app.py

import numpy as np
import os
import re
import datetime
import arxiv
import openai, tenacity
import base64, requests
import argparse
import configparser
import fitz, io, os
from PIL import Image
import gradio
import markdown
import json
import tiktoken

def parse_text(text):
    lines = text.split("\n")
    for i,line in enumerate(lines):
        if "```" in line:
            items = line.split('`')
            if items[-1]:
                lines[i] = f'<pre><code class="{items[-1]}">'
            else:
                lines[i] = f'</code></pre>'
        else:
            if i>0:
                line = line.replace("<", "&lt;")
                line = line.replace(">", "&gt;")
                lines[i] = '<br/>'+line.replace(" ", "&nbsp;")
    return "".join(lines)

def get_response(system, context, myKey, raw = False):
    openai.api_key = myKey
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[system, *context],
    )
    openai.api_key = ""
    if raw:
        return response
    else:
        message = response["choices"][0]["message"]["content"]
        message_with_stats = f'{message}'
        return message, parse_text(message_with_stats)

def valid_apikey(api_key):
    try:
        get_response({"role": "system", "content": "You are a helpful assistant."}, [{"role": "user", "content": "test"}], api_key)
        return "可用的api-key"
    except:
        return "无效的api-key"

class Paper:
    def __init__(self, path, title='', url='', abs='', authers=[], sl=[]):
        # 初始化函数，根据pdf路径初始化Paper对象                
        self.url =  url           # 文章链接
        self.path = path          # pdf路径
        self.sl = sl
        self.section_names = []   # 段落标题
        self.section_texts = {}   # 段落内容    
        self.abs = abs
        self.title_page = 0
        if title == '':
            self.pdf = fitz.open(self.path) # pdf文档
            self.title = self.get_title()
            self.parse_pdf()            
        else:
            self.title = title
        self.authers = authers        
        self.roman_num = ["I", "II", 'III', "IV", "V", "VI", "VII", "VIII", "IIX", "IX", "X"]
        self.digit_num = [str(d+1) for d in range(10)]
        self.first_image = ''
        
    def parse_pdf(self):
        self.pdf = fitz.open(self.path) # pdf文档
        self.text_list = [page.get_text() for page in self.pdf]
        self.all_text = ' '.join(self.text_list)
        self.section_page_dict = self._get_all_page_index() # 段落与页码的对应字典
        print("section_page_dict", self.section_page_dict)
        self.section_text_dict = self._get_all_page() # 段落与内容的对应字典
        self.section_text_dict.update({"title": self.title})
        self.section_text_dict.update({"paper_info": self.get_paper_info()})
        self.pdf.close()     
        
    def get_paper_info(self):
        first_page_text = self.pdf[self.title_page].get_text()
        if "Abstract" in self.section_text_dict.keys():
            abstract_text = self.section_text_dict['Abstract']
        else:
            abstract_text = self.abs
        introduction_text = self.section_text_dict['Introduction']
        first_page_text = first_page_text.replace(abstract_text, "").replace(introduction_text, "")
        return first_page_text
        
    def get_image_path(self, image_path=''):
        """
        将PDF中的第一张图保存到image.png里面，存到本地目录，返回文件名称，供gitee读取
        :param filename: 图片所在路径，"C:\\Users\\Administrator\\Desktop\\nwd.pdf"
        :param image_path: 图片提取后的保存路径
        :return:
        """
        # open file
        max_size = 0
        image_list = []
        with fitz.Document(self.path) as my_pdf_file:
            # 遍历所有页面
            for page_number in range(1, len(my_pdf_file) + 1):
                # 查看独立页面
                page = my_pdf_file[page_number - 1]
                # 查看当前页所有图片
                images = page.get_images()                
                # 遍历当前页面所有图片
                for image_number, image in enumerate(page.get_images(), start=1):           
                    # 访问图片xref
                    xref_value = image[0]
                    # 提取图片信息
                    base_image = my_pdf_file.extract_image(xref_value)
                    # 访问图片
                    image_bytes = base_image["image"]
                    # 获取图片扩展名
                    ext = base_image["ext"]
                    # 加载图片
                    image = Image.open(io.BytesIO(image_bytes))
                    image_size = image.size[0] * image.size[1]
                    if image_size > max_size:
                        max_size = image_size
                    image_list.append(image)
        for image in image_list:                            
            image_size = image.size[0] * image.size[1]
            if image_size == max_size:                
                image_name = f"image.{ext}"
                im_path = os.path.join(image_path, image_name)
                print("im_path:", im_path)
                
