app.py

import gradio as gr
import os
import sys
import json 
import requests

#Streaming endpoint 
API_URL = os.getenv("API_URL")
DISABLED = os.getenv("DISABLED") == 'True'

#Testing with my Open AI Key 
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

#Supress errors
def exception_handler(exception_type, exception, traceback):
    print("%s: %s" % (exception_type.__name__, exception))
sys.excepthook = exception_handler
sys.tracebacklimit = 0

def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  

    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_code = f"{response}"
    if response_code.strip() != "<Response [200]>":
        #print(f"response code - {response}")
        raise Exception(f"Sorry, hitting rate limit. Please try again later. {response}")
    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, response  # resembles {chatbot: chat, state: history}  
    print(json.dumps({"chat_counter": chat_counter, "payload": payload, "partial_words": partial_words, "token_counter": token_counter, "counter": counter}))
                   

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

title = """<h1 align="center">🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming</h1>"""
if DISABLED:
    title = """<h1 align="center" style="color:red">This app has reached OpenAI's usage limit. We are currently requesting an increase in our quota. Please check back in a few days.</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.
"""

theme = gr.themes.Default(primary_hue="green")                

with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
                #chatbot {height: 520px; overflow: auto;}""",
              theme=theme) as demo:
    gr.HTML(title)
    gr.HTML("""<h3 align="center">🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌</h1>""")
    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?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"):
        #GPT4 API Key is provided by Huggingface 
        #openai_api_key = gr.Textbox(type='password', label="Enter only your GPT4 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
        with gr.Row():
            with gr.Column(scale=7):
                b1 = gr.Button(visible=not DISABLED).style(full_width=True)
            with gr.Column(scale=3):
                server_status_code = gr.Textbox(label="Status code from OpenAI server", )
    
        #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)
        with gr.Modal("User Consent for Data Collection and Use", open=True):
            gr.HTML("""<p>
            By using our app powered by OpenAI's API, you acknowledge and agree that we may collect and use the data you provide, including the inputs you type into our app and the outputs generated by OpenAI's API. Your data may be published or shared with others.
            </p>
            <p>
            If you do not agree with our data collection and use practices, please do not use our app. By continuing to use our app, you are providing your explicit consent to the collection, use, and potential sharing of your data as described above.
            </p>""")
            accept_button = gr.Button("I Agree", id="accept_consent")

        # Disable chatbot, inputs, and buttons until consent is given
        chatbot.set_disabled(True)
        inputs.set_disabled(True)
        b1.set_disabled(True)

        def enable_inputs():
            chatbot.set_disabled(False)
            inputs.set_disabled(False)
            b1.set_disabled(False)

        accept_button.on_click(enable_inputs)

    inputs.submit( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
    b1.click( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
    b1.click(reset_textbox, [], [inputs])
    inputs.submit(reset_textbox, [], [inputs])
                    
    #gr.Markdown(description)
    demo.queue(max_size=20, concurrency_count=10).launch()