113 lines
5.0 KiB
Python
113 lines
5.0 KiB
Python
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
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from toolbox import CatchException, report_execption, write_results_to_file
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from toolbox import update_ui
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def get_meta_information(url, chatbot, history):
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import requests
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import arxiv
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import difflib
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from bs4 import BeautifulSoup
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from toolbox import get_conf
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proxies, = get_conf('proxies')
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headers = {
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'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Safari/537.36',
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}
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# 发送 GET 请求
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response = requests.get(url, proxies=proxies, headers=headers)
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# 解析网页内容
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soup = BeautifulSoup(response.text, "html.parser")
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def string_similar(s1, s2):
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return difflib.SequenceMatcher(None, s1, s2).quick_ratio()
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profile = []
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# 获取所有文章的标题和作者
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for result in soup.select(".gs_ri"):
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title = result.a.text.replace('\n', ' ').replace(' ', ' ')
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author = result.select_one(".gs_a").text
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try:
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citation = result.select_one(".gs_fl > a[href*='cites']").text # 引用次数是链接中的文本，直接取出来
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except:
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citation = 'cited by 0'
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abstract = result.select_one(".gs_rs").text.strip() # 摘要在 .gs_rs 中的文本，需要清除首尾空格
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search = arxiv.Search(
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query = title,
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max_results = 1,
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sort_by = arxiv.SortCriterion.Relevance,
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)
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try:
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paper = next(search.results())
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if string_similar(title, paper.title) > 0.90: # same paper
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abstract = paper.summary.replace('\n', ' ')
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is_paper_in_arxiv = True
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else: # different paper
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abstract = abstract
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is_paper_in_arxiv = False
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paper = next(search.results())
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except:
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abstract = abstract
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is_paper_in_arxiv = False
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print(title)
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print(author)
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print(citation)
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profile.append({
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'title':title,
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'author':author,
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'citation':citation,
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'abstract':abstract,
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'is_paper_in_arxiv':is_paper_in_arxiv,
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})
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chatbot[-1] = [chatbot[-1][0], title + f'\n\n是否在arxiv中（不在arxiv中无法获取完整摘要）:{is_paper_in_arxiv}\n\n' + abstract]
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yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
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return profile
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@CatchException
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def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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# 基本信息：功能、贡献者
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chatbot.append([
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"函数插件功能？",
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"分析用户提供的谷歌学术（google scholar）搜索页面中，出现的所有文章: binary-husky，插件初始化中..."])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# 尝试导入依赖，如果缺少依赖，则给出安装建议
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try:
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import arxiv
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import math
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from bs4 import BeautifulSoup
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except:
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report_execption(chatbot, history,
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a = f"解析项目: {txt}",
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b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4 arxiv```。")
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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# 清空历史，以免输入溢出
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history = []
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meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
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batchsize = 5
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for batch in range(math.ceil(len(meta_paper_info_list)/batchsize)):
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if len(meta_paper_info_list[:batchsize]) > 0:
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i_say = "下面是一些学术文献的数据，提取出以下内容：" + \
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"1、英文题目；2、中文题目翻译；3、作者；4、arxiv公开（is_paper_in_arxiv）；4、引用数量（cite）；5、中文摘要翻译。" + \
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f"以下是信息源：{str(meta_paper_info_list[:batchsize])}"
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inputs_show_user = f"请分析此页面中出现的所有文章：{txt}，这是第{batch+1}批"
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gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
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inputs=i_say, inputs_show_user=inputs_show_user,
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llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
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sys_prompt="你是一个学术翻译，请从数据中提取信息。你必须使用Markdown表格。你必须逐个文献进行处理。"
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)
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history.extend([ f"第{batch+1}批", gpt_say ])
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meta_paper_info_list = meta_paper_info_list[batchsize:]
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chatbot.append(["状态？",
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"已经全部完成，您可以试试让AI写一个Related Works，例如您可以继续输入Write a \"Related Works\" section about \"你搜索的研究领域\" for me."])
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msg = '正常'
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yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
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res = write_results_to_file(history)
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chatbot.append(("完成了吗？", res));
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yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
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