gpt-academic-hust / crazy_functions /批量翻译PDF文档_多线程.py
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from toolbox import CatchException, report_execption, write_results_to_file, predict_no_ui_but_counting_down
import re
import unicodedata
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 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
# 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]
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')
return meta_txt, page_one_meta
@CatchException
def 批量翻译PDF文档(txt, top_p, temperature, 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(二进制哈士奇)"])
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 tiktoken```。")
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, sys_prompt)
def request_gpt_model_in_new_thread_with_ui_alive(inputs, inputs_show_user, top_p, temperature, chatbot, history, sys_prompt, refresh_interval=0.2):
import time
from concurrent.futures import ThreadPoolExecutor
from request_llm.bridge_chatgpt import predict_no_ui_long_connection
# 用户反馈
chatbot.append([inputs_show_user, ""]); msg = '正常'
yield chatbot, [], msg
executor = ThreadPoolExecutor(max_workers=16)
mutable = ["", time.time()]
future = executor.submit(lambda:
predict_no_ui_long_connection(inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable)
)
while True:
# yield一次以刷新前端页面
time.sleep(refresh_interval)
# “喂狗”(看门狗)
mutable[1] = time.time()
if future.done(): break
chatbot[-1] = [chatbot[-1][0], mutable[0]]; msg = "正常"
yield chatbot, [], msg
return future.result()
def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(inputs_array, inputs_show_user_array, top_p, temperature, chatbot, history_array, sys_prompt_array, refresh_interval=0.2, max_workers=10, scroller_max_len=30):
import time
from concurrent.futures import ThreadPoolExecutor
from request_llm.bridge_chatgpt import predict_no_ui_long_connection
assert len(inputs_array) == len(history_array)
assert len(inputs_array) == len(sys_prompt_array)
executor = ThreadPoolExecutor(max_workers=max_workers)
n_frag = len(inputs_array)
# 异步原子
mutable = [["", time.time()] for _ in range(n_frag)]
def _req_gpt(index, inputs, history, sys_prompt):
gpt_say = predict_no_ui_long_connection(
inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable[index]
)
return gpt_say
# 异步任务开始
futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
cnt = 0
while True:
# yield一次以刷新前端页面
time.sleep(refresh_interval); cnt += 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][-scroller_max_len:].\
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, [], msg
# 异步任务结束
gpt_response_collection = []
for inputs_show_user, f in zip(inputs_show_user_array, futures):
gpt_res = f.result()
gpt_response_collection.extend([inputs_show_user, gpt_res])
return gpt_response_collection
def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt):
import time
import glob
import os
import fitz
import tiktoken
TOKEN_LIMIT_PER_FRAGMENT = 1600
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
enc = tiktoken.get_encoding("gpt2")
get_token_num = lambda txt: len(enc.encode(txt))
# 分解文本
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
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。",
top_p=top_p, temperature=temperature,
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文章段落:\n{frag}" for frag in paper_fragments],
inputs_show_user_array = [f"" for _ in paper_fragments],
top_p=top_p, temperature=temperature,
chatbot=chatbot,
history_array=[[paper_meta] for _ in paper_fragments],
sys_prompt_array=["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],
max_workers=16 # OpenAI所允许的最大并行过载
)
final = ["", paper_meta_info + '\n\n---\n\n---\n\n---\n\n']
final.extend(gpt_response_collection)
res = write_results_to_file(final)
chatbot.append((f"{fp}完成了吗?", res)); msg = "完成"
yield chatbot, history, msg