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" #Huggingface provided GPT4 OpenAI API Key OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") #Inferenec function def predict(system_msg, inputs, top_p, temperature, chat_counter, chatbot=[], history=[]): headers = { "Content-Type": "application/json", "Authorization": f"Bearer {OPENAI_API_KEY}" } print(f"system message is ^^ {system_msg}") if system_msg.strip() == '': initial_message = [{"role": "user", "content": f"{inputs}"},] multi_turn_message = [] else: initial_message= [{"role": "system", "content": system_msg}, {"role": "user", "content": f"{inputs}"},] multi_turn_message = [{"role": "system", "content": system_msg},] if chat_counter == 0 : payload = { "model": "gpt-4", "messages": initial_message , "temperature" : 1.0, "top_p":1.0, "n" : 1, "stream": True, "presence_penalty":0, "frequency_penalty":0, } print(f"chat_counter - {chat_counter}") else: #if chat_counter != 0 : messages=multi_turn_message # Of the type of - [{"role": "system", "content": system_msg},] for data in chatbot: user = {} user["role"] = "user" user["content"] = data[0] assistant = {} assistant["role"] = "assistant" assistant["content"] = data[1] messages.append(user) messages.append(assistant) temp = {} temp["role"] = "user" temp["content"] = inputs messages.append(temp) #messages payload = { "model": "gpt-3.5-turbo", "messages": messages, # Of the type of [{"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"Logging : 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) print(f"Logging : response code - {response}") token_counter = 0 partial_words = "" counter=0 for chunk in response.iter_lines(): #Skipping first chunk if counter == 0: counter+=1 continue # 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']: 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} #Resetting to blank def reset_textbox(): return gr.update(value='') #to set a component as visible=False def set_visible_false(): return gr.update(visible=False) #to set a component as visible=True def set_visible_true(): return gr.update(visible=True) title = """