chat_response.py

import numpy as np
import os
import re
import datetime
import time
import openai, tenacity
import argparse
import configparser
import json
import tiktoken
from get_paper_from_pdf import Paper
# ChatResponse
# 定义Response类
class Response:
    # 初始化方法，设置属性
    def __init__(self, args=None):
        if args.language == 'en':
            self.language = 'English'
        elif args.language == 'zh':
            self.language = 'Chinese'
        else:
            self.language = 'Chinese'        
        # 创建一个ConfigParser对象
        self.config = configparser.ConfigParser()
        # 读取配置文件
        self.config.read('apikey.ini')
        # 获取某个键对应的值        
        self.chat_api_list = self.config.get('OpenAI', 'OPENAI_API_KEYS')[1:-1].replace('\'', '').split(',')
        self.chat_api_list = [api.strip() for api in self.chat_api_list if len(api) > 5]
        self.cur_api = 0
        self.file_format = args.file_format        
        self.max_token_num = 4096
        self.encoding = tiktoken.get_encoding("gpt2")
    

    def response_by_chatgpt(self, comment_path):
        htmls = []
        # 读取回复的内容
        with open(comment_path, 'r') as file:  
            comments = file.read()

        chat_response_text = self.chat_response(text=comments)                       
        htmls.append(chat_response_text)
        
        # 将审稿意见保存起来
        date_str = str(datetime.datetime.now())[:13].replace(' ', '-')
        try:
            export_path = os.path.join('./', 'response_file')
            os.makedirs(export_path)
        except:
            pass                             
        file_name = os.path.join(export_path, date_str+'-Response.'+self.file_format)
        self.export_to_markdown("\n".join(htmls), file_name=file_name)
        htmls = [] 
    
    
    @tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, min=4, max=10),
                    stop=tenacity.stop_after_attempt(5),
                    reraise=True)
    def chat_response(self, text):
        openai.api_key = self.chat_api_list[self.cur_api]
        self.cur_api += 1
        self.cur_api = 0 if self.cur_api >= len(self.chat_api_list)-1 else self.cur_api
        response_prompt_token = 1000        
        text_token = len(self.encoding.encode(text))
        input_text_index = int(len(text)*(self.max_token_num-response_prompt_token)/text_token)
        input_text = "This is the review comments:" + text[:input_text_index]
        messages=[
                {"role": "system", "content": """You are the author, you submitted a paper, and the reviewers gave the review comments. 
                Please reply with what we have done, not what we will do.
                You need to extract questions from the review comments one by one, and then respond point-to-point to the reviewers’ concerns. 
                Please answer in {}. Follow the format of the output later: 
                - Response to reviewers
                #1 reviewer
                Concern #1: xxxx
                Author response: xxxxx

                Concern #2: xxxx
                Author response: xxxxx
                ...

                #2 reviewer
                Concern #1: xxxx
                Author response: xxxxx

                Concern #2: xxxx
                Author response: xxxxx
                ...

                #3 reviewer
                Concern #1: xxxx
                Author response: xxxxx

                Concern #2: xxxx
                Author response: xxxxx
                ...
                
                """.format(self.language)
 
                },
                {"role": "user", "content": input_text},
            ]
                
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=messages,
        )
        result = ''
        for choice in response.choices:
            result += choice.message.content
        print("********"*10)
        print(result)
        print("********"*10)
        print("prompt_token_used:", response.usage.prompt_tokens)
        print("completion_token_used:", response.usage.completion_tokens)
        print("total_token_used:", response.usage.total_tokens)
        print("response_time:", response.response_ms/1000.0, 's')                    
        return result        
                        
    def export_to_markdown(self, text, file_name, mode='w'):
        # 使用markdown模块的convert方法，将文本转换为html格式
        # html = markdown.markdown(text)
        # 打开一个文件，以写入模式
        with open(file_name, mode, encoding="utf-8") as f:
            # 将html格式的内容写入文件
            f.write(text)                    

def main(args):            
    Response1 = Response(args=args)
    Response1.response_by_chatgpt(comment_path=args.comment_path)

    
if __name__ == '__main__':    
    parser = argparse.ArgumentParser()
    parser.add_argument("--comment_path", type=str, default='review_comments.txt', help="path of comment")
    parser.add_argument("--file_format", type=str, default='txt', help="output file format")
    parser.add_argument("--language", type=str, default='en', help="output lauguage, en or zh")
    
    args = parser.parse_args()
    start_time = time.time()
    main(args=args)    
    print("response time:", time.time() - start_time)