Compare commits
4
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 binary-husky
|
a528c35d1f | ||
|
binary-husky
|
623b0c83a1 | ||
 Keldos
|
12c36a68ce | ||
|
Keldos
|
998e127b2f |
@@ -36,16 +36,14 @@ https://github.com/polarwinkel/mdtex2html
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自定义快捷键 | 支持自定义快捷键
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配置代理服务器 | 支持配置代理服务器
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模块化设计 | 支持自定义高阶的实验性功能
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自我程序剖析 | [函数插件] 一键读懂本项目的源代码
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程序剖析 | [函数插件] 一键可以剖析其他Python/C++等项目
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读论文 | [函数插件] 一键解读latex论文全文并生成摘要
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arxiv小助手 | [函数插件] 输入url一键翻译摘要+下载论文
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批量注释生成 | [函数插件] 一键批量生成函数注释
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chat分析报告生成 | [函数插件] 运行后自动生成总结汇报
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自我程序剖析 | [实验性功能] 一键读懂本项目的源代码
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程序剖析 | [实验性功能] 一键可以剖析其他Python/C++项目
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读论文 | [实验性功能] 一键解读latex论文全文并生成摘要
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批量注释生成 | [实验性功能] 一键批量生成函数注释
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chat分析报告生成 | [实验性功能] 运行后自动生成总结汇报
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公式显示 | 可以同时显示公式的tex形式和渲染形式
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图片显示 | 可以在markdown中显示图片
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支持GPT输出的markdown表格 | 可以输出支持GPT的markdown表格
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本地大语言模型接口 | 借助[TGUI](https://github.com/oobabooga/text-generation-webui)接入galactica等本地语言模型
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…… | ……
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</div>
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@@ -1,5 +1,5 @@
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# [step 1]>> 例如： API_KEY = "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" （此key无效）
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API_KEY = "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"
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API_KEY = "sk-此处填API密钥"
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# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改
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USE_PROXY = False
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@@ -34,7 +34,7 @@ WEB_PORT = -1
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MAX_RETRY = 2
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# OpenAI模型选择是（gpt4现在只对申请成功的人开放）
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LLM_MODEL = "TGUI:galactica-1.3b@localhost:7860" # "gpt-3.5-turbo"
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LLM_MODEL = "gpt-3.5-turbo"
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# OpenAI的API_URL
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API_URL = "https://api.openai.com/v1/chat/completions"
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@@ -1,187 +0,0 @@
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from predict import predict_no_ui
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from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down, get_conf
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import re, requests, unicodedata, os
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def download_arxiv_(url_pdf):
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if 'arxiv.org' not in url_pdf:
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if ('.' in url_pdf) and ('/' not in url_pdf):
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new_url = 'https://arxiv.org/abs/'+url_pdf
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print('下载编号：', url_pdf, '自动定位：', new_url)
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# download_arxiv_(new_url)
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return download_arxiv_(new_url)
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else:
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print('不能识别的URL！')
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return None
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if 'abs' in url_pdf:
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url_pdf = url_pdf.replace('abs', 'pdf')
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url_pdf = url_pdf + '.pdf'
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url_abs = url_pdf.replace('.pdf', '').replace('pdf', 'abs')
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title, other_info = get_name(_url_=url_abs)
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paper_id = title.split()[0] # '[1712.00559]'
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if '2' in other_info['year']:
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title = other_info['year'] + ' ' + title
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known_conf = ['NeurIPS', 'NIPS', 'Nature', 'Science', 'ICLR', 'AAAI']
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for k in known_conf:
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if k in other_info['comment']:
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title = k + ' ' + title
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download_dir = './gpt_log/arxiv/'
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os.makedirs(download_dir, exist_ok=True)
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title_str = title.replace('?', '？')\
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.replace(':', '：')\
