from toolbox import CatchException, report_execption, write_results_to_file
from toolbox import update_ui
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 colorful import *
def read_and_clean_pdf_text(fp):
"""
这个函数用于分割pdf，用了很多trick，逻辑较乱，效果奇好，不建议任何人去读这个函数
**输入参数说明**
- `fp`：需要读取和清理文本的pdf文件路径
**输出参数说明**
- `meta_txt`：清理后的文本内容字符串
- `page_one_meta`：第一页清理后的文本内容列表
**函数功能**
读取pdf文件并清理其中的文本内容，清理规则包括：
- 提取所有块元的文本信息，并合并为一个字符串
- 去除短块（字符数小于100）并替换为回车符
- 清理多余的空行
- 合并小写字母开头的段落块并替换为空格
- 清除重复的换行
- 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔
"""
import fitz, copy
import re
import numpy as np
fc = 0
fs = 1
fb = 2
REMOVE_FOOT_NOTE = True
REMOVE_FOOT_FFSIZE_PERCENT = 0.95
def primary_ffsize(l):
fsize_statiscs = {}
for wtf in l['spans']:
if wtf['size'] not in fsize_statiscs: fsize_statiscs[wtf['size']] = 0
fsize_statiscs[wtf['size']] += len(wtf['text'])
return max(fsize_statiscs, key=fsize_statiscs.get)
def ffsize_same(a,b):
return abs((a-b)/max(a,b)) < 0.02
# file_content = ""
with fitz.open(fp) as doc:
meta_txt = []
meta_font = []
meta_line = []
meta_span = []
for index, page in enumerate(doc):
# file_content += page.get_text()
text_areas = page.get_text("dict") # 获取页面上的文本信息
for t in text_areas['blocks']:
if 'lines' in t:
pf = 998
for l in t['lines']:
txt_line = "".join([wtf['text'] for wtf in l['spans']])
pf = primary_ffsize(l)
meta_line.append([txt_line, pf, l['bbox'], l])
for wtf in l['spans']: # for l in t['lines']:
meta_span.append([wtf['text'], wtf['size'], len(wtf['text'])])
# meta_line.append(["NEW_BLOCK", pf])
# 块元提取 for each word segment with in line for each line cross-line words for each block
meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
'- ', '') for t in text_areas['blocks'] if 'lines' in t])
meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
if index == 0:
page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
'- ', '') for t in text_areas['blocks'] if 'lines' in t]
# 获取正文主字体
fsize_statiscs = {}
for span in meta_span:
if span[1] not in fsize_statiscs: fsize_statiscs[span[1]] = 0
fsize_statiscs[span[1]] += span[2]
main_fsize = max(fsize_statiscs, key=fsize_statiscs.get)
if REMOVE_FOOT_NOTE:
give_up_fize_threshold = main_fsize * REMOVE_FOOT_FFSIZE_PERCENT
# 切分和重新整合
mega_sec = []
sec = []
for index, line in enumerate(meta_line):
if index == 0:
sec.append(line[fc])
continue
if REMOVE_FOOT_NOTE:
if meta_line[index][fs] <= give_up_fize_threshold:
continue
if ffsize_same(meta_line[index][fs], meta_line[index-1][fs]):
# 尝试识别段落
if meta_line[index][fc].endswith('.') and\
(meta_line[index-1][fc] != 'NEW_BLOCK') and \
(meta_line[index][fb][2] - meta_line[index][fb][0]) < (meta_line[index-1][fb][2] - meta_line[index-1][fb][0]) * 0.7:
sec[-1] += line[fc]
sec[-1] += "\n\n"
else:
sec[-1] += " "
sec[-1] += line[fc]
else:
if (index+1 < len(meta_line)) and \
meta_line[index][fs] > main_fsize:
# 单行 + 字体大
mega_sec.append(copy.deepcopy(sec))
sec = []
sec.append("# " + line[fc])
else:
# 尝试识别section
if meta_line[index-1][fs] > meta_line[index][fs]:
sec.append("\n" + line[fc])
else:
sec.append(line[fc])
mega_sec.append(copy.deepcopy(sec))
finals = []
for ms in mega_sec:
final = " ".join(ms)
final = final.replace('- ', ' ')
finals.append(final)
meta_txt = finals
def 把字符太少的块清除为回车(meta_txt):
