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from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down | |
import re | |
import unicodedata | |
fast_debug = False | |
def is_paragraph_break(match): | |
""" | |
根据给定的匹配结果来判断换行符是否表示段落分隔。 | |
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。 | |
也可以根据之前的内容长度来判断段落是否已经足够长。 | |
""" | |
prev_char, next_char = match.groups() | |
# 句子结束标志 | |
sentence_endings = ".!?" | |
# 设定一个最小段落长度阈值 | |
min_paragraph_length = 140 | |
if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length: | |
return "\n\n" | |
else: | |
return " " | |
def normalize_text(text): | |
""" | |
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。 | |
例如,将连字 "fi" 转换为 "f" 和 "i"。 | |
""" | |
# 对文本进行归一化处理,分解连字 | |
normalized_text = unicodedata.normalize("NFKD", text) | |
# 替换其他特殊字符 | |
cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text) | |
return cleaned_text | |
def clean_text(raw_text): | |
""" | |
对从 PDF 提取出的原始文本进行清洗和格式化处理。 | |
1. 对原始文本进行归一化处理。 | |
2. 替换跨行的连词,例如 “Espe-\ncially” 转换为 “Especially”。 | |
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。 | |
""" | |
# 对文本进行归一化处理 | |
normalized_text = normalize_text(raw_text) | |
# 替换跨行的连词 | |
text = re.sub(r'(\w+-\n\w+)', | |
lambda m: m.group(1).replace('-\n', ''), normalized_text) | |
# 根据前后相邻字符的特点,找到原文本中的换行符 | |
newlines = re.compile(r'(\S)\n(\S)') | |
# 根据 heuristic 规则,用空格或段落分隔符替换原换行符 | |
final_text = re.sub(newlines, lambda m: m.group( | |
1) + is_paragraph_break(m) + m.group(2), text) | |
return final_text.strip() | |
def read_and_clean_pdf_text(fp): | |
import fitz, re | |
import numpy as np | |
# file_content = "" | |
with fitz.open(fp) as doc: | |
meta_txt = [] | |
meta_font = [] | |
for page in doc: | |
# file_content += page.get_text() | |
text_areas = page.get_text("dict") # 获取页面上的文本信息 | |
# # 行元提取 for each word segment with in line for each line for each block | |
# 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]) | |
# 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]) | |
# 块元提取 for each word segment with in line for each line for each block | |
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]) | |
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]) | |
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') | |
# print(meta_txt) | |
return meta_txt | |
def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, systemPromptTxt, 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, ValeriaWong, Eralien"]) | |
yield chatbot, history, '正常' | |
# 尝试导入依赖,如果缺少依赖,则给出安装建议 | |
try: | |
import fitz, 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```。") | |
yield chatbot, history, '正常' | |
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 chatbot, history, '正常' | |
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 chatbot, history, '正常' | |
return | |
# 开始正式执行任务 | |
yield from 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt) | |
def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt): | |
import time | |
import glob | |
import os | |
import fitz | |
import tiktoken | |
from concurrent.futures import ThreadPoolExecutor | |
print('begin analysis on:', file_manifest) | |
for index, fp in enumerate(file_manifest): | |
### 1. 读取PDF文件 | |
file_content = read_and_clean_pdf_text(fp) | |
### 2. 递归地切割PDF文件 | |
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf | |
enc = tiktoken.get_encoding("gpt2") | |
TOKEN_LIMIT_PER_FRAGMENT = 2048 | |
get_token_num = lambda txt: len(enc.encode(txt)) | |
# 分解 | |
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( | |
txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT) | |
print([get_token_num(frag) for frag in paper_fragments]) | |
### 3. 逐个段落翻译 | |
## 3.1. 多线程开始 | |
from request_llm.bridge_chatgpt import predict_no_ui_long_connection | |
n_frag = len(paper_fragments) | |
# 异步原子 | |
mutable = [["", time.time()] for _ in range(n_frag)] | |
# 翻译函数 | |
def translate_(index, fragment, mutable): | |
i_say = f"以下是你需要翻译的文章段落:{fragment}" | |
# 请求gpt,需要一段时间 | |
gpt_say = predict_no_ui_long_connection( | |
inputs=i_say, top_p=top_p, temperature=temperature, history=[], # ["请翻译:" if len(previous_result)!=0 else "", previous_result], | |
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。", | |
observe_window=mutable[index]) | |
return gpt_say | |
### 4. 异步任务开始 | |
executor = ThreadPoolExecutor(max_workers=16) | |
# Submit tasks to the pool | |
futures = [executor.submit(translate_, index, frag, mutable) for index, frag in enumerate(paper_fragments)] | |
### 5. UI主线程,在任务期间提供实时的前端显示 | |
cnt = 0 | |
while True: | |
cnt += 1 | |
time.sleep(1) | |
worker_done = [h.done() for h in futures] | |
if all(worker_done): | |
executor.shutdown(); break | |
# 更好的UI视觉效果 | |
observe_win = [] | |
# 每个线程都要喂狗(看门狗) | |
for thread_index, _ in enumerate(worker_done): | |
mutable[thread_index][1] = time.time() | |
# 在前端打印些好玩的东西 | |
for thread_index, _ in enumerate(worker_done): | |
print_something_really_funny = "[ ...`"+mutable[thread_index][0][-30:].replace('\n','').replace('```','...').replace(' ','.').replace('<br/>','.....').replace('$','.')+"`... ]" | |
observe_win.append(print_something_really_funny) | |
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)]) | |
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" | |
yield chatbot, history, msg | |
# Wait for tasks to complete | |
results = [future.result() for future in futures] | |
print(results) | |
# full_result += gpt_say | |
# history.extend([fp, full_result]) | |
res = write_results_to_file(history) | |
chatbot.append(("完成了吗?", res)); msg = "完成" | |
yield chatbot, history, msg | |
# if __name__ == '__main__': | |
# pro() | |