others/ChatPaper.ipynb
<a href="https://colab.research.google.com/github/kaixindelele/ChatPaper/blob/main/ChatPaper.ipynb" target="_parent"></a>
!pip install arxiv PyMuPDF requests tiktoken tenacity pybase64 Pillow openai markdown gradio
from IPython.core.interactiveshell import InteractiveShell
import numpy as np
import os
import re
import datetime
import arxiv
import openai, tenacity
import base64, requests
import argparse
import configparser
import json
import tiktoken
import fitz, io, os
from PIL import Image
class Paper:
def __init__(self, path, title='', url='', abs='', authers=[]):
# 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.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
# break
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 = ["Abstract",
'Introduction', 'Related Work', 'Background',
"Preliminary", "Problem Formulation",
'Methods', 'Methodology', "Method", 'Approach', 'Approaches',
# exp
"Materials and Methods", "Experiment Settings",
'Experiment', "Experimental Results", "Evaluation", "Experiments",
"Results", 'Findings', 'Data Analysis',
"Discussion", "Results and Discussion", "Conclusion",
'References']
# 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', args=None):
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
if args.language == 'en':
self.language = 'English'
elif args.language == 'zh':
self.language = 'Chinese'
else:
self.language = 'Chinese'
self.filter_keys = filter_keys # 用于在摘要中筛选的关键词
self.root_path = root_path
self.chat_api_list = [args.api_key]
self.cur_api = 0
self.file_format = args.file_format
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)
# 把摘要也打印一下，看看到底是什么妖魔鬼怪！
print("abs_text:", result.summary.replace('-\n', '-').replace('\n', ' '))
print("-"*30)
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("筛选后剩下的论文数量：")
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)
def summary_with_chat(self, paper_list):
htmls = []
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]
chat_summary_text = self.chat_summary(text=text)
htmls.append('## Paper:' + str(paper_index+1))
htmls.append('\n\n\n')
htmls.append(chat_summary_text)
# 第二步总结方法：
# TODO，由于有些文章的方法章节名是算法名，所以简单的通过关键词来筛选，很难获取，后面需要用其他的方案去优化。
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\n<Methods>:\n\n" + method_text
chat_method_text = self.chat_method(text=text)
htmls.append(chat_method_text)
else:
chat_method_text = ''
htmls.append("\n"*4)
# 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\n<Conclusion>:\n\n" + conclusion_text
else:
text = summary_text
chat_conclusion_text = self.chat_conclusion(text=text)
htmls.append(chat_conclusion_text)
htmls.append("\n"*4)
# # 整合成一个文件，打包保存下来。
date_str = str(datetime.datetime.now())[:13].replace(' ', '-')
try:
export_path = os.path.join(self.root_path, 'export')
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)
# file_name = os.path.join(export_path, date_str+'-'+self.validateTitle(paper.title)+".md")
# self.export_to_markdown("\n".join(htmls), file_name=file_name, mode=mode)
htmls = []
@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):
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
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 {} 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 {} 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.
""".format(self.language, self.language)},
]
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
# prompt需要用英语替换，少占用token。
messages=messages,
)
result = ''
for choice in response.choices:
result += choice.message.content
print("conclusion_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')
return result
@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):
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
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 {} 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 {} 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.
""".format(self.language, self.language)},
]
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=messages,
)
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')
return result
@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):
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
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 {} 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 {} 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 {} 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.
""".format(self.language, self.language, self.language)},
]
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=messages,
)
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')
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)
# 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 main(args):
# 创建一个Reader对象，并调用show_info方法
if args.sort == 'Relevance':
sort = arxiv.SortCriterion.Relevance
elif args.sort == 'LastUpdatedDate':
sort = arxiv.SortCriterion.LastUpdatedDate
else:
sort = arxiv.SortCriterion.Relevance
if args.pdf_path:
reader1 = Reader(key_word=args.key_word,
query=args.query,
filter_keys=args.filter_keys,
sort=sort,
args=args
)
reader1.show_info()
# 开始判断是路径还是文件：
paper_list = []
if args.pdf_path.endswith(".pdf"):
paper_list.append(Paper(path=args.pdf_path))
else:
for root, dirs, files in os.walk(args.pdf_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)]
reader1.summary_with_chat(paper_list=paper_list)
else:
reader1 = Reader(key_word=args.key_word,
query=args.query,
filter_keys=args.filter_keys,
sort=sort,
args=args
)
reader1.show_info()
filter_results = reader1.filter_arxiv(max_results=args.max_results)
paper_list = reader1.download_pdf(filter_results)
reader1.summary_with_chat(paper_list=paper_list)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--pdf_path", type=str, default='', help="if none, the bot will download from arxiv with query")
parser.add_argument("--query", type=str, default='all: reinforcement learning', help="the query string, ti: xx, au: xx, all: xx,")
parser.add_argument("--key_word", type=str, default='deep reinforcement learning', help="the key word of user research fields")
parser.add_argument("--filter_keys", type=str, default='reinforcement learning', help="the filter key words, 摘要中每个单词都得有，才会被筛选为目标论文")
parser.add_argument("--max_results", type=int, default=2, help="the maximum number of results")
parser.add_argument("--sort", type=str, default="Relevance", help="another is LastUpdatedDate, and Relevance")
parser.add_argument("--save_image", default=False, help="save image? It takes a minute or two to save a picture! But pretty")
parser.add_argument("--file_format", type=str, default='md', help="导出的文件格式，如果存图片的话，最好是md，如果不是的话，txt的不会乱")
parser.add_argument("--language", type=str, default='zh', help="The other output lauguage is English, is en")
parser.add_argument("--api_key", type=str, default='sk-xxxxxxxxxxxx', help="your openai api key!")
parser.add_argument('-f')
args = parser.parse_args()
import time
start_time = time.time()
main(args=args)
print("summary time:", time.time() - start_time)