From 471a369bb8e80549dec1def680e3b0ab22ace140 Mon Sep 17 00:00:00 2001 From: kaixindelele <1985790413@qq.com> Date: Sat, 16 Sep 2023 22:09:44 +0800 Subject: [PATCH] =?UTF-8?q?=E8=AE=BA=E6=96=87=E7=BF=BB=E8=AF=91=E5=8F=AA?= =?UTF-8?q?=E8=BE=93=E5=87=BA=E4=B8=AD=E6=96=87?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- crazy_functions/批量翻译PDF文档_NOUGAT.py | 49 +++++++-- crazy_functions/批量翻译PDF文档_多线程.py | 127 +++++++++++++++++----- 2 files changed, 143 insertions(+), 33 deletions(-) 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 2dc15f7..4cfb741 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 @@ -47,7 +47,7 @@ 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): +def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port, only_chinese=True): disable_auto_promotion(chatbot) # 基本信息：功能、贡献者 @@ -84,12 +84,12 @@ 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) + yield from 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, only_chinese) -def 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): import copy import tiktoken TOKEN_LIMIT_PER_FRAGMENT = 1280 @@ -100,7 +100,7 @@ def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwa nougat_handle = nougat_interface() 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 = yield from nougat_handle.NOUGAT_parse_pdf(fp, chatbot, history) + fpp = nougat_handle.NOUGAT_parse_pdf(fp) with open(fpp, 'r', encoding='utf8') as f: article_content = f.readlines() @@ -170,19 +170,52 @@ def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwa 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) + 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: - gpt_response_collection_html[i] = gpt_response_collection_html[i] + # 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) 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 d620715..ca8568d 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 @@ -1,6 +1,6 @@ -from toolbox import CatchException, report_execption, get_log_folder +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, promote_file_to_downloadzone +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 @@ -11,7 +11,7 @@ import os import math @CatchException -def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): +def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port, only_chinese=True): disable_auto_promotion(chatbot) # 基本信息：功能、贡献者 @@ -51,15 +51,16 @@ 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) + 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) 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): +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 - TOKEN_LIMIT_PER_FRAGMENT = 1280 + import tiktoken + TOKEN_LIMIT_PER_FRAGMENT = 200 generated_conclusion_files = [] generated_html_files = [] DST_LANG = "中文" @@ -131,9 +132,39 @@ def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwa 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) + 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 = "" @@ -143,7 +174,10 @@ def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwa 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] + # 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) @@ -164,7 +198,7 @@ def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwa def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): import copy - TOKEN_LIMIT_PER_FRAGMENT = 1280 + TOKEN_LIMIT_PER_FRAGMENT = 200 generated_conclusion_files = [] generated_html_files = [] from crazy_functions.crazy_utils import construct_html @@ -176,20 +210,25 @@ 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=TOKEN_LIMIT_PER_FRAGMENT) + txt=file_content, get_token_fn=get_token_num, limit=256) 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) - + 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) + # 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[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0] + paper_meta = page_one_fragments[:] # 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=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_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=[], @@ -205,24 +244,62 @@ 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], + "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], # 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 " + 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] = "" else: - gpt_response_collection_md[i] = gpt_response_collection_md[i] + 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 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_history_to_file(final, create_report_file_name) - promote_file_to_downloadzone(res, chatbot=chatbot) + res = write_results_to_file(final, file_name=create_report_file_name) # 更新UI - generated_conclusion_files.append(f'{get_log_folder()}/{create_report_file_name}') + 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