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| from toolbox import CatchException, report_execption, write_results_to_file | |
| from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion | |
| from toolbox import write_history_to_file, get_log_folder | |
| from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive | |
| from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency | |
| from .crazy_utils import read_and_clean_pdf_text | |
| from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url | |
| from colorful import * | |
| import glob | |
| import os | |
| import math | |
| def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): | |
| disable_auto_promotion(chatbot) | |
| # 基本信息：功能、贡献者 | |
| chatbot.append([ | |
| "函数插件功能？", | |
| "批量翻译PDF文档。函数插件贡献者: Binary-Husky"]) | |
| yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 | |
| # 尝试导入依赖，如果缺少依赖，则给出安装建议 | |
| try: | |
| import fitz | |
| import tiktoken | |
| import scipdf | |
| 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 pymupdf tiktoken scipdf_parser```。") | |
| yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 | |
| return | |
| # 清空历史，以免输入溢出 | |
| history = [] | |
| from .crazy_utils import get_files_from_everything | |
| success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf') | |
| # 检测输入参数，如没有给定输入参数，直接退出 | |
| if not success: | |
| if txt == "": txt = '空空如也的输入栏' | |
| # 如果没找到任何文件 | |
| if len(file_manifest) == 0: | |
| report_execption(chatbot, history, | |
| a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}") | |
| yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 | |
| return | |
| # 开始正式执行任务 | |
| grobid_url = get_avail_grobid_url() | |
| if grobid_url is not None: | |
| yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url) | |
| else: | |
| yield from update_ui_lastest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3) | |
| yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) | |
| def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url): | |
| import copy | |
| TOKEN_LIMIT_PER_FRAGMENT = 1280 | |
| generated_conclusion_files = [] | |
| generated_html_files = [] | |
| DST_LANG = "中文" | |
| for index, fp in enumerate(file_manifest): | |
| chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 | |
| article_dict = parse_pdf(fp, grobid_url) | |
| if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。") | |
| prompt = "以下是一篇学术论文的基本信息:\n" | |
| # title | |
| title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n' | |
| # authors | |
| authors = article_dict.get('authors', '无法获取 authors'); prompt += f'authors:{authors}\n\n' | |
| # abstract | |
| abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n' | |
| # command | |
| prompt += f"请将题目和摘要翻译为{DST_LANG}。" | |
| meta = [f'# Title:\n\n', title, f'# Abstract:\n\n', abstract ] | |
| # 单线，获取文章meta信息 | |
| paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive( | |
| inputs=prompt, | |
| inputs_show_user=prompt, | |
| llm_kwargs=llm_kwargs, | |
| chatbot=chatbot, history=[], | |
| sys_prompt="You are an academic paper reader。", | |
| ) | |
| # 多线，翻译 | |
| inputs_array = [] | |
| inputs_show_user_array = [] | |
| # get_token_num | |
| from request_llm.bridge_all import model_info | |
| enc = model_info[llm_kwargs['llm_model']]['tokenizer'] | |
| def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) | |
| from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf | |
| def break_down(txt): | |
| raw_token_num = get_token_num(txt) | |
| if raw_token_num <= TOKEN_LIMIT_PER_FRAGMENT: | |
| return [txt] | |
| else: | |
| # raw_token_num > TOKEN_LIMIT_PER_FRAGMENT | |
| # find a smooth token limit to achieve even seperation | |
| count = int(math.ceil(raw_token_num / TOKEN_LIMIT_PER_FRAGMENT)) | |
| token_limit_smooth = raw_token_num // count + count | |
| return breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn=get_token_num, limit=token_limit_smooth) | |
