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qingxu fu 2023-04-06 18:15:11 +08:00
parent e8cf757dc0
commit 0b3f7b8821
2 changed files with 86 additions and 60 deletions
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71
crazy_functions/crazy_utils.py
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@ -1,31 +1,32 @@
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): 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 import time
from concurrent.futures import ThreadPoolExecutor from concurrent.futures import ThreadPoolExecutor
from request_llm.bridge_chatgpt import predict_no_ui_long_connection from request_llm.bridge_chatgpt import predict_no_ui_long_connection
# 用户反馈 # 用户反馈
chatbot.append([inputs_show_user, ""]); msg = '正常' chatbot.append([inputs_show_user, ""])
msg = '正常'
yield chatbot, [], msg yield chatbot, [], msg
executor = ThreadPoolExecutor(max_workers=16) executor = ThreadPoolExecutor(max_workers=16)
mutable = ["", time.time()] mutable = ["", time.time()]
future = executor.submit(lambda: 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) predict_no_ui_long_connection(
) inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable)
)
while True: while True:
# yield一次以刷新前端页面 # yield一次以刷新前端页面
time.sleep(refresh_interval) time.sleep(refresh_interval)
# “喂狗”（看门狗） # “喂狗”（看门狗）
mutable[1] = time.time() mutable[1] = time.time()
if future.done(): break if future.done():
chatbot[-1] = [chatbot[-1][0], mutable[0]]; msg = "正常" break
chatbot[-1] = [chatbot[-1][0], mutable[0]]
msg = "正常"
yield chatbot, [], msg yield chatbot, [], msg
return future.result() 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): 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 import time
from concurrent.futures import ThreadPoolExecutor from concurrent.futures import ThreadPoolExecutor
@ -35,34 +36,46 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(inp
executor = ThreadPoolExecutor(max_workers=max_workers) executor = ThreadPoolExecutor(max_workers=max_workers)
n_frag = len(inputs_array) n_frag = len(inputs_array)
# 用户反馈 # 用户反馈
chatbot.append(["请开始多线程操作。", ""]); msg = '正常' chatbot.append(["请开始多线程操作。", ""])
msg = '正常'
yield chatbot, [], msg yield chatbot, [], msg
# 异步原子 # 异步原子
mutable = [["", time.time()] for _ in range(n_frag)] mutable = [["", time.time()] for _ in range(n_frag)]
def _req_gpt(index, inputs, history, sys_prompt): def _req_gpt(index, inputs, history, sys_prompt):
gpt_say = predict_no_ui_long_connection( 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] inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable[
index]
) )
return gpt_say return gpt_say
# 异步任务开始 # 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)] 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 cnt = 0
while True: while True:
# yield一次以刷新前端页面 # yield一次以刷新前端页面
time.sleep(refresh_interval); cnt += 1 time.sleep(refresh_interval)
cnt += 1
worker_done = [h.done() for h in futures] worker_done = [h.done() for h in futures]
if all(worker_done): executor.shutdown(); break if all(worker_done):
executor.shutdown()
break
# 更好的UI视觉效果 # 更好的UI视觉效果
observe_win = [] observe_win = []
# 每个线程都要“喂狗”（看门狗） # 每个线程都要“喂狗”（看门狗）
for thread_index, _ in enumerate(worker_done): mutable[thread_index][1] = time.time() for thread_index, _ in enumerate(worker_done):
mutable[thread_index][1] = time.time()
# 在前端打印些好玩的东西 # 在前端打印些好玩的东西
for thread_index, _ in enumerate(worker_done): for thread_index, _ in enumerate(worker_done):
print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\ print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
replace('\n','').replace('```','...').replace(' ','.').replace('<br/>','.....').replace('$','.')+"`... ]" replace('\n', '').replace('```', '...').replace(
' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
observe_win.append(print_something_really_funny) 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)]) stat_str = ''.join([f'执行中: {obs}\n\n' if not done else '已完成\n\n' for done, obs in zip(
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" 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 yield chatbot, [], msg
# 异步任务结束 # 异步任务结束
gpt_response_collection = [] gpt_response_collection = []
@ -72,23 +85,23 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(inp
return gpt_response_collection return gpt_response_collection
def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit): def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
def cut(txt_tocut, must_break_at_empty_line): # 递归 def cut(txt_tocut, must_break_at_empty_line): # 递归
if get_token_fn(txt_tocut) <= limit: if get_token_fn(txt_tocut) <= limit:
return [txt_tocut] return [txt_tocut]
else: else:
lines = txt_tocut.split('\n') lines = txt_tocut.split('\n')
estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines) estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
estimated_line_cut = int(estimated_line_cut) estimated_line_cut = int(estimated_line_cut)
for cnt in reversed(range(estimated_line_cut)): for cnt in reversed(range(estimated_line_cut)):
if must_break_at_empty_line: if must_break_at_empty_line:
if lines[cnt] != "": continue if lines[cnt] != "":
continue
print(cnt) print(cnt)
prev = "\n".join(lines[:cnt]) prev = "\n".join(lines[:cnt])
post = "\n".join(lines[cnt:]) post = "\n".join(lines[cnt:])
if get_token_fn(prev) < limit: break if get_token_fn(prev) < limit:
break
if cnt == 0: if cnt == 0:
print('what the fuck ?') print('what the fuck ?')