                max_pix = 480
                origin_min_pix = min(image.size[0], image.size[1])
                
                if image.size[0] > image.size[1]:
                    min_pix = int(image.size[1] * (max_pix/image.size[0]))
                    newsize = (max_pix, min_pix)
                else:
                    min_pix = int(image.size[0] * (max_pix/image.size[1]))
                    newsize = (min_pix, max_pix)
                image = image.resize(newsize)
                
                image.save(open(im_path, "wb"))
                return im_path, ext
        return None, None
    
    # 定义一个函数，根据字体的大小，识别每个章节名称，并返回一个列表
    def get_chapter_names(self,):
        # # 打开一个pdf文件
        doc = fitz.open(self.path) # pdf文档        
        text_list = [page.get_text() for page in doc]
        all_text = ''
        for text in text_list:
            all_text += text
        # # 创建一个空列表，用于存储章节名称
        chapter_names = []
        for line in all_text.split('\n'):
            line_list = line.split(' ')
            if '.' in line:
                point_split_list = line.split('.')
                space_split_list = line.split(' ')
                if 1 < len(space_split_list) < 5:
                    if 1 < len(point_split_list) < 5 and (point_split_list[0] in self.roman_num or point_split_list[0] in self.digit_num):
                        print("line:", line)
                        chapter_names.append(line)        
        
        return chapter_names
        
    def get_title(self):
        doc = self.pdf # 打开pdf文件
        max_font_size = 0 # 初始化最大字体大小为0
        max_string = "" # 初始化最大字体大小对应的字符串为空
        max_font_sizes = [0]
        for page_index, page in enumerate(doc): # 遍历每一页
            text = page.get_text("dict") # 获取页面上的文本信息
            blocks = text["blocks"] # 获取文本块列表
            for block in blocks: # 遍历每个文本块
                if block["type"] == 0 and len(block['lines']): # 如果是文字类型
                    if len(block["lines"][0]["spans"]):
                        font_size = block["lines"][0]["spans"][0]["size"] # 获取第一行第一段文字的字体大小            
                        max_font_sizes.append(font_size)
                        if font_size > max_font_size: # 如果字体大小大于当前最大值
                            max_font_size = font_size # 更新最大值
                            max_string = block["lines"][0]["spans"][0]["text"] # 更新最大值对应的字符串
        max_font_sizes.sort()                
        print("max_font_sizes", max_font_sizes[-10:])
        cur_title = ''
        for page_index, page in enumerate(doc): # 遍历每一页
            text = page.get_text("dict") # 获取页面上的文本信息
            blocks = text["blocks"] # 获取文本块列表
            for block in blocks: # 遍历每个文本块
                if block["type"] == 0 and len(block['lines']): # 如果是文字类型
                    if len(block["lines"][0]["spans"]):
                        cur_string = block["lines"][0]["spans"][0]["text"] # 更新最大值对应的字符串
                        font_flags = block["lines"][0]["spans"][0]["flags"] # 获取第一行第一段文字的字体特征
                        font_size = block["lines"][0]["spans"][0]["size"] # 获取第一行第一段文字的字体大小                         
                        # print(font_size)
                        if abs(font_size - max_font_sizes[-1]) < 0.3 or abs(font_size - max_font_sizes[-2]) < 0.3:                        
                            # print("The string is bold.", max_string, "font_size:", font_size, "font_flags:", font_flags)                            
                            if len(cur_string) > 4 and "arXiv" not in cur_string:                            
                                # print("The string is bold.", max_string, "font_size:", font_size, "font_flags:", font_flags) 
                                if cur_title == ''    :
                                    cur_title += cur_string                       
                                else:
                                    cur_title += ' ' + cur_string                       
                            self.title_page = page_index