| for section in article_dict.get('sections'): | |
| if len(section['text']) == 0: continue | |
| section_frags = break_down(section['text']) | |
| for i, fragment in enumerate(section_frags): | |
| heading = section['heading'] | |
| if len(section_frags) > 1: heading += f' Part-{i+1}' | |
| inputs_array.append( | |
| f"你需要翻译{heading}章节，内容如下: \n\n{fragment}" | |
| ) | |
| inputs_show_user_array.append( | |
| f"# {heading}\n\n{fragment}" | |
| ) | |
| gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( | |
| inputs_array=inputs_array, | |
| inputs_show_user_array=inputs_show_user_array, | |
| llm_kwargs=llm_kwargs, | |
| chatbot=chatbot, | |
| history_array=[meta for _ in inputs_array], | |
| sys_prompt_array=[ | |
| "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in inputs_array], | |
| ) | |
| res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=None, file_fullname=None) | |
| promote_file_to_downloadzone(res_path, rename_file=os.path.basename(fp)+'.md', chatbot=chatbot) | |
| generated_conclusion_files.append(res_path) | |
| ch = construct_html() | |
| orig = "" | |
| trans = "" | |
| gpt_response_collection_html = copy.deepcopy(gpt_response_collection) | |
| for i,k in enumerate(gpt_response_collection_html): | |
| if i%2==0: | |
| gpt_response_collection_html[i] = inputs_show_user_array[i//2] | |
| else: | |
| gpt_response_collection_html[i] = gpt_response_collection_html[i] | |
| final = ["", "", "一、论文概况", "", "Abstract", paper_meta_info, "二、论文翻译", ""] | |
| final.extend(gpt_response_collection_html) | |
| for i, k in enumerate(final): | |
| if i%2==0: | |
| orig = k | |
| if i%2==1: | |
| trans = k | |
| ch.add_row(a=orig, b=trans) | |
| create_report_file_name = f"{os.path.basename(fp)}.trans.html" | |
| html_file = ch.save_file(create_report_file_name) | |
| generated_html_files.append(html_file) | |
| promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot) | |
| chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files))) | |
| yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 | |
| def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): | |
| import copy | |
| TOKEN_LIMIT_PER_FRAGMENT = 1280 | |
| generated_conclusion_files = [] | |
| generated_html_files = [] | |
| for index, fp in enumerate(file_manifest): | |
| # 读取PDF文件 | |
| file_content, page_one = read_and_clean_pdf_text(fp) | |
| file_content = file_content.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars | |
| page_one = str(page_one).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars | |
| # 递归地切割PDF文件 | |
| from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf | |
| from request_llm.bridge_all import model_info | |
| enc = model_info["gpt-3.5-turbo"]['tokenizer'] | |
| def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) | |
| paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( | |
| txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT) | |
| page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( | |
| txt=page_one, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4) | |
| # 为了更好的效果，我们剥离Introduction之后的部分（如果有） | |
| paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0] | |
| # 单线，获取文章meta信息 | |
| paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive( | |
| inputs=f"以下是一篇学术论文的基础信息，请从中提取出“标题”、“收录会议或期刊”、“作者”、“摘要”、“编号”、“作者邮箱”这六个部分。请用markdown格式输出，最后用中文翻译摘要部分。请提取：{paper_meta}", | |
| inputs_show_user=f"请从{fp}中提取出“标题”、“收录会议或期刊”等基本信息。", | |
| llm_kwargs=llm_kwargs, | |
| chatbot=chatbot, history=[], | |
| sys_prompt="Your job is to collect information from materials。", | |
| ) | |
| # 多线，翻译 | |
| gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( | |
| inputs_array=[ | |
| f"你需要翻译以下内容：\n{frag}" for frag in paper_fragments], | |
| inputs_show_user_array=[f"\n---\n 原文： \n\n {frag.replace('#', '')} \n---\n 翻译：\n " for frag in paper_fragments], | |
