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from toolbox import CatchException, report_execption, write_results_to_file |
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from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion |
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from toolbox import write_history_to_file, get_log_folder |
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive |
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from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency |
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from .crazy_utils import read_and_clean_pdf_text |
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from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url |
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from colorful import * |
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import glob |
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import os |
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import math |
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@CatchException |
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def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): |
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disable_auto_promotion(chatbot) |
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chatbot.append([ |
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"函数插件功能?", |
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"批量翻译PDF文档。函数插件贡献者: Binary-Husky"]) |
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yield from update_ui(chatbot=chatbot, history=history) |
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try: |
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import fitz |
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import tiktoken |
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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 pymupdf tiktoken```。") |
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yield from update_ui(chatbot=chatbot, history=history) |
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return |
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history = [] |
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from .crazy_utils import get_files_from_everything |
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success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf') |
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if not success: |
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if txt == "": txt = '空空如也的输入栏' |
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if len(file_manifest) == 0: |
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report_execption(chatbot, history, |
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a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}") |
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yield from update_ui(chatbot=chatbot, history=history) |
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return |
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grobid_url = get_avail_grobid_url() |
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if grobid_url is not None: |
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yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url) |
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else: |
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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) |
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yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) |
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def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url): |
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import copy |
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import tiktoken |
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TOKEN_LIMIT_PER_FRAGMENT = 1280 |
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generated_conclusion_files = [] |
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generated_html_files = [] |
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DST_LANG = "中文" |
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for index, fp in enumerate(file_manifest): |
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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) |
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article_dict = parse_pdf(fp, grobid_url) |
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print(article_dict) |
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prompt = "以下是一篇学术论文的基本信息:\n" |
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title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n' |
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authors = article_dict.get('authors', '无法获取 authors'); prompt += f'authors:{authors}\n\n' |
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abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n' |
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prompt += f"请将题目和摘要翻译为{DST_LANG}。" |
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meta = [f'# Title:\n\n', title, f'# Abstract:\n\n', abstract ] |
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paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive( |
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inputs=prompt, |
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inputs_show_user=prompt, |
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llm_kwargs=llm_kwargs, |
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chatbot=chatbot, history=[], |
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sys_prompt="You are an academic paper reader。", |
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) |
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inputs_array = [] |
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inputs_show_user_array = [] |
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from request_llm.bridge_all import model_info |
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enc = model_info[llm_kwargs['llm_model']]['tokenizer'] |
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) |
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf |
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def break_down(txt): |
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raw_token_num = get_token_num(txt) |
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if raw_token_num <= TOKEN_LIMIT_PER_FRAGMENT: |
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return [txt] |
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else: |
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count = int(math.ceil(raw_token_num / TOKEN_LIMIT_PER_FRAGMENT)) |
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token_limit_smooth = raw_token_num // count + count |
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return breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn=get_token_num, limit=token_limit_smooth) |
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for section in article_dict.get('sections'): |
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if len(section['text']) == 0: continue |
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section_frags = break_down(section['text']) |
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for i, fragment in enumerate(section_frags): |
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heading = section['heading'] |
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if len(section_frags) > 1: heading += f'Part-{i+1}' |
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inputs_array.append( |
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f"你需要翻译{heading}章节,内容如下: \n\n{fragment}" |
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) |
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inputs_show_user_array.append( |
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f"# {heading}\n\n{fragment}" |
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) |
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gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( |
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inputs_array=inputs_array, |
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inputs_show_user_array=inputs_show_user_array, |
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llm_kwargs=llm_kwargs, |
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chatbot=chatbot, |
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history_array=[meta for _ in inputs_array], |
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sys_prompt_array=[ |
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"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], |
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) |
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res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=None, file_fullname=None) |
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promote_file_to_downloadzone(res_path, rename_file=os.path.basename(fp)+'.md', chatbot=chatbot) |
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generated_conclusion_files.append(res_path) |
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ch = construct_html() |
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orig = "" |
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trans = "" |
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gpt_response_collection_html = copy.deepcopy(gpt_response_collection) |
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for i,k in enumerate(gpt_response_collection_html): |
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if i%2==0: |
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gpt_response_collection_html[i] = inputs_show_user_array[i//2] |
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else: |
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gpt_response_collection_html[i] = gpt_response_collection_html[i] |
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final = ["", "", "一、论文概况", "", "Abstract", paper_meta_info, "二、论文翻译", ""] |
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final.extend(gpt_response_collection_html) |
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for i, k in enumerate(final): |
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if i%2==0: |
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orig = k |
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if i%2==1: |
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trans = k |
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ch.add_row(a=orig, b=trans) |
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create_report_file_name = f"{os.path.basename(fp)}.trans.html" |
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html_file = ch.save_file(create_report_file_name) |
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generated_html_files.append(html_file) |
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promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot) |