        title = cur_title.replace('\n', ' ')                        
        return title

    def _get_all_page_index(self):
        # 定义需要寻找的章节名称列表
        section_list = self.sl
        # 初始化一个字典来存储找到的章节和它们在文档中出现的页码
        section_page_dict = {}
        # 遍历每一页文档
        for page_index, page in enumerate(self.pdf):
            # 获取当前页面的文本内容
            cur_text = page.get_text()
            # 遍历需要寻找的章节名称列表
            for section_name in section_list:
                # 将章节名称转换成大写形式
                section_name_upper = section_name.upper()
                # 如果当前页面包含"Abstract"这个关键词
                if "Abstract" == section_name and section_name in cur_text:
                    # 将"Abstract"和它所在的页码加入字典中
                    section_page_dict[section_name] = page_index
                # 如果当前页面包含章节名称，则将章节名称和它所在的页码加入字典中
                else:
                    if section_name + '\n' in cur_text:
                        section_page_dict[section_name] = page_index
                    elif section_name_upper + '\n' in cur_text:
                        section_page_dict[section_name] = page_index
        # 返回所有找到的章节名称及它们在文档中出现的页码
        return section_page_dict

    def _get_all_page(self):
        """
        获取PDF文件中每个页面的文本信息，并将文本信息按照章节组织成字典返回。
        Returns:
            section_dict (dict): 每个章节的文本信息字典，key为章节名，value为章节文本。
        """
        text = ''
        text_list = []
        section_dict = {}
        
        # 再处理其他章节：
        text_list = [page.get_text() for page in self.pdf]
        for sec_index, sec_name in enumerate(self.section_page_dict):
            print(sec_index, sec_name, self.section_page_dict[sec_name])
            if sec_index <= 0 and self.abs:
                continue
            else:
                # 直接考虑后面的内容：
                start_page = self.section_page_dict[sec_name]
                if sec_index < len(list(self.section_page_dict.keys()))-1:
                    end_page = self.section_page_dict[list(self.section_page_dict.keys())[sec_index+1]]
                else:
                    end_page = len(text_list)
                print("start_page, end_page:", start_page, end_page)
                cur_sec_text = ''
                if end_page - start_page == 0:
                    if sec_index < len(list(self.section_page_dict.keys()))-1:
                        next_sec = list(self.section_page_dict.keys())[sec_index+1]
                        if text_list[start_page].find(sec_name) == -1:
                            start_i = text_list[start_page].find(sec_name.upper())
                        else:
                            start_i = text_list[start_page].find(sec_name)
                        if text_list[start_page].find(next_sec) == -1:
                            end_i = text_list[start_page].find(next_sec.upper())
                        else:
                            end_i = text_list[start_page].find(next_sec)                        
                        cur_sec_text += text_list[start_page][start_i:end_i]
                else:
                    for page_i in range(start_page, end_page):                    
#                         print("page_i:", page_i)
                        if page_i == start_page:
                            if text_list[start_page].find(sec_name) == -1:
                                start_i = text_list[start_page].find(sec_name.upper())
                            else:
                                start_i = text_list[start_page].find(sec_name)
                            cur_sec_text += text_list[page_i][start_i:]
                        elif page_i < end_page:
                            cur_sec_text += text_list[page_i]
                        elif page_i == end_page:
                            if sec_index < len(list(self.section_page_dict.keys()))-1:
                                next_sec = list(self.section_page_dict.keys())[sec_index+1]
                                if text_list[start_page].find(next_sec) == -1:
                                    end_i = text_list[start_page].find(next_sec.upper())
                                else:
                                    end_i = text_list[start_page].find(next_sec)  
                                cur_sec_text += text_list[page_i][:end_i]
                section_dict[sec_name] = cur_sec_text.replace('-\n', '').replace('\n', ' ')
        return section_dict