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.replace('\"', '“')\
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.replace('\n', '')\
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.replace(' ', ' ')\
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.replace(' ', ' ')
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requests_pdf_url = url_pdf
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file_path = download_dir+title_str
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# if os.path.exists(file_path):
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# print('返回缓存文件')
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# return './gpt_log/arxiv/'+title_str
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print('下载中')
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proxies, = get_conf('proxies')
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r = requests.get(requests_pdf_url, proxies=proxies)
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with open(file_path, 'wb+') as f:
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f.write(r.content)
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print('下载完成')
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# print('输出下载命令：','aria2c -o \"%s\" %s'%(title_str,url_pdf))
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# subprocess.call('aria2c --all-proxy=\"172.18.116.150:11084\" -o \"%s\" %s'%(download_dir+title_str,url_pdf), shell=True)
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x = "%s %s %s.bib" % (paper_id, other_info['year'], other_info['authors'])
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x = x.replace('?', '？')\
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.replace(':', '：')\
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.replace('\"', '“')\
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.replace('\n', '')\
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.replace(' ', ' ')\
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.replace(' ', ' ')
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return './gpt_log/arxiv/'+title_str, other_info
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def get_name(_url_):
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import os
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from bs4 import BeautifulSoup
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print('正在获取文献名！')
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print(_url_)
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# arxiv_recall = {}
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# if os.path.exists('./arxiv_recall.pkl'):
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# with open('./arxiv_recall.pkl', 'rb') as f:
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# arxiv_recall = pickle.load(f)
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# if _url_ in arxiv_recall:
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# print('在缓存中')
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# return arxiv_recall[_url_]
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proxies, = get_conf('proxies')
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res = requests.get(_url_, proxies=proxies)
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bs = BeautifulSoup(res.text, 'html.parser')
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other_details = {}
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# get year
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try:
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year = bs.find_all(class_='dateline')[0].text
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year = re.search(r'(\d{4})', year, re.M | re.I).group(1)
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other_details['year'] = year
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abstract = bs.find_all(class_='abstract mathjax')[0].text
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other_details['abstract'] = abstract
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except:
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other_details['year'] = ''
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print('年份获取失败')
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# get author
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try:
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authors = bs.find_all(class_='authors')[0].text
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authors = authors.split('Authors:')[1]
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other_details['authors'] = authors
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except:
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other_details['authors'] = ''
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print('authors获取失败')
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# get comment
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try:
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comment = bs.find_all(class_='metatable')[0].text
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real_comment = None
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for item in comment.replace('\n', ' ').split(' '):
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if 'Comments' in item:
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real_comment = item
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if real_comment is not None:
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other_details['comment'] = real_comment
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else:
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other_details['comment'] = ''
