diff --git a/crazy_functions/pdf_fns/parse_pdf.py b/crazy_functions/pdf_fns/parse_pdf.py index 8a7117a..a726aa2 100644 --- a/crazy_functions/pdf_fns/parse_pdf.py +++ b/crazy_functions/pdf_fns/parse_pdf.py @@ -1,6 +1,14 @@ +from functools import lru_cache +from toolbox import gen_time_str +from toolbox import promote_file_to_downloadzone +from toolbox import write_history_to_file, promote_file_to_downloadzone +from colorful import * import requests import random -from functools import lru_cache +import copy +import os +import math + class GROBID_OFFLINE_EXCEPTION(Exception): pass def get_avail_grobid_url(): @@ -28,3 +36,133 @@ def parse_pdf(pdf_path, grobid_url): raise RuntimeError("解析PDF失败，请检查PDF是否损坏。") return article_dict + +def produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files): + # -=-=-=-=-=-=-=-= 写出第1个文件：翻译前后混合 -=-=-=-=-=-=-=-= + res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=f"{gen_time_str()}translated_and_original.md", file_fullname=None) + promote_file_to_downloadzone(res_path, rename_file=os.path.basename(res_path)+'.md', chatbot=chatbot) + generated_conclusion_files.append(res_path) + + # -=-=-=-=-=-=-=-= 写出第2个文件：仅翻译后的文本 -=-=-=-=-=-=-=-= + translated_res_array = [] + # 记录当前的大章节标题： + last_section_name = "" + for index, value in enumerate(gpt_response_collection): + # 先挑选偶数序列号： + if index % 2 != 0: + # 先提取当前英文标题： + cur_section_name = gpt_response_collection[index-1].split('\n')[0].split(" Part")[0] + # 如果index是1的话，则直接使用first section name： + if cur_section_name != last_section_name: + cur_value = cur_section_name + '\n' + last_section_name = copy.deepcopy(cur_section_name) + else: + cur_value = "" + # 再做一个小修改：重新修改当前part的标题，默认用英文的 + cur_value += value + translated_res_array.append(cur_value) + res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + translated_res_array, + file_basename = f"{gen_time_str()}-translated_only.md", + file_fullname = None, + auto_caption = False) + promote_file_to_downloadzone(res_path, rename_file=os.path.basename(res_path)+'.md', chatbot=chatbot) + generated_conclusion_files.append(res_path) + return res_path + +def translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG): + from crazy_functions.crazy_utils import construct_html + from crazy_functions.crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf + from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive + from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency + + 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=())) + + 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], + ) + # -=-=-=-=-=-=-=-= 写出Markdown文件 -=-=-=-=-=-=-=-= + produce_report_markdown(gpt_response_collection, meta, paper_meta_info, chatbot, fp, generated_conclusion_files) + + # -=-=-=-=-=-=-=-= 写出HTML文件 -=-=-=-=-=-=-=-= + 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: + # 先提取当前英文标题： + cur_section_name = gpt_response_collection[i-1].split('\n')[0].split(" Part")[0] + cur_value = cur_section_name + "\n" + gpt_response_collection_html[i] + gpt_response_collection_html[i] = cur_value + + 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_conclusion_files.append(html_file) + promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot) diff --git a/crazy_functions/批量翻译PDF文档_NOUGAT.py b/crazy_functions/批量翻译PDF文档_NOUGAT.py index 4cfb741..4aded07 100644 --- a/crazy_functions/批量翻译PDF文档_NOUGAT.py +++ b/crazy_functions/批量翻译PDF文档_NOUGAT.py @@ -1,11 +1,12 @@ -from toolbox import CatchException, report_execption, gen_time_str +from toolbox import CatchException, report_execption, get_log_folder, 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 toolbox import write_history_to_file, promote_file_to_downloadzone 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 .