# 定义Reader类
class Reader:
    # 初始化方法，设置属性
    def __init__(self, key_word='', query='', filter_keys='', 
                 root_path='./',
                 gitee_key='',
                 sort=arxiv.SortCriterion.SubmittedDate, user_name='defualt', language='cn', key='', model_name="gpt-3.5-turbo", p=1.0, temperature=1.0):
        self.key = str(key) # OpenAI key
        self.user_name = user_name # 读者姓名
        self.key_word = key_word # 读者感兴趣的关键词
        self.query = query # 读者输入的搜索查询
        self.sort = sort # 读者选择的排序方式
        self.language = language # 读者选择的语言        
        self.filter_keys = filter_keys # 用于在摘要中筛选的关键词
        self.root_path = root_path
        self.file_format = 'md' # or 'txt'，如果为图片，则必须为'md'
        self.save_image = False
        if self.save_image:
            self.gitee_key = self.config.get('Gitee', 'api')
        else:
            self.gitee_key = ''
        self.max_token_num = 4096
        self.encoding = tiktoken.get_encoding("gpt2")
                
    def get_arxiv(self, max_results=30):
        search = arxiv.Search(query=self.query,
                              max_results=max_results,                              
                              sort_by=self.sort,
                              sort_order=arxiv.SortOrder.Descending,
                              )       
        return search
     
    def filter_arxiv(self, max_results=30):
        search = self.get_arxiv(max_results=max_results)
        print("all search:")
        for index, result in enumerate(search.results()):
            print(index, result.title, result.updated)
            
        filter_results = []   
        filter_keys = self.filter_keys
        
        print("filter_keys:", self.filter_keys)
        # 确保每个关键词都能在摘要中找到，才算是目标论文
        for index, result in enumerate(search.results()):
            abs_text = result.summary.replace('-\n', '-').replace('\n', ' ')
            meet_num = 0
            for f_key in filter_keys.split(" "):
                if f_key.lower() in abs_text.lower():
                    meet_num += 1
            if meet_num == len(filter_keys.split(" ")):
                filter_results.append(result)
                # break
        print("filter_results:", len(filter_results))
        print("filter_papers:")
        for index, result in enumerate(filter_results):
            print(index, result.title, result.updated)
        return filter_results
    
    def validateTitle(self, title):
        # 将论文的乱七八糟的路径格式修正
        rstr = r"[\/\\\:\*\?\"\<\>\|]" # '/ \ : * ? " < > |'
        new_title = re.sub(rstr, "_", title) # 替换为下划线
        return new_title

    def download_pdf(self, filter_results):
        # 先创建文件夹
        date_str = str(datetime.datetime.now())[:13].replace(' ', '-')        
        key_word = str(self.key_word.replace(':', ' '))        
        path = self.root_path  + 'pdf_files/' + self.query.replace('au: ', '').replace('title: ', '').replace('ti: ', '').replace(':', ' ')[:25] + '-' + date_str
        try:
            os.makedirs(path)
        except:
            pass
        print("All_paper:", len(filter_results))
        # 开始下载：
        paper_list = []
        for r_index, result in enumerate(filter_results):
            try:
                title_str = self.validateTitle(result.title)
                pdf_name = title_str+'.pdf'
                # result.download_pdf(path, filename=pdf_name)
                self.try_download_pdf(result, path, pdf_name)
                paper_path = os.path.join(path, pdf_name)
                print("paper_path:", paper_path)
                paper = Paper(path=paper_path,
                              url=result.entry_id,
                              title=result.title,
                              abs=result.summary.replace('-\n', '-').replace('\n', ' '),
                              authers=[str(aut) for aut in result.authors],
                              )
                # 下载完毕，开始解析：
                paper.parse_pdf()
                paper_list.append(paper)
            except Exception as e:
                print("download_error:", e)
                pass
        return paper_list
    
    @tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, min=4, max=10),
                    stop=tenacity.stop_after_attempt(5),
                    reraise=True)
    def try_download_pdf(self, result, path, pdf_name):
        result.download_pdf(path, filename=pdf_name)
    
