206 lines
9.2 KiB
Python
206 lines
9.2 KiB
Python
from toolbox import CatchException, report_execption, write_results_to_file
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from toolbox import update_ui
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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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def read_and_clean_pdf_text(fp):
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"""
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**输入参数说明**
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- `fp`：需要读取和清理文本的pdf文件路径
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**输出参数说明**
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- `meta_txt`：清理后的文本内容字符串
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- `page_one_meta`：第一页清理后的文本内容列表
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**函数功能**
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读取pdf文件并清理其中的文本内容，清理规则包括：
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- 提取所有块元的文本信息，并合并为一个字符串
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- 去除短块（字符数小于100）并替换为回车符
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- 清理多余的空行
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- 合并小写字母开头的段落块并替换为空格
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- 清除重复的换行
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- 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
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"""
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import fitz
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import re
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import numpy as np
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# file_content = ""
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with fitz.open(fp) as doc:
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meta_txt = []
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meta_font = []
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for index, page in enumerate(doc):
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# file_content += page.get_text()
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text_areas = page.get_text("dict") # 获取页面上的文本信息
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# 块元提取 for each word segment with in line for each line cross-line words for each block
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meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
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'- ', '') for t in text_areas['blocks'] if 'lines' in t])
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meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
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for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
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if index == 0:
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page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
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'- ', '') for t in text_areas['blocks'] if 'lines' in t]
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def 把字符太少的块清除为回车(meta_txt):
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for index, block_txt in enumerate(meta_txt):
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if len(block_txt) < 100:
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meta_txt[index] = '\n'
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return meta_txt
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meta_txt = 把字符太少的块清除为回车(meta_txt)
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def 清理多余的空行(meta_txt):
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for index in reversed(range(1, len(meta_txt))):
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if meta_txt[index] == '\n' and meta_txt[index-1] == '\n':
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meta_txt.pop(index)
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return meta_txt
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meta_txt = 清理多余的空行(meta_txt)
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def 合并小写开头的段落块(meta_txt):
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def starts_with_lowercase_word(s):
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pattern = r"^[a-z]+"
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match = re.match(pattern, s)
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if match:
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return True
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else:
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return False
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for _ in range(100):
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for index, block_txt in enumerate(meta_txt):
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if starts_with_lowercase_word(block_txt):
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if meta_txt[index-1] != '\n':
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meta_txt[index-1] += ' '
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else:
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meta_txt[index-1] = ''
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meta_txt[index-1] += meta_txt[index]
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meta_txt[index] = '\n'
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return meta_txt
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meta_txt = 合并小写开头的段落块(meta_txt)
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meta_txt = 清理多余的空行(meta_txt)
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meta_txt = '\n'.join(meta_txt)
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# 清除重复的换行
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for _ in range(5):
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meta_txt = meta_txt.replace('\n\n', '\n')
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# 换行 -> 双换行
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meta_txt = meta_txt.replace('\n', '\n\n')
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return meta_txt, page_one_meta
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@CatchException
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def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, sys_prompt, WEB_PORT):
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import glob
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import os
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# 基本信息：功能、贡献者
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chatbot.append([
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"函数插件功能？",
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"批量总结PDF文档。函数插件贡献者: Binary-Husky（二进制哈士奇）"])
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yield from update_ui(chatbot=chatbot, history=history)
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# 尝试导入依赖，如果缺少依赖，则给出安装建议
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try:
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import fitz
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import tiktoken
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except:
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report_execption(chatbot, history,
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a=f"解析项目: {txt}",
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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```。")
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yield from update_ui(chatbot=chatbot, history=history)
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return
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# 清空历史，以免输入溢出
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history = []
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# 检测输入参数，如没有给定输入参数，直接退出
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if os.path.exists(txt):
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project_folder = txt
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else:
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if txt == "":
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txt = '空空如也的输入栏'
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report_execption(chatbot, history,
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a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
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yield from update_ui(chatbot=chatbot, history=history)
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return
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# 搜索需要处理的文件清单
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file_manifest = [f for f in glob.glob(
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f'{project_folder}/**/*.pdf', recursive=True)]
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# 如果没找到任何文件
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if len(file_manifest) == 0:
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report_execption(chatbot, history,
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a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
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yield from update_ui(chatbot=chatbot, history=history)
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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(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt)
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def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt):
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import os
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import tiktoken
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TOKEN_LIMIT_PER_FRAGMENT = 1600
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generated_conclusion_files = []
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for index, fp in enumerate(file_manifest):
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# 读取PDF文件
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file_content, page_one = read_and_clean_pdf_text(fp)
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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 toolbox import get_conf
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enc = tiktoken.encoding_for_model(*get_conf('LLM_MODEL'))
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def get_token_num(txt): return len(enc.encode(txt))
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# 分解文本
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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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page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
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txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
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# 为了更好的效果，我们剥离Introduction之后的部分
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paper_meta = page_one_fragments[0].split('introduction')[0].split(
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'Introduction')[0].split('INTRODUCTION')[0]
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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_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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top_p=top_p, temperature=temperature,
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chatbot=chatbot, history=[],
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sys_prompt="Your job is to collect information from materials。",
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)
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# 多线，翻译
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gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
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inputs_array=[
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f"以下是你需要翻译的文章段落：\n{frag}" for frag in paper_fragments],
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inputs_show_user_array=[f"" for _ in paper_fragments],
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top_p=top_p, temperature=temperature,
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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，把整个段落翻译成中文，要求语言简洁，禁止重复输出原文。" for _ in paper_fragments],
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max_workers=16 # OpenAI所允许的最大并行过载
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)
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final = ["", paper_meta_info + '\n\n---\n\n---\n\n---\n\n']
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final.extend(gpt_response_collection)
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create_report_file_name = f"{os.path.basename(fp)}.trans.md"
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res = write_results_to_file(final, file_name=create_report_file_name)
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generated_conclusion_files.append(
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f'./gpt_log/{create_report_file_name}')
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chatbot.append((f"{fp}完成了吗？", res))
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msg = "完成"
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yield from update_ui(chatbot=chatbot, history=chatbot, msg=msg)
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# 准备文件的下载
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import shutil
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for pdf_path in generated_conclusion_files:
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# 重命名文件
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rename_file = f'./gpt_log/总结论文-{os.path.basename(pdf_path)}'
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if os.path.exists(rename_file):
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os.remove(rename_file)
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shutil.copyfile(pdf_path, rename_file)
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if os.path.exists(pdf_path):
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os.remove(pdf_path)
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chatbot.append(("给出输出文件清单", str(generated_conclusion_files)))
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yield from update_ui(chatbot=chatbot, history=chatbot, msg=msg)
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