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except:
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other_details['comment'] = ''
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print('年份获取失败')
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title_str = BeautifulSoup(
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res.text, 'html.parser').find('title').contents[0]
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print('获取成功：', title_str)
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# arxiv_recall[_url_] = (title_str+'.pdf', other_details)
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# with open('./arxiv_recall.pkl', 'wb') as f:
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# pickle.dump(arxiv_recall, f)
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return title_str+'.pdf', other_details
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@CatchException
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def 下载arxiv论文并翻译摘要(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
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CRAZY_FUNCTION_INFO = "下载arxiv论文并翻译摘要，作者 binary-husky。正在提取摘要并下载PDF文档……"
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raise RuntimeError()
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import glob
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import os
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# 基本信息：功能、贡献者
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chatbot.append(["函数插件功能？", CRAZY_FUNCTION_INFO])
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yield chatbot, history, '正常'
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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 pdfminer, bs4
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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 pdfminer beautifulsoup4```。")
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yield chatbot, history, '正常'
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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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# 提取摘要，下载PDF文档
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try:
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pdf_path, info = download_arxiv_(txt)
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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"下载pdf文件未成功")
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yield chatbot, history, '正常'
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return
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# 翻译摘要等
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i_say = f"请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。材料如下：{str(info)}"
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i_say_show_user = f'请你阅读以下学术论文相关的材料，提取摘要，翻译为中文。论文：{pdf_path}'
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chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
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yield chatbot, history, '正常'
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msg = '正常'
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# ** gpt request **
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gpt_say = yield from predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[]) # 带超时倒计时
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chatbot[-1] = (i_say_show_user, gpt_say)
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history.append(i_say_show_user); history.append(gpt_say)
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yield chatbot, history, msg
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# 写入文件
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import shutil
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# 重置文件的创建时间
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shutil.copyfile(pdf_path, pdf_path.replace('.pdf', '.autodownload.pdf')); os.remove(pdf_path)
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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 chatbot, history, msg
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@@ -148,20 +148,3 @@ def 解析一个C项目(txt, top_p, temperature, chatbot, history, systemPromptT
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return
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yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
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@CatchException
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def 解析一个Golang项目(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
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history = [] # 清空历史，以免输入溢出
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import glob, os
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if os.path.exists(txt):
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project_folder = txt
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else:
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if txt == "": txt = '空空如也的输入栏'
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report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
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yield chatbot, history, '正常'
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return
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file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.go', recursive=True)]
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if len(file_manifest) == 0:
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report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何golang文件: {txt}")
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yield chatbot, history, '正常'
