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ReviewFormat.txt

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+* Overall Review
+Please briefly summarize the main points and contributions of this paper.
+xxx
+
+* Paper Strength 
+Please provide a list of the strengths of this paper, including but not limited to: innovative and practical methodology, insightful empirical findings or in-depth theoretical analysis, well-structured review of relevant literature, and any other factors that may make the paper valuable to readers. (Maximum length: 2,000 characters) 
+(1) xxx
+(2) xxx
+(3) xxx
+...
+
+* Paper Weakness 
+Please provide a numbered list of your main concerns regarding this paper (so authors could respond to the concerns individually). These may include, but are not limited to: inadequate implementation details for reproducing the study, limited evaluation and ablation studies for the proposed method, correctness of the theoretical analysis or experimental results, lack of comparisons or discussions with widely-known baselines in the field, lack of clarity in exposition, or any other factors that may impede the reader's understanding or benefit from the paper. Please kindly refrain from providing a general assessment of the paper's novelty without providing detailed explanations. (Maximum length: 2,000 characters) 
+(1) xxx
+(2) xxx
+(3) xxx
+...
+
+* Questions To Authors And Suggestions For Rebuttal 
+Please provide a numbered list of specific and clear questions that pertain to the details of the proposed method, evaluation setting, or additional results that would aid in supporting the authors' claims. The questions should be formulated in a manner that, after the authors have answered them during the rebuttal, it would enable a more thorough assessment of the paper's quality. (Maximum length: 2,000 characters)
+xxx
+
+*Overall score (1-10)
+The paper is scored on a scale of 1-10, with 10 being the full mark, and 6 stands for borderline accept. Then give the reason for your rating.
+xxx

chat_response.py

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+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 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)
+

chat_reviewer.py

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+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 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.lower()), 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)
+