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf from colorful import * +import copy import os import math import logging @@ -47,7 +48,7 @@ def markdown_to_dict(article_content): @CatchException -def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port, only_chinese=True): +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 @@ -84,15 +85,15 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst 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, only_chinese) + yield from 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) -def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, only_chinese=True): +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 + TOKEN_LIMIT_PER_FRAGMENT = 512 generated_conclusion_files = [] generated_html_files = [] DST_LANG = "中文" @@ -106,129 +107,7 @@ def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwa 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], - ) - if only_chinese: - # 直接提取出翻译的内容，然后保存下去： - chinese_list = [] - # 记录当前的大章节标题： - last_section_name = "" - for index, value in enumerate(gpt_response_collection): - # 先挑选偶数序列号： - if index % 2 != 0: - # 先提取当前英文标题： - cur_section_name = gpt_response_collection[index-1].split('\n')[0].split(" Part")[0] - - # 如果index是1的话，则直接使用first section name： - if cur_section_name != last_section_name: - cur_value = cur_section_name + '\n' - last_section_name = copy.deepcopy(cur_section_name) - else: - cur_value = "" - # 再判断翻译是否错误，如果错误，则直接贴原来的英文： - if "The OpenAI account associated" in value: - cur_value += gpt_response_collection[index-1] - else: - # 再做一个小修改：重新修改当前part的标题，默认用英文的 - cur_value += value - - chinese_list.append(cur_value) - res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + chinese_list, 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) - else: - 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) - # 叠加HTML文件 - 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: - # 先提取当前英文标题： - cur_section_name = gpt_response_collection[i-1].split('\n')[0].split(" Part")[0] - cur_value = cur_section_name + "\n" + gpt_response_collection_html[i] - gpt_response_collection_html[i] = cur_value - - 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) + yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG) 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 diff --git a/crazy_functions/批量翻译PDF文档_多线程.py b/crazy_functions/批量翻译PDF文档_多线程.py index ca8568d..971710e 100644 --- a/crazy_functions/批量翻译PDF文档_多线程.py +++ b/crazy_functions/批量翻译PDF文档_多线程.py @@ -1,17 +1,17 @@ -from toolbox import CatchException, report_execption, write_results_to_file +from toolbox import CatchException, report_execption, get_log_folder, 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 toolbox import write_history_to_file, promote_file_to_downloadzone 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 .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf from colorful import * -import glob +import copy import os import math @CatchException -def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port, only_chinese=True): +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 @@ -51,16 +51,15 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst # 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, only_chinese=only_chinese) + 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, only_chinese=True): - import copy - import tiktoken - TOKEN_LIMIT_PER_FRAGMENT = 200 +def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url): + import copy, json + TOKEN_LIMIT_PER_FRAGMENT = 512 generated_conclusion_files = [] generated_html_files = [] DST_LANG = "中文" @@ -68,137 +67,23 @@ def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwa 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], - ) - if only_chinese: - # 直接提取出翻译的内容，然后保存下去： - chinese_list = [] - # 记录当前的大章节标题： - last_section_name = "" - for index, value in enumerate(gpt_response_collection): - # 先挑选偶数序列号： - if index % 2 != 0: - # 先提取当前英文标题： - cur_section_name = gpt_response_collection[index-1].split('\n')[0].split(" Part")[0] - - # 如果index是1的话，则直接使用first section name： - if cur_section_name != last_section_name: - cur_value = cur_section_name + '\n' - last_section_name = copy.deepcopy(cur_section_name) - else: - cur_value = "" - # 再判断翻译是否错误，如果错误，则直接贴原来的英文： - if "The OpenAI account associated" in value: - cur_value += gpt_response_collection[index-1] - else: - # 再做一个小修改：重新修改当前part的标题，默认用英文的 - cur_value += value - - chinese_list.append(cur_value) + grobid_json_res = os.path.join(get_log_folder(), gen_time_str() + "grobid.json") + with open(grobid_json_res, 'w+', encoding='utf8') as f: + f.write(json.dumps(article_dict, indent=4, ensure_ascii=False)) + promote_file_to_downloadzone(grobid_json_res, chatbot=chatbot) - res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + chinese_list, 