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return
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yield from 解析源代码(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
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@@ -14,7 +14,6 @@ def get_crazy_functionals():
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from crazy_functions.解析项目源代码 import 解析一个Python项目
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from crazy_functions.解析项目源代码 import 解析一个C项目的头文件
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from crazy_functions.解析项目源代码 import 解析一个C项目
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from crazy_functions.解析项目源代码 import 解析一个Golang项目
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from crazy_functions.高级功能函数模板 import 高阶功能模板函数
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from crazy_functions.代码重写为全英文_多线程 import 全项目切换英文
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@@ -36,11 +35,6 @@ def get_crazy_functionals():
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"AsButton": False, # 加入下拉菜单中
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"Function": 解析一个C项目
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},
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"解析整个Go项目": {
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"Color": "stop", # 按钮颜色
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"AsButton": False, # 加入下拉菜单中
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"Function": 解析一个Golang项目
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},
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"读Tex论文写摘要": {
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"Color": "stop", # 按钮颜色
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"Function": 读文章写摘要
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@@ -11,9 +11,8 @@ proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION, CHATBOT_HEIGHT =
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PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
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if not AUTHENTICATION: AUTHENTICATION = None
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title = "ChatGPT 学术优化" if LLM_MODEL.startswith('gpt') else "ChatGPT / LLM 学术优化"
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initial_prompt = "Serve me as a writing and programming assistant."
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title_html = f"<h1 align=\"center\">{title}</h1>"
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title_html = """<h1 align="center">ChatGPT 学术优化</h1>"""
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# 问询记录, python 版本建议3.9+（越新越好）
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import logging
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@@ -141,5 +140,5 @@ def auto_opentab_delay():
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threading.Thread(target=open, name="open-browser", daemon=True).start()
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auto_opentab_delay()
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demo.title = title
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demo.title = "ChatGPT 学术优化"
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demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", share=True, server_port=PORT, auth=AUTHENTICATION)
|
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+2
-9
@@ -112,7 +112,8 @@ def predict_no_ui_long_connection(inputs, top_p, temperature, history=[], sys_pr
|
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return result
|
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|
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|
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def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='',
|
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stream = True, additional_fn=None):
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"""
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发送至chatGPT，流式获取输出。
|
||||
用于基础的对话功能。
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||||
@@ -243,11 +244,3 @@ def generate_payload(inputs, top_p, temperature, history, system_prompt, stream)
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return headers,payload
|
||||
|
||||
|
||||
if not LLM_MODEL.startswith('gpt'):
|
||||
# 函数重载到另一个文件
|
||||
from request_llm.bridge_tgui import predict_tgui, predict_tgui_no_ui
|
||||
predict = predict_tgui
|
||||
predict_no_ui = predict_tgui_no_ui
|
||||
predict_no_ui_long_connection = predict_tgui_no_ui
|
||||
|
||||
|
||||
@@ -1,36 +0,0 @@
|
||||
# 如何使用其他大语言模型
|
||||
|
||||
## 1. 先运行text-generation
|
||||
``` sh
|
||||
# 下载模型（ text-generation 这么牛的项目，别忘了给人家star ）
|
||||
git clone https://github.com/oobabooga/text-generation-webui.git
|
||||
|
||||
# 安装text-generation的额外依赖
|
||||
pip install accelerate bitsandbytes flexgen gradio llamacpp markdown numpy peft requests rwkv safetensors sentencepiece tqdm datasets git+https://github.com/huggingface/transformers
|
||||
|
||||
# 切换路径
|
||||
cd text-generation-webui
|
||||
|
||||
# 下载模型
|
||||
python download-model.py facebook/galactica-1.3b
|
||||
# 其他可选如 facebook/opt-1.3b
|
||||
# facebook/galactica-6.7b
|
||||
# facebook/galactica-120b
|
||||
# facebook/pygmalion-1.3b 等
|
||||
# 详情见 https://github.com/oobabooga/text-generation-webui
|
||||
|
||||
# 启动text-generation，注意把模型的斜杠改成下划线
|
||||
python server.py --cpu --listen --listen-port 7860 --model facebook_galactica-1.3b
|
||||
```
|
||||
|
||||
## 2. 修改config.py
|
||||
``` sh
|
||||
# LLM_MODEL格式较复杂 TGUI:[模型]@[ws地址]:[ws端口] , 端口要和上面给定的端口一致
|
||||
LLM_MODEL = "TGUI:galactica-1.3b@localhost:7860"
|
||||
```
|
||||
|
||||