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chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files))) |
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yield from update_ui(chatbot=chatbot, history=history) |
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def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): |
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import copy |
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TOKEN_LIMIT_PER_FRAGMENT = 1280 |
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generated_conclusion_files = [] |
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generated_html_files = [] |
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for index, fp in enumerate(file_manifest): |
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file_content, page_one = read_and_clean_pdf_text(fp) |
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file_content = file_content.encode('utf-8', 'ignore').decode() |
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page_one = str(page_one).encode('utf-8', 'ignore').decode() |
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf |
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from request_llm.bridge_all import model_info |
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enc = model_info["gpt-3.5-turbo"]['tokenizer'] |
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=())) |
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paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( |
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txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT) |
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page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( |
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txt=page_one, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4) |
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paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0] |
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paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive( |
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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}", |
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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。", |
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llm_kwargs=llm_kwargs, |
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chatbot=chatbot, history=[], |
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sys_prompt="Your job is to collect information from materials。", |
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) |
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gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( |
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inputs_array=[ |
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f"你需要翻译以下内容:\n{frag}" for frag in paper_fragments], |
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inputs_show_user_array=[f"\n---\n 原文: \n\n {frag.replace('#', '')} \n---\n 翻译:\n " for frag in paper_fragments], |
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llm_kwargs=llm_kwargs, |
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chatbot=chatbot, |
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history_array=[[paper_meta] for _ in paper_fragments], |
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sys_prompt_array=[ |
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"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], |
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) |
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gpt_response_collection_md = copy.deepcopy(gpt_response_collection) |
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for i,k in enumerate(gpt_response_collection_md): |
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if i%2==0: |
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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 " |
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else: |
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gpt_response_collection_md[i] = gpt_response_collection_md[i] |
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final = ["一、论文概况\n\n---\n\n", paper_meta_info.replace('# ', '### ') + '\n\n---\n\n', "二、论文翻译", ""] |
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final.extend(gpt_response_collection_md) |
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create_report_file_name = f"{os.path.basename(fp)}.trans.md" |
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res = write_results_to_file(final, file_name=create_report_file_name) |
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generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}') |
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chatbot.append((f"{fp}完成了吗?", res)) |
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yield from update_ui(chatbot=chatbot, history=history) |
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try: |
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ch = construct_html() |
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orig = "" |
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trans = "" |
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gpt_response_collection_html = copy.deepcopy(gpt_response_collection) |
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for i,k in enumerate(gpt_response_collection_html): |
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if i%2==0: |
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gpt_response_collection_html[i] = paper_fragments[i//2].replace('#', '') |
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else: |
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gpt_response_collection_html[i] = gpt_response_collection_html[i] |
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final = ["论文概况", paper_meta_info.replace('# ', '### '), "二、论文翻译", ""] |
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final.extend(gpt_response_collection_html) |
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for i, k in enumerate(final): |
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if i%2==0: |
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orig = k |
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if i%2==1: |
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trans = k |
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ch.add_row(a=orig, b=trans) |
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create_report_file_name = f"{os.path.basename(fp)}.trans.html" |
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generated_html_files.append(ch.save_file(create_report_file_name)) |
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except: |
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from toolbox import trimmed_format_exc |
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print('writing html result failed:', trimmed_format_exc()) |
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for pdf_path in generated_conclusion_files: |
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rename_file = f'翻译-{os.path.basename(pdf_path)}' |
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promote_file_to_downloadzone(pdf_path, rename_file=rename_file, chatbot=chatbot) |
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for html_path in generated_html_files: |
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rename_file = f'翻译-{os.path.basename(html_path)}' |
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promote_file_to_downloadzone(html_path, rename_file=rename_file, chatbot=chatbot) |
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chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files))) |
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yield from update_ui(chatbot=chatbot, history=history) |
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class construct_html(): |
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def __init__(self) -> None: |
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self.css = """ |
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.row { |
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display: flex; |
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flex-wrap: wrap; |
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} |
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.column { |
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flex: 1; |
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padding: 10px; |
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} |
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.table-header { |
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font-weight: bold; |
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border-bottom: 1px solid black; |
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} |
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.table-row { |
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border-bottom: 1px solid lightgray; |
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} |
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.table-cell { |
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padding: 5px; |
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} |
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""" |
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self.html_string = f'<!DOCTYPE html><head><meta charset="utf-8"><title>翻译结果</title><style>{self.css}</style></head>' |
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def add_row(self, a, b): |
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tmp = """ |
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<div class="row table-row"> |
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<div class="column table-cell">REPLACE_A</div> |
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<div class="column table-cell">REPLACE_B</div> |
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</div> |
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""" |
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from toolbox import markdown_convertion |
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tmp = tmp.replace('REPLACE_A', markdown_convertion(a)) |
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tmp = tmp.replace('REPLACE_B', markdown_convertion(b)) |
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self.html_string += tmp |
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def save_file(self, file_name): |
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with open(os.path.join(get_log_folder(), file_name), 'w', encoding='utf8') as f: |
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f.write(self.html_string.encode('utf-8', 'ignore').decode()) |
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return os.path.join(get_log_folder(), file_name) |
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