for index, block_txt in enumerate(meta_txt):
if len(block_txt) < 100:
meta_txt[index] = '\n'
return meta_txt
meta_txt = 把字符太少的块清除为回车(meta_txt)
def 清理多余的空行(meta_txt):
for index in reversed(range(1, len(meta_txt))):
if meta_txt[index] == '\n' and meta_txt[index-1] == '\n':
meta_txt.pop(index)
return meta_txt
meta_txt = 清理多余的空行(meta_txt)
def 合并小写开头的段落块(meta_txt):
def starts_with_lowercase_word(s):
pattern = r"^[a-z]+"
match = re.match(pattern, s)
if match:
return True
else:
return False
for _ in range(100):
for index, block_txt in enumerate(meta_txt):
if starts_with_lowercase_word(block_txt):
if meta_txt[index-1] != '\n':
meta_txt[index-1] += ' '
else:
meta_txt[index-1] = ''
meta_txt[index-1] += meta_txt[index]
meta_txt[index] = '\n'
return meta_txt
meta_txt = 合并小写开头的段落块(meta_txt)
meta_txt = 清理多余的空行(meta_txt)
meta_txt = '\n'.join(meta_txt)
# 清除重复的换行
for _ in range(5):
meta_txt = meta_txt.replace('\n\n', '\n')
# 换行 -> 双换行
meta_txt = meta_txt.replace('\n', '\n\n')
for f in finals:
print亮黄(f)
print亮绿('***************************')
return meta_txt, page_one_meta
@CatchException
def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
import glob
import os
# 基本信息：功能、贡献者
chatbot.append([
"函数插件功能？",
"批量总结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 = []
# 检测输入参数，如没有给定输入参数，直接退出
if os.path.exists(txt):
project_folder = txt
else:
if txt == "":
txt = '空空如也的输入栏'
report_execption(chatbot, history,
a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
# 搜索需要处理的文件清单
file_manifest = [f for f in glob.glob(
f'{project_folder}/**/*.pdf', recursive=True)]
# 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
yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt)
def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt):
import os
import tiktoken
TOKEN_LIMIT_PER_FRAGMENT = 1600
generated_conclusion_files = []
for index, fp in enumerate(file_manifest):
# 读取PDF文件
file_content, page_one = read_and_clean_pdf_text(fp)
# 递归地切割PDF文件
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
from toolbox import get_conf
enc = tiktoken.encoding_for_model(*get_conf('LLM_MODEL'))
def get_token_num(txt): return len(enc.encode(txt))
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=str(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"以下是你需要翻译的论文片段：\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，负责把学术论文的片段准确翻译成中文。" for _ in paper_fragments],
max_workers=16 # OpenAI所允许的最大并行过载
)
# 整理报告的格式
for i,k in enumerate(gpt_response_collection):
if i%2==0:
gpt_response_collection[i] = f"\n\n---\n\n ## 原文[{i//2}/{len(gpt_response_collection)//2}]： \n\n {paper_fragments[i//2].replace('#', '')} \n\n---\n\n ## 翻译[{i//2}/{len(gpt_response_collection)//2}]：\n "
else:
gpt_response_collection[i] = gpt_response_collection[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)
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
# 准备文件的下载
import shutil
for pdf_path in generated_conclusion_files:
# 重命名文件
rename_file = f'./gpt_log/总结论文-{os.path.basename(pdf_path)}'
if os.path.exists(rename_file):
os.remove(rename_file)
shutil.copyfile(pdf_path, rename_file)
if os.path.exists(pdf_path):
os.remove(pdf_path)
chatbot.append(("给出输出文件清单", str(generated_conclusion_files)))
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面