# -*- coding: utf-8 -*- """ @author:XuMing(xuming624@qq.com) @description: """ import gradio as gr import os import json import requests from loguru import logger from dotenv import load_dotenv # logger.add('gradio_server.log', rotation='10 MB', encoding='utf-8', level='DEBUG') def get_api_key(): api_key = '' if os.path.isfile('.env'): load_dotenv() if os.environ.get('API_KEY') is not None: api_key = os.environ.get('API_KEY') return api_key def set_new_api_key(api_key): # Write the api key to the .env file with open('.env', 'w') as f: f.write(f'API_KEY={api_key}') # Streaming endpoint for OPENAI ChatGPT API_URL = "https://api.openai.com/v1/chat/completions" # Predict function for CHATGPT def predict_chatgpt(inputs, top_p_chatgpt, temperature_chatgpt, openai_api_key, chat_counter_chatgpt, chatbot_chatgpt=[], history=[]): # Define payload and header for chatgpt API payload = { "model": "gpt-3.5-turbo", "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}" } # Handling the different roles for ChatGPT if chat_counter_chatgpt != 0: messages = [] for data in chatbot_chatgpt: 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) payload = { "model": "gpt-3.5-turbo", "messages": messages, # [{"role": "user", "content": f"{inputs}"}], "temperature": temperature_chatgpt, # 1.0, "top_p": top_p_chatgpt, # 1.0, "n": 1, "stream": True, "presence_penalty": 0, "frequency_penalty": 0, } chat_counter_chatgpt += 1 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 = "" counter = 0 for chunk in response.iter_lines(): # Skipping the 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) > 13 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_chatgpt # this resembles {chatbot: chat, state: history} logger.info(f"input: {inputs}, output: {partial_words}") def reset_textbox(): return gr.update(value="") def reset_chat(chatbot, state): return None, [] title = """