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Gradio-demo-streaming / app.py

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	import time
	import gradio as gr
	import os
	import json
	import requests

	#Streaming endpoint
	API_URL = os.getenv("API_URL") + "/generate_stream"

	def predict(inputs, top_p, temperature, top_k, repetition_penalty, history=[]):
	if not inputs.startswith("User: "):
	inputs = "User: " + inputs + "\n"
	payload = {
	"inputs": inputs, #"My name is Jane and I",
	"parameters": {
	"details": True,
	"do_sample": True,
	"max_new_tokens": 100,
	"repetition_penalty": repetition_penalty, #1.03,
	"seed": 0,
	"temperature": temperature, #0.5,
	"top_k": top_k, #10,
	"top_p": top_p #0.95
	}
	}

	headers = {
	'accept': 'text/event-stream',
	'Content-Type': 'application/json'
	}

	history.append(inputs)
	# make a POST request to the API endpoint using the requests.post method, passing in stream=True
	response = requests.post(API_URL, headers=headers, json=payload, stream=True)
	token_counter = 0
	partial_words = ""
	# loop over the response data using the iter_lines method of the response object
	for chunk in response.iter_lines():
	# check whether each line is non-empty
	if chunk:
	# decode each line as response data is in bytes
	partial_words = partial_words + json.loads(chunk.decode()[5:])['token']['text']
	if token_counter == 0:
	history.append(" " + partial_words)
	else:
	history[-1] = partial_words
	chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ] # convert to tuples of list
	token_counter+=1
	yield chat, history #{chatbot: chat, state: history} #[(partial_words, history)]

	def reset_textbox():
	return gr.update(value='')

	title = """<h1 align="center">🔥Streaming your 🤖Chatbot output with Gradio🚀</h1>"""
	description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
	```
	User: <utterance>
	Assistant: <utterance>
	User: <utterance>
	Assistant: <utterance>
	...
	```
	In this app, you can explore the outputs of a 20B large language model.
	"""

	with gr.Blocks(css = """#col_container {width: 700px; margin-left: auto; margin-right: auto;}
	#chatbot {height: 400px; overflow: auto;}""") as demo:
	gr.HTML(title)
	with gr.Column(elem_id = "col_container"):
	chatbot = gr.Chatbot(elem_id='chatbot') #c
	inputs = gr.Textbox(placeholder= "Hi my name is Joe.", label= "Type an input and press Enter") #t
	state = gr.State([]) #s
	b1 = gr.Button()

	#inputs, top_p, temperature, top_k, repetition_penalty
	with gr.Accordion("Parameters", open=False):
	top_p = gr.Slider( minimum=-0, maximum=1.0, value=0.95, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
	temperature = gr.Slider( minimum=-0, maximum=5.0, value=0.5, step=0.1, interactive=True, label="Temperature",)
	top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
	repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )


	inputs.submit( predict, [inputs, top_p, temperature, top_k, repetition_penalty, state], [chatbot, state],)
	b1.click( predict, [inputs, top_p, temperature, top_k, repetition_penalty, state], [chatbot, state],)
	b1.click(reset_textbox, [], [inputs])
	inputs.submit(reset_textbox, [], [inputs])

	gr.Markdown(description)
	demo.queue().launch(debug=True)