256 lines
11 KiB
Python
256 lines
11 KiB
Python
from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
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import re
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import unicodedata
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fast_debug = False
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def is_paragraph_break(match):
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"""
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根据给定的匹配结果来判断换行符是否表示段落分隔。
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如果换行符前为句子结束标志（句号，感叹号，问号），且下一个字符为大写字母，则换行符更有可能表示段落分隔。
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也可以根据之前的内容长度来判断段落是否已经足够长。
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"""
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prev_char, next_char = match.groups()
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# 句子结束标志
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sentence_endings = ".!?"
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# 设定一个最小段落长度阈值
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min_paragraph_length = 140
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if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length:
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return "\n\n"
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else:
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return " "
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def normalize_text(text):
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"""
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通过把连字（ligatures）等文本特殊符号转换为其基本形式来对文本进行归一化处理。
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例如，将连字 "fi" 转换为 "f" 和 "i"。
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"""
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# 对文本进行归一化处理，分解连字
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normalized_text = unicodedata.normalize("NFKD", text)
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# 替换其他特殊字符
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cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text)
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return cleaned_text
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def clean_text(raw_text):
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"""
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对从 PDF 提取出的原始文本进行清洗和格式化处理。
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1. 对原始文本进行归一化处理。
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2. 替换跨行的连词，例如 “Espe-\ncially” 转换为 “Especially”。
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3. 根据 heuristic 规则判断换行符是否是段落分隔，并相应地进行替换。
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"""
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# 对文本进行归一化处理
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normalized_text = normalize_text(raw_text)
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# 替换跨行的连词
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text = re.sub(r'(\w+-\n\w+)',
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lambda m: m.group(1).replace('-\n', ''), normalized_text)
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# 根据前后相邻字符的特点，找到原文本中的换行符
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newlines = re.compile(r'(\S)\n(\S)')
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# 根据 heuristic 规则，用空格或段落分隔符替换原换行符
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final_text = re.sub(newlines, lambda m: m.group(
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1) + is_paragraph_break(m) + m.group(2), text)
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return final_text.strip()
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def read_and_clean_pdf_text(fp):
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import fitz, 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 page in 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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# # 行元提取 for each word segment with in line for each line for each block
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# meta_txt.extend( [ ["".join( [wtf['text'] for wtf in l['spans'] ]) for l in t['lines'] ] for t in text_areas['blocks'] if 'lines' in t])
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# meta_font.extend([ [ np.mean([wtf['size'] for wtf in l['spans'] ]) for l in t['lines'] ] for t in text_areas['blocks'] if 'lines' in t])
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# 块元提取 for each word segment with in line for each line for each block
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meta_txt.extend( [ " ".join(["".join( [wtf['text'] for wtf in l['spans'] ]) for l in t['lines'] ]) 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'] ]) for l in t['lines'] ]) 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': meta_txt[index-1] += ' '
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else: 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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# print(meta_txt)
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return meta_txt
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@CatchException
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def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, systemPromptTxt, 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, ValeriaWong, Eralien"])
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yield chatbot, history, '正常'
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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, 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```。")
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yield chatbot, history, '正常'
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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 chatbot, history, '正常'
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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 chatbot, history, '正常'
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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, systemPromptTxt)
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def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt):
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import time
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import glob
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import os
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import fitz
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import tiktoken
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from concurrent.futures import ThreadPoolExecutor
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print('begin analysis on:', file_manifest)
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for index, fp in enumerate(file_manifest):
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### 1. 读取PDF文件
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file_content = read_and_clean_pdf_text(fp)
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### 2. 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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enc = tiktoken.get_encoding("gpt2")
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TOKEN_LIMIT_PER_FRAGMENT = 2048
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get_token_num = lambda txt: len(enc.encode(txt))
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# 分解
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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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print([get_token_num(frag) for frag in paper_fragments])
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### 3. 逐个段落翻译
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## 3.1. 多线程开始
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from request_llm.bridge_chatgpt import predict_no_ui_long_connection
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n_frag = len(paper_fragments)
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# 异步原子
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mutable = [["", time.time()] for _ in range(n_frag)]
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# 翻译函数
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def translate_(index, fragment, mutable):
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i_say = f"以下是你需要翻译的文章段落：{fragment}"
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# 请求gpt，需要一段时间
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gpt_say = predict_no_ui_long_connection(
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inputs=i_say, top_p=top_p, temperature=temperature, history=[], # ["请翻译：" if len(previous_result)!=0 else "", previous_result],
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sys_prompt="请你作为一个学术翻译，负责将给定的文章段落翻译成中文，要求语言简洁、精准、凝练。你只需要给出翻译后的文本，不能重复原文。",
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observe_window=mutable[index])
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return gpt_say
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### 4. 异步任务开始
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executor = ThreadPoolExecutor(max_workers=16)
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# Submit tasks to the pool
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futures = [executor.submit(translate_, index, frag, mutable) for index, frag in enumerate(paper_fragments)]
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### 5. UI主线程，在任务期间提供实时的前端显示
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cnt = 0
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while True:
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cnt += 1
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time.sleep(1)
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worker_done = [h.done() for h in futures]
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if all(worker_done):
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executor.shutdown(); break
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# 更好的UI视觉效果
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observe_win = []
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# 每个线程都要喂狗（看门狗）
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for thread_index, _ in enumerate(worker_done):
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mutable[thread_index][1] = time.time()
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# 在前端打印些好玩的东西
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for thread_index, _ in enumerate(worker_done):
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print_something_really_funny = "[ ...`"+mutable[thread_index][0][-30:].replace('\n','').replace('```','...').replace(' ','.').replace('<br/>','.....').replace('$','.')+"`... ]"
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observe_win.append(print_something_really_funny)
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stat_str = ''.join([f'执行中: {obs}\n\n' if not done else '已完成\n\n' for done, obs in zip(worker_done, observe_win)])
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chatbot[-1] = [chatbot[-1][0], f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt%10+1))]; msg = "正常"
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yield chatbot, history, msg
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# Wait for tasks to complete
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results = [future.result() for future in futures]
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print(results)
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# full_result += gpt_say
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# history.extend([fp, full_result])
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res = write_results_to_file(history)
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chatbot.append(("完成了吗？", res)); msg = "完成"
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yield chatbot, history, msg
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# if __name__ == '__main__':
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# pro()
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