    @tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, min=4, max=10),
                    stop=tenacity.stop_after_attempt(5),
                    reraise=True)
    def upload_gitee(self, image_path, image_name='', ext='png'):
        """
        上传到码云
        :return:
        """ 
        with open(image_path, 'rb') as f:
            base64_data = base64.b64encode(f.read())
            base64_content = base64_data.decode()
        
        date_str = str(datetime.datetime.now())[:19].replace(':', '-').replace(' ', '-') + '.' + ext
        path = image_name+ '-' +date_str
        
        payload = {
            "access_token": self.gitee_key,
            "owner": self.config.get('Gitee', 'owner'),
            "repo": self.config.get('Gitee', 'repo'),
            "path": self.config.get('Gitee', 'path'),
            "content": base64_content,
            "message": "upload image"
        }
        # 这里需要修改成你的gitee的账户和仓库名，以及文件夹的名字：
        url = f'https://gitee.com/api/v5/repos/'+self.config.get('Gitee', 'owner')+'/'+self.config.get('Gitee', 'repo')+'/contents/'+self.config.get('Gitee', 'path')+'/'+path
        rep = requests.post(url, json=payload).json()
        print("rep:", rep)
        if 'content' in rep.keys():
            image_url = rep['content']['download_url']
        else:
            image_url = r"https://gitee.com/api/v5/repos/"+self.config.get('Gitee', 'owner')+'/'+self.config.get('Gitee', 'repo')+'/contents/'+self.config.get('Gitee', 'path')+'/' + path
            
        return image_url
    
    def summary_with_chat(self, paper_list, key, model_name, p, temperature):
        htmls = []
        utoken = 0
        ctoken = 0
        ttoken = 0
        for paper_index, paper in enumerate(paper_list):
            # 第一步先用title，abs，和introduction进行总结。
            text = ''
            text += 'Title:' + paper.title
            text += 'Url:' + paper.url
            text += 'Abstrat:' + paper.abs
            text += 'Paper_info:' + paper.section_text_dict['paper_info']
            # intro
            text += list(paper.section_text_dict.values())[0]
            #max_token = 2500 * 4
            #text = text[:max_token]
            chat_summary_text, utoken1, ctoken1, ttoken1 = self.chat_summary(text=text, key=str(key), model_name=str(model_name), p=p, temperature=temperature)           
            htmls.append(chat_summary_text)
            
            # TODO 往md文档中插入论文里的像素最大的一张图片，这个方案可以弄的更加智能一些：
            method_key = ''
            for parse_key in paper.section_text_dict.keys():
                if 'method' in parse_key.lower() or 'approach' in parse_key.lower():
                    method_key = parse_key
                    break
                
            if method_key != '':
                text = ''
                method_text = ''
                summary_text = ''
                summary_text += "<summary>" + chat_summary_text
                # methods                
                method_text += paper.section_text_dict[method_key]                   
                text = summary_text + "\n<Methods>:\n" + method_text                 
                chat_method_text, utoken2, ctoken2, ttoken2 = self.chat_method(text=text, key=str(key), model_name=str(model_name), p=p, temperature=temperature)
                htmls.append(chat_method_text)
            else:
                chat_method_text = ''
            htmls.append("\n")
            
            # 第三步总结全文，并打分：
            conclusion_key = ''
            for parse_key in paper.section_text_dict.keys():
                if 'conclu' in parse_key.lower():
                    conclusion_key = parse_key
                    break
            
            text = ''
            conclusion_text = ''
            summary_text = ''
            summary_text += "<summary>" + chat_summary_text + "\n <Method summary>:\n" + chat_method_text            
            if conclusion_key != '':
                # conclusion                
                conclusion_text += paper.section_text_dict[conclusion_key]                                
                text = summary_text + "\n <Conclusion>:\n" + conclusion_text 
            else:
                text = summary_text            
            chat_conclusion_text, utoken3, ctoken3, ttoken3 = self.chat_conclusion(text=text, key=str(key), model_name=str(model_name), p=p, temperature=temperature)
            htmls.append(chat_conclusion_text)
            htmls.append("\n")
            # token统计
            utoken = utoken + utoken1 + utoken2 + utoken3
            ctoken = ctoken + ctoken1 + ctoken2 + ctoken3
            ttoken = ttoken + ttoken1 + ttoken2 + ttoken3
            cost = (ttoken / 1000) * 0.002
            pos_count = {
                        "usage_token_used": str(utoken),
                        "completion_token_used": str(ctoken),
                        "total_token_used": str(ttoken),
                        "cost": str(cost),
                    }
            md_text = "\n".join(htmls)
            
