diff --git a/crazy_functions/crazy_utils.py b/crazy_functions/crazy_utils.py
index f1b5e82..49732f3 100644
--- a/crazy_functions/crazy_utils.py
+++ b/crazy_functions/crazy_utils.py
@@ -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]
+
diff --git a/crazy_functions/批量翻译PDF文档_多线程.py b/crazy_functions/批量翻译PDF文档_多线程.py
index 91ad003..c8f72b8 100644
--- a/crazy_functions/批量翻译PDF文档_多线程.py
+++ b/crazy_functions/批量翻译PDF文档_多线程.py
@@ -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('
','.....').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('
','.....').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()
diff --git a/crazy_functions/高级功能函数模板.py b/crazy_functions/高级功能函数模板.py
index 69fd379..95e8240 100644
--- a/crazy_functions/高级功能函数模板.py
+++ b/crazy_functions/高级功能函数模板.py
@@ -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):