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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, 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 glob | |
import os | |
import math | |
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 fitz | |
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 pymupdf 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 | |
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 | |
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) | |
print(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 | |
def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): | |
import copy | |
TOKEN_LIMIT_PER_FRAGMENT = 1280 | |
generated_conclusion_files = [] | |
generated_html_files = [] | |
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_utils import breakdown_txt_to_satisfy_token_limit_for_pdf | |
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=())) | |
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( | |
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//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] | |
# 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_results_to_file(final, file_name=create_report_file_name) | |
# 更新UI | |
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 | |
# 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 | |
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) | |