            return markdown.markdown(md_text), pos_count
            
            
    @tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, min=4, max=10),
                    stop=tenacity.stop_after_attempt(5),
                    reraise=True)
    def chat_conclusion(self, text, key, model_name, p, temperature):
        openai.api_key = key
        conclusion_prompt_token = 650        
        text_token = len(self.encoding.encode(text))
        clip_text_index = int(len(text)*(self.max_token_num-conclusion_prompt_token)/text_token)
        clip_text = text[:clip_text_index]   
        
        messages=[
                {"role": "system", "content": "You are a reviewer in the field of ["+self.key_word+"] and you need to critically review this article"},  # chatgpt 角色
                {"role": "assistant", "content": "This is the <summary> and <conclusion> part of an English literature, where <summary> you have already summarized, but <conclusion> part, I need your help to summarize the following questions:"+clip_text},  # 背景知识，可以参考OpenReview的审稿流程
                {"role": "user", "content": """                 
                 8. Make the following summary.Be sure to use Chinese answers (proper nouns need to be marked in English).
                    - (1):What is the significance of this piece of work?
                    - (2):Summarize the strengths and weaknesses of this article in three dimensions: innovation point, performance, and workload.                   
                    .......
                 Follow the format of the output later: 
                 8. Conclusion: \n\n
                    - (1):xxx;\n                     
                    - (2):Innovation point: xxx; Performance: xxx; Workload: xxx;\n                      
                 
                 Be sure to use Chinese answers (proper nouns need to be marked in English), statements as concise and academic as possible, do not repeat the content of the previous <summary>, the value of the use of the original numbers, be sure to strictly follow the format, the corresponding content output to xxx, in accordance with \n line feed, ....... means fill in according to the actual requirements, if not, you can not write.                 
                 """},
            ]
        response = openai.ChatCompletion.create(
            model=model_name,
            # prompt需要用英语替换，少占用token。
            messages=messages,
            temperature=temperature, # What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
            top_p=p # An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
        )
        
        result = ''
        for choice in response.choices:
            result += choice.message.content
        #print("prompt_token_used:", response.usage.prompt_tokens,
        #      "completion_token_used:", response.usage.completion_tokens,
        #      "total_token_used:", response.usage.total_tokens)
        #print("response_time:", response.response_ms/1000.0, 's')
        usage_token = response.usage.prompt_tokens
        com_token = response.usage.completion_tokens
        total_token = response.usage.total_tokens
        
        return result, usage_token, com_token, total_token            
    
    @tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, min=4, max=10),
                    stop=tenacity.stop_after_attempt(5),
                    reraise=True)
    def chat_method(self, text, key, model_name, p, temperature):
        openai.api_key = key
        method_prompt_token = 650        
        text_token = len(self.encoding.encode(text))
        clip_text_index = int(len(text)*(self.max_token_num-method_prompt_token)/text_token)
        clip_text = text[:clip_text_index]        
        messages=[
                {"role": "system", "content": "You are a researcher in the field of ["+self.key_word+"] who is good at summarizing papers using concise statements"},  # chatgpt 角色
                {"role": "assistant", "content": "This is the <summary> and <Method> part of an English document, where <summary> you have summarized, but the <Methods> part, I need your help to read and summarize the following questions."+clip_text},  # 背景知识
                {"role": "user", "content": """                 
                 7. Describe in detail the methodological idea of this article. Be sure to use Chinese answers (proper nouns need to be marked in English). For example, its steps are.
                    - (1):...
                    - (2):...
                    - (3):...
                    - .......
                 Follow the format of the output that follows: 
                 7. Methods: \n\n
                    - (1):xxx;\n 
                    - (2):xxx;\n 
                    - (3):xxx;\n  
                    ....... \n\n     
                 
