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| from toolbox import CatchException, report_execption, gen_time_str | |
| 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 os | |
| import math | |
| import logging | |
| def markdown_to_dict(article_content): | |
| import markdown | |
| from bs4 import BeautifulSoup | |
| cur_t = "" | |
| cur_c = "" | |
| results = {} | |
| for line in article_content: | |
| if line.startswith('#'): | |
| if cur_t!="": | |
| if cur_t not in results: | |
| results.update({cur_t:cur_c.lstrip('\n')}) | |
| else: | |
| # 处理重名的章节 | |
| results.update({cur_t + " " + gen_time_str():cur_c.lstrip('\n')}) | |
| cur_t = line.rstrip('\n') | |
| cur_c = "" | |
| else: | |
| cur_c += line | |
| results_final = {} | |
| for k in list(results.keys()): | |
| if k.startswith('# '): | |
| results_final['title'] = k.split('# ')[-1] | |
| results_final['authors'] = results.pop(k).lstrip('\n') | |
| if k.startswith('###### Abstract'): | |
| results_final['abstract'] = results.pop(k).lstrip('\n') | |
| results_final_sections = [] | |
| for k,v in results.items(): | |
| results_final_sections.append({ | |
| 'heading':k.lstrip("# "), | |
| 'text':v if len(v) > 0 else f"The beginning of {k.lstrip('# ')} section." | |
| }) | |
| results_final['sections'] = results_final_sections | |
| return results_final | |
| 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 nougat | |
| import tiktoken | |
| 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 nougat-ocr tiktoken```。") | |
| 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 | |
| yield from 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) | |
| def nougat_with_timeout(command, cwd, timeout=3600): | |
| import subprocess | |
| process = subprocess.Popen(command, shell=True, cwd=cwd) | |
| try: | |
| stdout, stderr = process.communicate(timeout=timeout) | |
| except subprocess.TimeoutExpired: | |
| process.kill() | |
| stdout, stderr = process.communicate() | |
| print("Process timed out!") | |
| return False | |
| return True | |
| def NOUGAT_parse_pdf(fp): | |
| import glob | |
| from toolbox import get_log_folder, gen_time_str | |
| dst = os.path.join(get_log_folder(plugin_name='nougat'), gen_time_str()) | |
| os.makedirs(dst) | |
| nougat_with_timeout(f'nougat --out "{os.path.abspath(dst)}" "{os.path.abspath(fp)}"', os.getcwd()) | |
| res = glob.glob(os.path.join(dst,'*.mmd')) | |
| if len(res) == 0: | |
| raise RuntimeError("Nougat解析论文失败。") | |
| return res[0] | |
| def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): | |
| import copy | |
| import tiktoken | |
| 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"正在解析论文，请稍候。（第一次运行时，需要花费较长时间下载NOUGAT参数）"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 | |
| fpp = NOUGAT_parse_pdf(fp) | |
| with open(fpp, 'r', encoding='utf8') as f: | |
| article_content = f.readlines() | |
| article_dict = markdown_to_dict(article_content) | |
| logging.info(article_dict) | |
| 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 | |
| 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) | |