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) - else: - 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: - # 先提取当前英文标题： - cur_section_name = gpt_response_collection[i-1].split('\n')[0].split(" Part")[0] - cur_value = cur_section_name + "\n" + gpt_response_collection_html[i] - gpt_response_collection_html[i] = cur_value - - 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) - + if article_dict is None: raise RuntimeError("解析PDF失败，请检查PDF是否损坏。") + yield from translate_pdf(article_dict, llm_kwargs, chatbot, fp, generated_conclusion_files, TOKEN_LIMIT_PER_FRAGMENT, DST_LANG) 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): + """ + 此函数已经弃用 + """ import copy - TOKEN_LIMIT_PER_FRAGMENT = 200 + TOKEN_LIMIT_PER_FRAGMENT = 512 generated_conclusion_files = [] generated_html_files = [] from crazy_functions.crazy_utils import construct_html @@ -210,25 +95,20 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, # 递归地切割PDF文件 from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf - # from .crazy_utils import split_main_text 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=())) + 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=256) + 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) - ## 用我这个分段切分。 - # paper_fragments = split_main_text(text=file_content, max_token=TOKEN_LIMIT_PER_FRAGMENT) - # page_one_fragments = split_main_text(text=page_one, max_token=TOKEN_LIMIT_PER_FRAGMENT) - + 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] - paper_meta = page_one_fragments[:] + 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”、“作者单位”、“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，不要提取Introduction部分的内容。请提取：{paper_meta}", + 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=[], @@ -244,62 +124,24 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, chatbot=chatbot, history_array=[[paper_meta] for _ in paper_fragments], sys_prompt_array=[ - "请你作为一个学术翻译，负责把学术论文的部分章节文本，准确翻译成中文。注意：1. 文章中的每一句话都要翻译，并且消除输入文本前后的无意义乱码，2. 请自动识别小章节标题(小标题长度不要超过20个字符，也不要少于3个字符)，并且用'### xxx'的markdown格式标记出来。" for _ in paper_fragments], + "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 - add_origin = True - for i, k in enumerate(gpt_response_collection_md): - if i % 2 ==0: - cur_trans = gpt_response_collection_md[i] - # 做个小小的处理，把翻译的结果中非常长的“#”去掉 - temp_trans = "" - for line_text in cur_trans.split('\n'): - if len(line_text) == 0: - # print("空行") - temp_trans += "\n\n" - else: - if "#" in line_text[0]: - if len(line_text.split(' ')) > 12: - temp_trans += line_text.replace('#', '') - else: - temp_trans += line_text - temp_trans += "\n\n" - else: - temp_trans += line_text + "\n\n" - - # 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 " - if add_origin: - 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] = "" + 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: - cur_trans = gpt_response_collection_md[i] - # 做个小小的处理，把翻译的结果中非常长的“#”去掉 - temp_trans = "" - for line_text in cur_trans.split('\n'): - if len(line_text) == 0: - # print("空行") - temp_trans += "\n\n" - else: - if "#" in line_text[0]: - if len(line_text) > 12: - temp_trans += line_text.replace('#', '') - else: - temp_trans += line_text - temp_trans += "\n\n" - else: - temp_trans += line_text + "\n\n" - - gpt_response_collection_md[i] = temp_trans + 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) + res = write_history_to_file(final, create_report_file_name) + promote_file_to_downloadzone(res, chatbot=chatbot) # 更新UI - generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}') + generated_conclusion_files.append(f'{get_log_folder()}/{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 diff --git a/toolbox.py b/toolbox.py index 1452c13..6a53868 100644 --- a/toolbox.py +++ b/toolbox.py @@ -216,7 +216,7 @@ def get_reduce_token_percent(text): return 0.5, '不详' -def write_history_to_file(history, file_basename=None, file_fullname=None): +def write_history_to_file(history, file_basename=None, file_fullname=None, auto_caption=True): """ 将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。 """ @@ -235,7 +235,7 @@ def write_history_to_file(history, file_basename=None, file_fullname=None): if type(content) != str: content = str(content) except: continue - if i % 2 == 0: + if i % 2 == 0 and auto_caption: f.write('## ') try: f.write(content)