                 Be sure to use Chinese answers (proper nouns need to be marked in English), statements as concise and academic as possible, do not repeat the content of the previous <summary>, the value of the use of the original numbers, be sure to strictly follow the format, the corresponding content output to xxx, in accordance with \n line feed, ....... means fill in according to the actual requirements, if not, you can not write.                 
                 """},
            ]
        response = openai.ChatCompletion.create(
            model=model_name,
            messages=messages,
            temperature=temperature, # What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
            top_p=p # An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
        )
        
        result = ''
        for choice in response.choices:
            result += choice.message.content
        print("method_result:\n", result)
        #print("prompt_token_used:", response.usage.prompt_tokens,
        #      "completion_token_used:", response.usage.completion_tokens,
        #      "total_token_used:", response.usage.total_tokens)
        #print("response_time:", response.response_ms/1000.0, 's')
        usage_token = response.usage.prompt_tokens
        com_token = response.usage.completion_tokens
        total_token = response.usage.total_tokens
        
        return result, usage_token, com_token, total_token 
    
    @tenacity.retry(wait=tenacity.wait_exponential(multiplier=1, min=4, max=10),
                    stop=tenacity.stop_after_attempt(5),
                    reraise=True)
    def chat_summary(self, text, key, model_name, p, temperature):
        openai.api_key = key
        summary_prompt_token = 1000        
        text_token = len(self.encoding.encode(text))
        clip_text_index = int(len(text)*(self.max_token_num-summary_prompt_token)/text_token)
        clip_text = text[:clip_text_index]
        messages=[
                {"role": "system", "content": "You are a researcher in the field of ["+self.key_word+"] who is good at summarizing papers using concise statements"},
                {"role": "assistant", "content": "This is the title, author, link, abstract and introduction of an English document. I need your help to read and summarize the following questions: "+clip_text},
                {"role": "user", "content": """                 
                 1. Mark the title of the paper (with Chinese translation)
                 2. list all the authors' names (use English)
                 3. mark the first author's affiliation (output Chinese translation only)                 
                 4. mark the keywords of this article (use English)
                 5. link to the paper, Github code link (if available, fill in Github:None if not)
                 6. summarize according to the following four points.Be sure to use Chinese answers (proper nouns need to be marked in English)
                    - (1):What is the research background of this article?
                    - (2):What are the past methods? What are the problems with them? Is the approach well motivated?
                    - (3):What is the research methodology proposed in this paper?
                    - (4):On what task and what performance is achieved by the methods in this paper? Can the performance support their goals?
                 Follow the format of the output that follows:                  
                 1. Title: xxx\n\n
                 2. Authors: xxx\n\n
                 3. Affiliation: xxx\n\n                 
                 4. Keywords: xxx\n\n   
                 5. Urls: xxx or xxx , xxx \n\n      
                 6. Summary: \n\n
                    - (1):xxx;\n 
                    - (2):xxx;\n 
                    - (3):xxx;\n  
                    - (4):xxx.\n\n     
                 
                 Be sure to use Chinese answers (proper nouns need to be marked in English), statements as concise and academic as possible, do not have too much repetitive information, numerical values using the original numbers, be sure to strictly follow the format, the corresponding content output to xxx, in accordance with \n line feed.                 
                 """},
            ]
                
        response = openai.ChatCompletion.create(
            model=model_name,
            messages=messages,
            temperature=temperature, # What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
            top_p=p # An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.
        )
        
        result = ''
        for choice in response.choices:
            result += choice.message.content
        print("summary_result:\n", result)
        #print("prompt_token_used:", response.usage.prompt_tokens,
        #      "completion_token_used:", response.usage.completion_tokens,
        #      "total_token_used:", response.usage.total_tokens)
        #print("response_time:", response.response_ms/1000.0, 's')
        usage_token = response.usage.prompt_tokens
        com_token = response.usage.completion_tokens
        total_token = response.usage.total_tokens
        
        return result, usage_token, com_token, total_token        
            
    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)        

