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| from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive | |
| from toolbox import CatchException, report_execption, write_results_to_file | |
| def get_meta_information(url, chatbot, history): | |
| import requests | |
| import arxiv | |
| import difflib | |
| from bs4 import BeautifulSoup | |
| from toolbox import get_conf | |
| proxies, = get_conf('proxies') | |
| headers = { | |
| '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', | |
| } | |
| # 发送 GET 请求 | |
| response = requests.get(url, proxies=proxies, headers=headers) | |
| # 解析网页内容 | |
| soup = BeautifulSoup(response.text, "html.parser") | |
| def string_similar(s1, s2): | |
| return difflib.SequenceMatcher(None, s1, s2).quick_ratio() | |
| profile = [] | |
| # 获取所有文章的标题和作者 | |
| for result in soup.select(".gs_ri"): | |
| title = result.a.text.replace('\n', ' ').replace(' ', ' ') | |
| author = result.select_one(".gs_a").text | |
| try: | |
| 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 | |
| except: | |
| citation = 'cited by 0' | |
| 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 | |
| search = arxiv.Search( | |
| query = title, | |
| max_results = 1, | |
| sort_by = arxiv.SortCriterion.Relevance, | |
| ) | |
| paper = next(search.results()) | |
| if string_similar(title, paper.title) > 0.90: # same paper | |
| abstract = paper.summary.replace('\n', ' ') | |
| is_paper_in_arxiv = True | |
| else: # different paper | |
| abstract = abstract | |
| is_paper_in_arxiv = False | |
| paper = next(search.results()) | |
| print(title) | |
| print(author) | |
| print(citation) | |
| profile.append({ | |
| 'title':title, | |
| 'author':author, | |
| 'citation':citation, | |
| 'abstract':abstract, | |
| 'is_paper_in_arxiv':is_paper_in_arxiv, | |
| }) | |
| 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] | |
| msg = "正常" | |
| yield chatbot, [], msg | |
| return profile | |
| def 谷歌检索小助手(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT): | |
| # 基本信息：功能、贡献者 | |
| chatbot.append([ | |
| "函数插件功能？", | |
| "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..."]) | |
| yield chatbot, history, '正常' | |
| # 尝试导入依赖，如果缺少依赖，则给出安装建议 | |
| try: | |
| import arxiv | |
| from bs4 import BeautifulSoup | |
| except: | |
| report_execption(chatbot, history, | |
| a = f"解析项目: {txt}", | |
| 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```。") | |
| yield chatbot, history, '正常' | |
| return | |
| # 清空历史，以免输入溢出 | |
| history = [] | |
| meta_paper_info_list = yield from get_meta_information(txt, chatbot, history) | |
| if len(meta_paper_info_list[:10]) > 0: | |
| i_say = "下面是一些学术文献的数据，请从中提取出以下内容。" + \ | |
| "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。" + \ | |
| f"以下是信息源：{str(meta_paper_info_list[:10])}" | |
| inputs_show_user = f"请分析此页面中出现的所有文章：{txt}" | |
| gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( | |
| inputs=i_say, inputs_show_user=inputs_show_user, | |
| top_p=top_p, temperature=temperature, chatbot=chatbot, history=[], | |
| 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格式。你必须逐个文献进行处理。" | |
| ) | |
| history.extend([ "第一批", gpt_say ]) | |
| meta_paper_info_list = meta_paper_info_list[10:] | |
| chatbot.append(["状态？", "已经全部完成"]) | |
| msg = '正常' | |
| yield chatbot, history, msg | |
| res = write_results_to_file(history) | |
| chatbot.append(("完成了吗？", res)); | |
| yield chatbot, history, msg | |