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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_old(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)
response = requests.post(API_URL, headers=headers, json=payload)
responses = response.text.split("\n\n")
partial_words = ""
for idx, resp in enumerate(responses):
if resp[:4] == 'data':
partial_words = partial_words + json.loads(resp[5:])['token']['text']
#print(partial_words)
time.sleep(0.05)
if idx == 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
yield chat, history #resembles {chatbot: chat, state: history}
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)
response = requests.post(API_URL2, headers=headers, json=payload, stream=True)
token_counter = 0
partial_words = "" #inputs
for chunk in response.iter_lines():
if chunk:
#print(chunk.decode())
partial_words = partial_words + json.loads(chunk.decode()[5:])['token']['text']
#print(partial_words)
time.sleep(0.05)
#print([(partial_words, "")])
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
#yield [(partial_words, history)]
token_counter+=1
yield chat, history #{chatbot: chat, state: history} #[(partial_words, history)]
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 large language models.
"""
with gr.Blocks(css = "#chatbot {height: 400px; overflow: auto;}") as demo:
gr.HTML(title)
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", )
#b1.click(predict, [t,s], [c,s])
#inputs.submit(predict, [t,s], [c,s])
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],)
gr.Markdown(description)
demo.queue().launch(debug=True)
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