == "Text Classification":
	# Create a text input for entering the text to classify
	text_input = st.text_input("Enter some text to classify")

	# Create a button for classifying the text
	classify_button = st.button("Classify")

	# If the button is clicked and the input is not empty
	if classify_button and text_input:
	# Create a payload with the input text
	data = json.dumps({"inputs": text_input})

	# Get the output from the API
	output = get_output(task, model, data, api_token=huggingface_api_token)

	# Display the output label and score on the output widget
	ref_output = output[0]
	for out in ref_output:
	st.text(f"{out['label']}: {round(out['score'] * 100, 1)}%")

	elif task == "Text-to-Speech":
	# Create a text input for entering the text to synthesize
	text_input = st.text_input("Enter some text to synthesize")

	# Create a button for synthesizing the speech
	synthesize_button = st.button("Synthesize")

	# If the button is clicked and the input is not empty
	if synthesize_button and text_input:
	# Create a payload with the input text
	data = json.dumps({"inputs": text_input})

	# Get the output from the API
	output = get_output(task, model, data, api_token=huggingface_api_token, model_type="audio")

	# Display the output audio on the output widget
	with open("audio.wav", "wb") as f:
	f.write(output.content)
	st.audio("audio.wav")

	elif task == "Image Classification":
	# Create a file uploader for uploading an image to classify
	image_file = st.file_uploader("Upload an image to classify")

	# Create a button for classifying the image
	classify_button = st.button("Classify")

	# Create an empty output widget
	output_widget = st.empty()

	# If the button is clicked and the file is not None
	if classify_button and image_file:
	# Read the image file as bytes
	image_bytes = image_file.read()

	# Get the output from the API
	output = get_output(task, model, data=image_bytes, api_token=huggingface_api_token)

	# Display the output label and score on the output widget
	for items in output:
	st.text(f"{items['label']}: {round(items['score'] * 100, 1)}%")