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 # 定义Reviewer类 class Reviewer: # 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 validateTitle(self, title): # 修正论文的路径格式 rstr = r"[\/\\\:\*\?\"\<\>\|]" # '/ \ : * ? " < > |' new_title = re.sub(rstr, "_", title) # 替换为下划线 return new_title def review_by_chatgpt(self, paper_list): htmls = [] for paper_index, paper in enumerate(paper_list): sections_of_interest = self.stage_1(paper) # extract the essential parts of the paper text = '' text += 'Title:' + paper.title + '. ' text += 'Abstract: ' + paper.section_texts['Abstract'] intro_title = next((item for item in paper.section_names if 'ntroduction' in item), None) if intro_title is not None: text += 'Introduction: ' + paper.section_texts[intro_title] # Similar for conclusion section conclusion_title = next((item for item in paper.section_names if 'onclusion' in item), None) if conclusion_title is not None: text += 'Conclusion: ' + paper.section_texts[conclusion_title] for heading in sections_of_interest: if heading in paper.section_names: text += heading + ': ' + paper.section_texts[heading] chat_review_text = self.chat_review(text=text) htmls.append('## Paper:' + str(paper_index+1)) htmls.append('\n\n\n') htmls.append(chat_review_text) # 将审稿意见保存起来 date_str = str(datetime.datetime.now())[:13].replace(' ', '-') try: export_path = os.path.join('./', 'output_file') os.makedirs(export_path) except: pass mode = 'w' if paper_index == 0 else 'a' file_name = os.path.join(export_path, date_str+'-'+self.validateTitle(paper.title)+"."+self.file_format) self.export_to_markdown("\n".join(htmls), file_name=file_name, mode=mode) htmls = [] def stage_1(self, paper): htmls = [] text = '' text += 'Title: ' + paper.title + '. ' text += 'Abstract: ' + paper.section_texts['Abstract'] 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 messages = [ {"role": "system", "content": f"You are a professional reviewer in the field of {args.research_fields}. " f"I will give you a paper. You need to review this paper and discuss the novelty and originality of ideas, correctness, clarity, the significance of results, potential impact and quality of the presentation. " f"Due to the length limitations, I am only allowed to provide you the abstract, introduction, conclusion and at most two sections of this paper." f"Now I will give you the title and abstract and the headings of potential sections. " f"You need to reply at most two headings. Then I will further provide you the full information, includes aforementioned sections and at most two sections you called for.\n\n" f"Title: {paper.title}\n\n" f"Abstract: {paper.section_texts['Abstract']}\n\n" f"Potential Sections: {paper.section_names[2:-1]}\n\n" f"Follow the following format to output your choice of sections:" f"{{chosen section 1}}, {{chosen section 2}}\n\n"}, {"role": "user", "content": text}, ] response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=messages, ) result = '' for choice in response.choices: result += choice.message.content print(result) return result.split(',') @tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, min=4, max=10), stop=tenacity.stop_after_attempt(5), reraise=True) def chat_review(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 review_prompt_token = 1000 text_token = len(self.encoding.encode(text)) input_text_index = int(len(text)*(self.max_token_num-review_prompt_token)/text_token) input_text = "This is the paper for your review:" + text[:input_text_index] with open('ReviewFormat.txt', 'r') as file: # 读取特定的审稿格式 review_format = file.read() messages=[ {"role": "system", "content": "You are a professional reviewer in the field of "+args.research_fields+". Now I will give you a paper. You need to give a complete review opinion according to the following requirements and format:"+ review_format +" Please answer in {}.".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): reviewer1 = Reviewer(args=args) # 开始判断是路径还是文件： paper_list = [] if args.paper_path.endswith(".pdf"): paper_list.append(Paper(path=args.paper_path)) else: for root, dirs, files in os.walk(args.paper_path): print("root:", root, "dirs:", dirs, 'files:', files) #当前目录路径 for filename in files: # 如果找到PDF文件，则将其复制到目标文件夹中 if filename.endswith(".pdf"): paper_list.append(Paper(path=os.path.join(root, filename))) print("------------------paper_num: {}------------------".format(len(paper_list))) [print(paper_index, paper_name.path.split('\\')[-1]) for paper_index, paper_name in enumerate(paper_list)] reviewer1.review_by_chatgpt(paper_list=paper_list) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("--paper_path", type=str, default='', help="path of papers") parser.add_argument("--file_format", type=str, default='txt', help="output file format") parser.add_argument("--research_fields", type=str, default='computer science, artificial intelligence and reinforcement learning', help="the research fields of paper") 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("review time:", time.time() - start_time)