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import gradio as gr | |
import os | |
import json | |
import requests | |
#Streaming endpoint | |
API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream" | |
#Testing with my Open AI Key | |
#OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") | |
def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[], history=[]): #repetition_penalty, top_k | |
payload = { | |
"model": "gpt-4", | |
"messages": [{"role": "user", "content": f"{inputs}"}], | |
"temperature" : 1.0, | |
"top_p":1.0, | |
"n" : 1, | |
"stream": True, | |
"presence_penalty":0, | |
"frequency_penalty":0, | |
} | |
headers = { | |
"Content-Type": "application/json", | |
"Authorization": f"Bearer {openai_api_key}" | |
} | |
print(f"chat_counter - {chat_counter}") | |
if chat_counter != 0 : | |
messages=[] | |
for data in chatbot: | |
temp1 = {} | |
temp1["role"] = "user" | |
temp1["content"] = data[0] | |
temp2 = {} | |
temp2["role"] = "assistant" | |
temp2["content"] = data[1] | |
messages.append(temp1) | |
messages.append(temp2) | |
temp3 = {} | |
temp3["role"] = "user" | |
temp3["content"] = inputs | |
messages.append(temp3) | |
#messages | |
payload = { | |
"model": "gpt-4", | |
"messages": messages, #[{"role": "user", "content": f"{inputs}"}], | |
"temperature" : temperature, #1.0, | |
"top_p": top_p, #1.0, | |
"n" : 1, | |
"stream": True, | |
"presence_penalty":0, | |
"frequency_penalty":0, | |
} | |
chat_counter+=1 | |
history.append(inputs) | |
print(f"payload is - {payload}") | |
# 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) | |
#response = requests.post(API_URL, headers=headers, json=payload, stream=True) | |
token_counter = 0 | |
partial_words = "" | |
counter=0 | |
for chunk in response.iter_lines(): | |
#Skipping first chunk | |
if counter == 0: | |
counter+=1 | |
continue | |
#counter+=1 | |
# check whether each line is non-empty | |
if chunk.decode() : | |
chunk = chunk.decode() | |
# decode each line as response data is in bytes | |
if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']: | |
#if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0: | |
# break | |
partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"] | |
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, chat_counter # resembles {chatbot: chat, state: history} | |
def reset_textbox(): | |
return gr.update(value='') | |
title = """<h1 align="center">🔥ChatGPT API 🚀Streaming🚀</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 gpt-4 LLM. | |
""" | |
with gr.Blocks(css = """#col_container {width: 1000px; margin-left: auto; margin-right: auto;} | |
#chatbot {height: 520px; overflow: auto;}""") as demo: | |
gr.HTML(title) | |
gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPTwithAPI?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''') | |
with gr.Column(elem_id = "col_container"): | |
openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here") | |
chatbot = gr.Chatbot(elem_id='chatbot') #c | |
inputs = gr.Textbox(placeholder= "Hi there!", 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=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",) | |
temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, 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", ) | |
chat_counter = gr.Number(value=0, visible=False, precision=0) | |
inputs.submit( predict, [inputs, top_p, temperature, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],) | |
b1.click( predict, [inputs, top_p, temperature, openai_api_key, chat_counter, chatbot, state], [chatbot, state, chat_counter],) | |
b1.click(reset_textbox, [], [inputs]) | |
inputs.submit(reset_textbox, [], [inputs]) | |
#gr.Markdown(description) | |
demo.queue().launch(debug=True) | |