raise RuntimeError("存在一行极长的文本！") raise RuntimeError("存在一行极长的文本！")
@ -102,22 +115,25 @@ def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
except RuntimeError: except RuntimeError:
return cut(txt, must_break_at_empty_line=False) return cut(txt, must_break_at_empty_line=False)
def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit): def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
def cut(txt_tocut, must_break_at_empty_line): # 递归 def cut(txt_tocut, must_break_at_empty_line): # 递归
if get_token_fn(txt_tocut) <= limit: if get_token_fn(txt_tocut) <= limit:
return [txt_tocut] return [txt_tocut]
else: else:
lines = txt_tocut.split('\n') lines = txt_tocut.split('\n')
estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines) estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
estimated_line_cut = int(estimated_line_cut) estimated_line_cut = int(estimated_line_cut)
cnt = 0 cnt = 0
for cnt in reversed(range(estimated_line_cut)): for cnt in reversed(range(estimated_line_cut)):
if must_break_at_empty_line: if must_break_at_empty_line:
if lines[cnt] != "": continue if lines[cnt] != "":
continue
print(cnt) print(cnt)
prev = "\n".join(lines[:cnt]) prev = "\n".join(lines[:cnt])
post = "\n".join(lines[cnt:]) post = "\n".join(lines[cnt:])
if get_token_fn(prev) < limit: break if get_token_fn(prev) < limit:
break
if cnt == 0: if cnt == 0:
# print('what the fuck ? 存在一行极长的文本！') # print('what the fuck ? 存在一行极长的文本！')
raise RuntimeError("存在一行极长的文本！") raise RuntimeError("存在一行极长的文本！")
@ -135,4 +151,3 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
# 这个中文的句号是故意的，作为一个标识而存在 # 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) res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False)
return [r.replace('。\n', '.') for r in res] return [r.replace('。\n', '.') for r in res]

63
crazy_functions/批量翻译PDF文档_多线程.py
View File

@ -2,6 +2,7 @@ from toolbox import CatchException, report_execption, write_results_to_file
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
def read_and_clean_pdf_text(fp): def read_and_clean_pdf_text(fp):
""" """
**输入参数说明** **输入参数说明**
@ -20,7 +21,8 @@ def read_and_clean_pdf_text(fp):
- 清除重复的换行 - 清除重复的换行
- 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔 - 将每个换行符替换为两个换行符，使每个段落之间有两个换行符分隔
""" """
import fitz, re import fitz
import re
import numpy as np import numpy as np
# file_content = "" # file_content = ""
with fitz.open(fp) as doc: with fitz.open(fp) as doc:
@ -31,10 +33,13 @@ def read_and_clean_pdf_text(fp):
text_areas = page.get_text("dict") # 获取页面上的文本信息 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 # 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_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
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]) '- ', '') for t in text_areas['blocks'] if 'lines' in t])
if index==0: meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
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] 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): def 把字符太少的块清除为回车(meta_txt):
for index, block_txt in enumerate(meta_txt): for index, block_txt in enumerate(meta_txt):
@ -61,8 +66,10 @@ def read_and_clean_pdf_text(fp):
for _ in range(100): for _ in range(100):
for index, block_txt in enumerate(meta_txt): for index, block_txt in enumerate(meta_txt):
if starts_with_lowercase_word(block_txt): if starts_with_lowercase_word(block_txt):
if meta_txt[index-1]!='\n': meta_txt[index-1] += ' ' if meta_txt[index-1] != '\n':
else: meta_txt[index-1] = '' meta_txt[index-1] += ' '
else:
meta_txt[index-1] = ''
meta_txt[index-1] += meta_txt[index] meta_txt[index-1] += meta_txt[index]
meta_txt[index] = '\n' meta_txt[index] = '\n'
return meta_txt return meta_txt
@ -72,13 +79,14 @@ def read_and_clean_pdf_text(fp):
meta_txt = '\n'.join(meta_txt) meta_txt = '\n'.join(meta_txt)
# 清除重复的换行 # 清除重复的换行
for _ in range(5): for _ in range(5):