## 3. 运行！
|
||||
``` sh
|
||||
cd chatgpt-academic
|
||||
python main.py
|
||||
```
|
||||
@@ -1,167 +0,0 @@
|
||||
'''
|
||||
Contributed by SagsMug. Modified by binary-husky
|
||||
https://github.com/oobabooga/text-generation-webui/pull/175
|
||||
'''
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import random
|
||||
import string
|
||||
import websockets
|
||||
import logging
|
||||
import time
|
||||
import threading
|
||||
import importlib
|
||||
from toolbox import get_conf
|
||||
LLM_MODEL, = get_conf('LLM_MODEL')
|
||||
|
||||
# "TGUI:galactica-1.3b@localhost:7860"
|
||||
model_name, addr_port = LLM_MODEL.split('@')
|
||||
assert ':' in addr_port, "LLM_MODEL 格式不正确！" + LLM_MODEL
|
||||
addr, port = addr_port.split(':')
|
||||
|
||||
def random_hash():
|
||||
letters = string.ascii_lowercase + string.digits
|
||||
return ''.join(random.choice(letters) for i in range(9))
|
||||
|
||||
async def run(context, max_token=512):
|
||||
params = {
|
||||
'max_new_tokens': max_token,
|
||||
'do_sample': True,
|
||||
'temperature': 0.5,
|
||||
'top_p': 0.9,
|
||||
'typical_p': 1,
|
||||
'repetition_penalty': 1.05,
|
||||
'encoder_repetition_penalty': 1.0,
|
||||
'top_k': 0,
|
||||
'min_length': 0,
|
||||
'no_repeat_ngram_size': 0,
|
||||
'num_beams': 1,
|
||||
'penalty_alpha': 0,
|
||||
'length_penalty': 1,
|
||||
'early_stopping': True,
|
||||
'seed': -1,
|
||||
}
|
||||
session = random_hash()
|
||||
|
||||
async with websockets.connect(f"ws://{addr}:{port}/queue/join") as websocket:
|
||||
while content := json.loads(await websocket.recv()):
|
||||
#Python3.10 syntax, replace with if elif on older
|
||||
if content["msg"] == "send_hash":
|
||||
await websocket.send(json.dumps({
|
||||
"session_hash": session,
|
||||
"fn_index": 12
|
||||
}))
|
||||
elif content["msg"] == "estimation":
|
||||
pass
|
||||
elif content["msg"] == "send_data":
|
||||
await websocket.send(json.dumps({
|
||||
"session_hash": session,
|
||||
"fn_index": 12,
|
||||
"data": [
|
||||
context,
|
||||
params['max_new_tokens'],
|
||||
params['do_sample'],
|
||||
params['temperature'],
|
||||
params['top_p'],
|
||||
params['typical_p'],
|
||||
params['repetition_penalty'],
|
||||
params['encoder_repetition_penalty'],
|
||||
params['top_k'],
|
||||
params['min_length'],
|
||||
params['no_repeat_ngram_size'],
|
||||
params['num_beams'],
|
||||
params['penalty_alpha'],
|
||||
params['length_penalty'],
|
||||
params['early_stopping'],
|
||||
params['seed'],
|
||||
]
|
||||
}))
|
||||
elif content["msg"] == "process_starts":
|
||||
pass
|
||||
elif content["msg"] in ["process_generating", "process_completed"]:
|
||||
yield content["output"]["data"][0]
|
||||
# You can search for your desired end indicator and
|
||||
# stop generation by closing the websocket here
|
||||
if (content["msg"] == "process_completed"):
|
||||
break
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def predict_tgui(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='', stream = True, additional_fn=None):
|
||||
"""
|
||||
发送至chatGPT，流式获取输出。
|
||||
用于基础的对话功能。
|
||||
inputs 是本次问询的输入
|
||||
top_p, temperature是chatGPT的内部调优参数
|
||||
history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
|
||||
chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
|
||||
additional_fn代表点击的哪个按钮，按钮见functional.py
|
||||
"""
|
||||
if additional_fn is not None:
|
||||
import functional
|
||||
importlib.reload(functional) # 热更新prompt
|
||||
functional = functional.get_functionals()
|
||||
if "PreProcess" in functional[additional_fn]: inputs = functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
|
||||
inputs = functional[additional_fn]["Prefix"] + inputs + functional[additional_fn]["Suffix"]
|
||||
|
||||
raw_input = "What I would like to say is the following: " + inputs
|
||||
logging.info(f'[raw_input] {raw_input}')
|
||||
history.extend([inputs, ""])
|
||||
chatbot.append([inputs, ""])
|
||||
yield chatbot, history, "等待响应"
|
||||
|
||||
prompt = inputs
|
||||
tgui_say = ""
|
||||
|
||||
mutable = ["", time.time()]
|
||||
def run_coorotine(mutable):
|
||||
async def get_result(mutable):
|
||||
async for response in run(prompt):
|
||||
print(response[len(mutable[0]):])
|
||||
mutable[0] = response
|
||||
if (time.time() - mutable[1]) > 3:
|
||||
print('exit when no listener')
|
||||
break
|
||||
asyncio.run(get_result(mutable))
|
||||
|
||||
thread_listen = threading.Thread(target=run_coorotine, args=(mutable,), daemon=True)
|
||||
thread_listen.start()
|
||||
|
||||
while thread_listen.is_alive():
|
||||
time.sleep(1)
|
||||
mutable[1] = time.time()
|
||||
# Print intermediate steps
|
||||
if tgui_say != mutable[0]:
|
||||
tgui_say = mutable[0]
|
||||
history[-1] = tgui_say
|
||||
chatbot[-1] = (history[-2], history[-1])
|
||||
yield chatbot, history, "status_text"
|
||||
|
||||
logging.info(f'[response] {tgui_say}')
|
||||
|
||||
|
||||
|
||||
def predict_tgui_no_ui(inputs, top_p, temperature, history=[], sys_prompt=""):
|
||||
raw_input = "What I would like to say is the following: " + inputs
|
||||
prompt = inputs
|
||||
tgui_say = ""
|
||||
mutable = ["", time.time()]
|
||||
def run_coorotine(mutable):
|
||||
async def get_result(mutable):
|
||||
async for response in run(prompt, max_token=20):
|
||||
print(response[len(mutable[0]):])
|
||||
mutable[0] = response
|
||||
if (time.time() - mutable[1]) > 3:
|
||||
print('exit when no listener')
|
||||
break
|
||||
asyncio.run(get_result(mutable))
|
||||
thread_listen = threading.Thread(target=run_coorotine, args=(mutable,))
|
||||
thread_listen.start()
|
||||
while thread_listen.is_alive():
|
||||
time.sleep(1)
|
||||
mutable[1] = time.time()
|
||||
tgui_say = mutable[0]
|
||||
return tgui_say
|
||||
Reference in New Issue
Block a user