    # 定义一个方法，打印出读者信息
    def show_info(self):        
        print(f"Key word: {self.key_word}")
        print(f"Query: {self.query}")
        print(f"Sort: {self.sort}")                

def upload_pdf(key, text, model_name, p, temperature, file):
    # 检查两个输入都不为空
    if not key or not text or not file:
        return "两个输入都不能为空，请输入字符并上传 PDF 文件！"
    # 判断PDF文件
    #if file and file.name.split(".")[-1].lower() != "pdf":
    #    return '请勿上传非 PDF 文件！'
    else:
        section_list = text.split(',')
        paper_list = [Paper(path=file, sl=section_list)]
        # 创建一个Reader对象
        reader = Reader()
        sum_info, cost = reader.summary_with_chat(paper_list=paper_list, key=key, model_name=model_name, p=p, temperature=temperature)
        return cost, sum_info

api_title = "api-key可用验证"
api_description = '''<div align='left'>

<img src='https://visitor-badge.laobi.icu/badge?page_id=https://huggingface.co/spaces/wangrongsheng/ChatPaper'>

<img align='right' src='https://i.328888.xyz/2023/03/12/vH9dU.png' width="150">

Use ChatGPT to summary the papers.Star our Github [🌟ChatPaper](https://github.com/kaixindelele/ChatPaper) .

💗如果您觉得我们的项目对您有帮助，还请您给我们一些鼓励！💗

🔴请注意：千万不要用于严肃的学术场景，只能用于论文阅读前的初筛！

</div>
'''

api_input = [
    gradio.inputs.Textbox(label="请输入你的api-key(必填)", default="", type='password')
]
api_gui = gradio.Interface(fn=valid_apikey, inputs=api_input, outputs="text", title=api_title, description=api_description)

# 标题
title = "ChatPaper"
# 描述
description = '''<div align='left'>

<img src='https://visitor-badge.laobi.icu/badge?page_id=https://huggingface.co/spaces/wangrongsheng/ChatPaper'>

<img align='right' src='https://i.328888.xyz/2023/03/12/vH9dU.png' width="150">

Use ChatGPT to summary the papers.Star our Github [🌟ChatPaper](https://github.com/kaixindelele/ChatPaper) .

💗如果您觉得我们的项目对您有帮助，还请您给我们一些鼓励！💗

🔴请注意：千万不要用于严肃的学术场景，只能用于论文阅读前的初筛！

</div>
'''
# 创建Gradio界面
ip = [
    gradio.inputs.Textbox(label="请输入你的api-key(必填)", default="", type='password'),
    gradio.inputs.Textbox(label="请输入论文大标题索引(用英文逗号隔开,必填)", default="'Abstract,Introduction,Related Work,Background,Preliminary,Problem Formulation,Methods,Methodology,Method,Approach,Approaches,Materials and Methods,Experiment Settings,Experiment,Experimental Results,Evaluation,Experiments,Results,Findings,Data Analysis,Discussion,Results and Discussion,Conclusion,References'"),
    gradio.inputs.Radio(choices=["gpt-3.5-turbo", "gpt-3.5-turbo-0301"], default="gpt-3.5-turbo", label="Select model"),
    gradio.inputs.Slider(minimum=-0, maximum=1.0, default=1.0, step=0.05, label="Top-p (nucleus sampling)"),
    gradio.inputs.Slider(minimum=-0, maximum=5.0, default=1.0, step=0.1, label="Temperature"),
    gradio.inputs.File(label="请上传论文PDF(必填)")
]

chatpaper_gui = gradio.Interface(fn=upload_pdf, inputs=ip, outputs=["json", "html"], title=title, description=description)

# Start server
gui = gradio.TabbedInterface(interface_list=[api_gui, chatpaper_gui], tab_names=["API-key", "ChatPaper"])
gui.launch(quiet=True,show_api=False)