| llm_kwargs=llm_kwargs, | |
| chatbot=chatbot, | |
| history_array=[[paper_meta] for _ in paper_fragments], | |
| sys_prompt_array=[ | |
| "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in paper_fragments], | |
| # max_workers=5 # OpenAI所允许的最大并行过载 | |
| ) | |
| gpt_response_collection_md = copy.deepcopy(gpt_response_collection) | |
| # 整理报告的格式 | |
| for i,k in enumerate(gpt_response_collection_md): | |
| if i%2==0: | |
| gpt_response_collection_md[i] = f"\n\n---\n\n ## 原文[{i//2}/{len(gpt_response_collection_md)//2}]： \n\n {paper_fragments[i//2].replace('#', '')} \n\n---\n\n ## 翻译[{i//2}/{len(gpt_response_collection_md)//2}]：\n " | |
| else: | |
| gpt_response_collection_md[i] = gpt_response_collection_md[i] | |
| final = ["一、论文概况\n\n---\n\n", paper_meta_info.replace('# ', '### ') + '\n\n---\n\n', "二、论文翻译", ""] | |
| final.extend(gpt_response_collection_md) | |
| create_report_file_name = f"{os.path.basename(fp)}.trans.md" | |
| res = write_results_to_file(final, file_name=create_report_file_name) | |
| # 更新UI | |
| generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}') | |
| chatbot.append((f"{fp}完成了吗？", res)) | |
| yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 | |
| # write html | |
| try: | |
| ch = construct_html() | |
| orig = "" | |
| trans = "" | |
| gpt_response_collection_html = copy.deepcopy(gpt_response_collection) | |
| for i,k in enumerate(gpt_response_collection_html): | |
| if i%2==0: | |
| gpt_response_collection_html[i] = paper_fragments[i//2].replace('#', '') | |
| else: | |
| gpt_response_collection_html[i] = gpt_response_collection_html[i] | |
| final = ["论文概况", paper_meta_info.replace('# ', '### '), "二、论文翻译", ""] | |
| final.extend(gpt_response_collection_html) | |
| for i, k in enumerate(final): | |
| if i%2==0: | |
| orig = k | |
| if i%2==1: | |
| trans = k | |
| ch.add_row(a=orig, b=trans) | |
| create_report_file_name = f"{os.path.basename(fp)}.trans.html" | |
| generated_html_files.append(ch.save_file(create_report_file_name)) | |
| except: | |
| from toolbox import trimmed_format_exc | |
| print('writing html result failed:', trimmed_format_exc()) | |
| # 准备文件的下载 | |
| for pdf_path in generated_conclusion_files: | |
| # 重命名文件 | |
| rename_file = f'翻译-{os.path.basename(pdf_path)}' | |
| promote_file_to_downloadzone(pdf_path, rename_file=rename_file, chatbot=chatbot) | |
| for html_path in generated_html_files: | |
| # 重命名文件 | |
| rename_file = f'翻译-{os.path.basename(html_path)}' | |
| promote_file_to_downloadzone(html_path, rename_file=rename_file, chatbot=chatbot) | |
| chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files))) | |
| yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 | |
| class construct_html(): | |
| def __init__(self) -> None: | |
| self.css = """ | |
| .row { | |
| display: flex; | |
| flex-wrap: wrap; | |
| } | |
| .column { | |
| flex: 1; | |
| padding: 10px; | |
| } | |
| .table-header { | |
| font-weight: bold; | |
| border-bottom: 1px solid black; | |
| } | |
| .table-row { | |
| border-bottom: 1px solid lightgray; | |
| } | |
| .table-cell { | |
| padding: 5px; | |
| } | |
| """ | |
| self.html_string = f'<!DOCTYPE html><head><meta charset="utf-8"><title>翻译结果</title><style>{self.css}</style></head>' | |
| def add_row(self, a, b): | |
| tmp = """ | |
| <div class="row table-row"> | |
| <div class="column table-cell">REPLACE_A</div> | |
| <div class="column table-cell">REPLACE_B</div> | |
| </div> | |
| """ | |
| from toolbox import markdown_convertion | |
| tmp = tmp.replace('REPLACE_A', markdown_convertion(a)) | |
| tmp = tmp.replace('REPLACE_B', markdown_convertion(b)) | |
| self.html_string += tmp | |
| def save_file(self, file_name): | |
| with open(os.path.join(get_log_folder(), file_name), 'w', encoding='utf8') as f: | |
| f.write(self.html_string.encode('utf-8', 'ignore').decode()) | |
| return os.path.join(get_log_folder(), file_name) | |