Update app.py

app.py

@@ -1,15 +1,195 @@
+import numpy as np
 import os
-import gradio as gr
-import openai
-
-def gpt(api_key):
-    openai.api_key = api_key
-    completion = openai.ChatCompletion.create(
-     model="gpt-3.5-turbo", 
-     messages=[{"role": "user", "content": "Generate a fake news story"}]
-    )
-    return completion.choices[0].message.content
-
-demo = gr.Interface(fn=gpt, inputs="text", outputs="text")
+import re
+import datetime
+import time
+import openai, tenacity
+import argparse
+import configparser
+import json
+import tiktoken
+from get_paper_from_pdf import Paper
+import gradio
+
+# 定义Reviewer类
+class Reviewer:
+    # 初始化方法，设置属性
+    def __init__(self, api, review_format, paper_pdf, language):
+        self.api = api
+        self.review_format = review_format
+
+        self.language = language
+    
+        self.max_token_num = 4096
+        self.encoding = tiktoken.get_encoding("gpt2")
     
-demo.launch()
+    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):
+        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, total_token_used = self.chat_review(text=text)            
+        return chat_review_text, total_token_used
+            
+
+
+    def stage_1(self, paper):
+        htmls = []
+        text = ''
+        text += 'Title: ' + paper.title + '. '
+        text += 'Abstract: ' + paper.section_texts['Abstract']
+        openai.api_key = self.api
+        messages = [
+            {"role": "system",
+             "content": f"You are a professional reviewer. "
+                        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.api   # 读取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]
+        messages=[
+                {"role": "system", "content": "You are a professional reviewer. Now I will give you a paper. You need to give a complete review opinion according to the following requirements and format:"+ self.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, response.usage.total_tokens        
+                                        
+
+def main(api, review_format, paper_pdf, language):  
+    start_time = time.time()
+    if not api or not review_format or not paper_pdf:
+        return "请输入完整内容！"
+    # 判断PDF文件
+    else:
+        paper_list = [Paper(path=paper_pdf)]
+        # 创建一个Reader对象
+        reviewer1 = Reviewer(api, review_format, paper_pdf, language)
+        # 开始判断是路径还是文件：   
+        comments, total_token_used = reviewer1.review_by_chatgpt(paper_list=paper_list)
+    time_used = time.time() - start_time
+    output2 ="使用token数："+ str(total_token_used)+"\n花费时间："+ str(time_used) +"秒"
+    return comments, output2
+        
+
+
+########################################################################################################    
+# 标题
+title = "ChatReviewer"
+# 描述
+description = '''<div align='left'>
+ChatReviewer是一款基于ChatGPT-3.5的API开发的论文自动评审AI助手。其用途如下：
+
+1.对论文进行批量总结和评论，提高科研人员的文献阅读和理解的效率。
+
+2.对自己的论文进行评估，根据ChatReviewer生成的审稿意见进行查漏补缺，进一步提高自己的论文质量。
+
+3.辅助论文审稿，给出参考的审稿意见，提高审稿效率和审稿质量。（⭐️禁止复制粘贴！）
+
+⭐️⭐️⭐️ 声明：请对审稿的论文负责，不要直接复制粘贴ChatReviewer生成的任何审稿意见！！！
+</div>
+'''
+
+# 创建Gradio界面
+inp = [gradio.inputs.Textbox(label="请输入你的API-key(sk开头的字符串)",
+                          default="",
+                          type='password'),
+    gradio.inputs.Textbox(
+        label="请输入特定的审稿格式",
+        default="""* 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)
+*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
+"""
+    ),
+    gradio.inputs.File(label="请上传论文PDF(必填)"),
+    gradio.inputs.Radio(choices=["English", "Chinese"],
+                        default="English",
+                        label="选择输出语言"),
+]
+
+chat_reviewer_gui = gradio.Interface(fn=main,
+                                 inputs=inp,
+                                 outputs = [gradio.Textbox(lines=20, label="评审结果"), gradio.Textbox(lines=2, label="资源统计")],
+                                 title=title,
+                                 description=description)
+
+# Start server
+chat_reviewer_gui .launch(quiet=True, show_api=False)