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Your Name 2023-04-06 03:30:02 +08:00
parent dcaa7a1808
commit 85d85d850a
3 changed files with 119 additions and 85 deletions
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11
crazy_functions/crazy_utils.py
View File

@ -37,6 +37,7 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
lines = txt_tocut.split('\n')
estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
estimated_line_cut = int(estimated_line_cut)
cnt = 0
for cnt in reversed(range(estimated_line_cut)):
if must_break_at_empty_line:
if lines[cnt] != "": continue
@ -45,7 +46,7 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
post = "\n".join(lines[cnt:])
if get_token_fn(prev) < limit: break
if cnt == 0:
print('what the fuck ?')
# print('what the fuck ? 存在一行极长的文本！')
raise RuntimeError("存在一行极长的文本！")
# print(len(post))
# 列表递归接龙
@ -55,4 +56,10 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
try:
return cut(txt, must_break_at_empty_line=True)
except RuntimeError:
return cut(txt, must_break_at_empty_line=False)
try:
return cut(txt, must_break_at_empty_line=False)
except RuntimeError:
# 这个中文的句号是故意的，作为一个标识而存在
res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False)
return [r.replace('。\n', '.') for r in res]

191
crazy_functions/批量翻译PDF文档_多线程.py
View File

@ -1,7 +1,6 @@
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):
@ -61,7 +60,6 @@ def clean_text(raw_text):
return final_text.strip()
def read_and_clean_pdf_text(fp):
import fitz, re
import numpy as np
@ -69,19 +67,16 @@ def read_and_clean_pdf_text(fp):
with fitz.open(fp) as doc:
meta_txt = []
meta_font = []
for page in doc:
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]
# # 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:
@ -123,19 +118,17 @@ def read_and_clean_pdf_text(fp):
# 换行 -> 双换行
meta_txt = meta_txt.replace('\n', '\n\n')
# print(meta_txt)
return meta_txt
return meta_txt, page_one_meta
@CatchException
def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
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, ValeriaWong, Eralien"])
"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
@ -174,82 +167,116 @@ def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, systemPromp
return
# 开始正式执行任务
yield from 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt)
yield from 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt)
def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, systemPromptTxt):
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, 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
from concurrent.futures import ThreadPoolExecutor
print('begin analysis on:', file_manifest)
TOKEN_LIMIT_PER_FRAGMENT = 1600
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
# 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")
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
# 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
)
# Wait for tasks to complete
results = [future.result() for future in futures]
final = ["", paper_meta_info + '\n\n---\n\n---\n\n---\n\n'].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
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()

2
crazy_functions/高级功能函数模板.py
View File

@ -5,7 +5,7 @@ import datetime
@CatchException
def 高阶功能模板函数(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
history = [] # 清空历史，以免输入溢出
chatbot.append(("这是什么功能？", "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板。为了做到简单易读，该函数只有25行代码，所以不会实时反馈文字流或心跳，请耐心等待程序输出完成。此外我们也提供可同步处理大量文件的多线程Demo供您参考。您若希望分享新的功能模组，请不吝PR！"))
chatbot.append(("这是什么功能？", "[Local Message] 请注意，您正在调用一个[函数插件]的模板，该函数面向希望实现更多有趣功能的开发者，它可以作为创建新功能函数的模板(该函数只有25行代码)。此外我们也提供可同步处理大量文件的多线程Demo供您参考。您若希望分享新的功能模组，请不吝PR！"))
yield chatbot, history, '正常' # 由于请求gpt需要一段时间，我们先及时地做一次状态显示
for i in range(5):