论文翻译只输出中文
This commit is contained in:
kaixindelele
parent
760ff1840c
commit
471a369bb8
@ -47,7 +47,7 @@ def markdown_to_dict(article_content):
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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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def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port, only_chinese=True):
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disable_auto_promotion(chatbot)
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# 基本信息：功能、贡献者
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@ -84,12 +84,12 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
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return
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# 开始正式执行任务
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yield from 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
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yield from 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, only_chinese)
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def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
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def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, only_chinese=True):
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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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@ -100,7 +100,7 @@ def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwa
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nougat_handle = nougat_interface()
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for index, fp in enumerate(file_manifest):
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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
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fpp = yield from nougat_handle.NOUGAT_parse_pdf(fp, chatbot, history)
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fpp = nougat_handle.NOUGAT_parse_pdf(fp)
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with open(fpp, 'r', encoding='utf8') as f:
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article_content = f.readlines()
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@ -170,19 +170,52 @@ def 解析PDF_基于NOUGAT(file_manifest, project_folder, llm_kwargs, plugin_kwa
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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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if only_chinese:
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# 直接提取出翻译的内容，然后保存下去：
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chinese_list = []
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# 记录当前的大章节标题：
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last_section_name = ""
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for index, value in enumerate(gpt_response_collection):
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# 先挑选偶数序列号：
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if index % 2 != 0:
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# 先提取当前英文标题：
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cur_section_name = gpt_response_collection[index-1].split('\n')[0].split(" Part")[0]
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# 如果index是1的话，则直接使用first section name：
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if cur_section_name != last_section_name:
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cur_value = cur_section_name + '\n'
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last_section_name = copy.deepcopy(cur_section_name)
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else:
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cur_value = ""
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# 再判断翻译是否错误，如果错误，则直接贴原来的英文：
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if "The OpenAI account associated" in value:
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cur_value += gpt_response_collection[index-1]
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else:
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# 再做一个小修改：重新修改当前part的标题，默认用英文的
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cur_value += value
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chinese_list.append(cur_value)
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res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + chinese_list, 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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else:
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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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# 叠加HTML文件
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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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# 先提取当前英文标题：
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cur_section_name = gpt_response_collection[i-1].split('\n')[0].split(" Part")[0]
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cur_value = cur_section_name + "\n" + gpt_response_collection_html[i]
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gpt_response_collection_html[i] = cur_value
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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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@ -1,6 +1,6 @@
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from toolbox import CatchException, report_execption, get_log_folder
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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, promote_file_to_downloadzone
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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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@ -11,7 +11,7 @@ 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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def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port, only_chinese=True):
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disable_auto_promotion(chatbot)
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# 基本信息：功能、贡献者
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@ -51,15 +51,16 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
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# 开始正式执行任务
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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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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)
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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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def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url, only_chinese=True):
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import copy
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TOKEN_LIMIT_PER_FRAGMENT = 1280
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import tiktoken
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TOKEN_LIMIT_PER_FRAGMENT = 200
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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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@ -131,9 +132,39 @@ def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwa
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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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if only_chinese:
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# 直接提取出翻译的内容，然后保存下去：
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chinese_list = []
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# 记录当前的大章节标题：
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last_section_name = ""
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for index, value in enumerate(gpt_response_collection):
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# 先挑选偶数序列号：
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if index % 2 != 0:
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# 先提取当前英文标题：
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cur_section_name = gpt_response_collection[index-1].split('\n')[0].split(" Part")[0]
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# 如果index是1的话，则直接使用first section name：
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if cur_section_name != last_section_name:
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cur_value = cur_section_name + '\n'
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last_section_name = copy.deepcopy(cur_section_name)
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else:
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cur_value = ""
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# 再判断翻译是否错误，如果错误，则直接贴原来的英文：
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if "The OpenAI account associated" in value:
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cur_value += gpt_response_collection[index-1]
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else:
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# 再做一个小修改：重新修改当前part的标题，默认用英文的
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cur_value += value
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chinese_list.append(cur_value)
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res_path = write_history_to_file(meta + ["# Meta Translation" , paper_meta_info] + chinese_list, 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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else:
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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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@ -143,7 +174,10 @@ def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwa
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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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# 先提取当前英文标题：
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cur_section_name = gpt_response_collection[i-1].split('\n')[0].split(" Part")[0]
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cur_value = cur_section_name + "\n" + gpt_response_collection_html[i]
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gpt_response_collection_html[i] = cur_value
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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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@ -164,7 +198,7 @@ def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwa
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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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TOKEN_LIMIT_PER_FRAGMENT = 200
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generated_conclusion_files = []
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generated_html_files = []
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from crazy_functions.crazy_utils import construct_html
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@ -176,20 +210,25 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
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# 递归地切割PDF文件
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
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# from .crazy_utils import split_main_text
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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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txt=file_content, get_token_fn=get_token_num, limit=256)
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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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txt=page_one, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
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## 用我这个分段切分。
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# paper_fragments = split_main_text(text=file_content, max_token=TOKEN_LIMIT_PER_FRAGMENT)
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# page_one_fragments = split_main_text(text=page_one, max_token=TOKEN_LIMIT_PER_FRAGMENT)
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# 为了更好的效果，我们剥离Introduction之后的部分（如果有）
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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 = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
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paper_meta = page_one_fragments[:]
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# 单线，获取文章meta信息
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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=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}",
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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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@ -205,24 +244,62 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
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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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"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],
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# max_workers=5 # OpenAI所允许的最大并行过载
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)
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gpt_response_collection_md = copy.deepcopy(gpt_response_collection)
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# 整理报告的格式
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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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add_origin = True
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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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cur_trans = gpt_response_collection_md[i]
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# 做个小小的处理，把翻译的结果中非常长的“#”去掉
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temp_trans = ""
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for line_text in cur_trans.split('\n'):
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if len(line_text) == 0:
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# print("空行")
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temp_trans += "\n\n"
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else:
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if "#" in line_text[0]:
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if len(line_text.split(' ')) > 12:
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temp_trans += line_text.replace('#', '')
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else:
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temp_trans += line_text
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temp_trans += "\n\n"
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else:
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temp_trans += line_text + "\n\n"
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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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if add_origin:
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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] = ""
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else:
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gpt_response_collection_md[i] = gpt_response_collection_md[i]
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cur_trans = gpt_response_collection_md[i]
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# 做个小小的处理，把翻译的结果中非常长的“#”去掉
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temp_trans = ""
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for line_text in cur_trans.split('\n'):
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if len(line_text) == 0:
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# print("空行")
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temp_trans += "\n\n"
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else:
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if "#" in line_text[0]:
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if len(line_text) > 12:
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temp_trans += line_text.replace('#', '')
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else:
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temp_trans += line_text
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temp_trans += "\n\n"
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else:
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temp_trans += line_text + "\n\n"
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gpt_response_collection_md[i] = temp_trans
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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_history_to_file(final, create_report_file_name)
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promote_file_to_downloadzone(res, chatbot=chatbot)
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res = write_results_to_file(final, file_name=create_report_file_name)
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# 更新UI
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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
|
||||
|
||||
|
||||
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Reference in New Issue
Block a user