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批量翻译PDF文档_多线程.py
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批量翻译PDF文档_多线程.py
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from toolbox import CatchException, report_exception, get_log_folder, gen_time_str, check_packages
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 .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, translate_pdf
from colorful import *
import os
@CatchException
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:
check_packages(["fitz", "tiktoken", "scipdf"])
except:
report_exception(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 pymupdf tiktoken scipdf_parser```。")
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_exception(chatbot, history,
a=f"解析项目: {txt}", b=f"找不到任何.pdf拓展名的文件: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
# 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)
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):
import copy, json
TOKEN_LIMIT_PER_FRAGMENT = 1024
generated_conclusion_files = []
generated_html_files = []
DST_LANG = "中文"
from crazy_functions.pdf_fns.report_gen_html import construct_html
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)
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)
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 = 1024
generated_conclusion_files = []
generated_html_files = []
from crazy_functions.pdf_fns.report_gen_html import construct_html
for index, fp in enumerate(file_manifest):
# 读取PDF文件
file_content, page_one = read_and_clean_pdf_text(fp)
file_content = file_content.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
page_one = str(page_one).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
# 递归地切割PDF文件
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
paper_fragments = breakdown_text_to_satisfy_token_limit(txt=file_content, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
page_one_fragments = breakdown_text_to_satisfy_token_limit(txt=page_one, limit=TOKEN_LIMIT_PER_FRAGMENT//4, llm_model=llm_kwargs['llm_model'])
# 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]
# 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_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=[],
sys_prompt="Your job is to collect information from materials。",
)
# 多线，翻译
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
inputs_array=[
f"你需要翻译以下内容：\n{frag}" for frag in paper_fragments],
inputs_show_user_array=[f"\n---\n 原文： \n\n {frag.replace('#', '')} \n---\n 翻译：\n " for frag in paper_fragments],
llm_kwargs=llm_kwargs,
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],
# 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 "
else:
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_history_to_file(final, create_report_file_name)
promote_file_to_downloadzone(res, chatbot=chatbot)
# 更新UI
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
# write html
try:
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] = paper_fragments[i//2].replace('#', '')
else:
gpt_response_collection_html[i] = gpt_response_collection_html[i]
final = ["论文概况", paper_meta_info.replace('# ', '### '), "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"
generated_html_files.append(ch.save_file(create_report_file_name))
except:
from toolbox import trimmed_format_exc
print('writing html result failed:', trimmed_format_exc())
# 准备文件的下载
for pdf_path in generated_conclusion_files:
# 重命名文件
rename_file = f'翻译-{os.path.basename(pdf_path)}'
promote_file_to_downloadzone(pdf_path, rename_file=rename_file, chatbot=chatbot)
for html_path in generated_html_files:
# 重命名文件
rename_file = f'翻译-{os.path.basename(html_path)}'
promote_file_to_downloadzone(html_path, rename_file=rename_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