gpt-academic / crazy_functions /批量翻译PDF文档_多线程.py
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重命名一些函数
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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
@CatchException
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()