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
@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:
import fitz
import tiktoken
import scipdf
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 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_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
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)
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],
)
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)