meta_txt = meta_txt.replace('\n\n','\n') meta_txt = meta_txt.replace('\n\n', '\n')
# 换行 -> 双换行 # 换行 -> 双换行
meta_txt = meta_txt.replace('\n', '\n\n') meta_txt = meta_txt.replace('\n', '\n\n')
return meta_txt, page_one_meta return meta_txt, page_one_meta
@CatchException @CatchException
def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, sys_prompt, WEB_PORT): def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, sys_prompt, WEB_PORT):
import glob import glob
@ -92,7 +100,8 @@ def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, sys_prompt,
# 尝试导入依赖，如果缺少依赖，则给出安装建议 # 尝试导入依赖，如果缺少依赖，则给出安装建议
try: try:
import fitz, tiktoken import fitz
import tiktoken
except: except:
report_execption(chatbot, history, report_execption(chatbot, history,
a=f"解析项目: {txt}", a=f"解析项目: {txt}",
@ -129,13 +138,8 @@ def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, sys_prompt,
yield from 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt) 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, sys_prompt): def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt):
import time
import glob
import os import os
import fitz
import tiktoken import tiktoken
TOKEN_LIMIT_PER_FRAGMENT = 1600 TOKEN_LIMIT_PER_FRAGMENT = 1600
generated_conclusion_files = [] generated_conclusion_files = []
@ -145,14 +149,15 @@ def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, histor
# 递归地切割PDF文件 # 递归地切割PDF文件
from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
enc = tiktoken.get_encoding("gpt2") enc = tiktoken.get_encoding("gpt2")
get_token_num = lambda txt: len(enc.encode(txt)) def get_token_num(txt): return len(enc.encode(txt))
# 分解文本 # 分解文本
paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf( paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT) 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( 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) 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 # 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] paper_meta = page_one_fragments[0].split('introduction')[0].split(
'Introduction')[0].split('INTRODUCTION')[0]
# 单线，获取文章meta信息 # 单线，获取文章meta信息
paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive( 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=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}",
@ -163,21 +168,25 @@ def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, histor
) )
# 多线，翻译 # 多线，翻译
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency( 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_array=[
inputs_show_user_array = [f"" for _ in paper_fragments], 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, top_p=top_p, temperature=temperature,
chatbot=chatbot, chatbot=chatbot,
history_array=[[paper_meta] for _ in paper_fragments], 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], sys_prompt_array=[
max_workers=16 # OpenAI所允许的最大并行过载 "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 = ["", paper_meta_info + '\n\n---\n\n---\n\n---\n\n']
final.extend(gpt_response_collection) final.extend(gpt_response_collection)
create_report_file_name = f"{os.path.basename(fp)}.trans.md" create_report_file_name = f"{os.path.basename(fp)}.trans.md"
res = write_results_to_file(final, file_name=create_report_file_name) res = write_results_to_file(final, file_name=create_report_file_name)
generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}') generated_conclusion_files.append(
chatbot.append((f"{fp}完成了吗？", res)); msg = "完成" f'./gpt_log/{create_report_file_name}')
chatbot.append((f"{fp}完成了吗？", res))
msg = "完成"
yield chatbot, history, msg yield chatbot, history, msg
# 准备文件的下载 # 准备文件的下载
@ -185,8 +194,10 @@ def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, histor
for pdf_path in generated_conclusion_files: for pdf_path in generated_conclusion_files:
# 重命名文件 # 重命名文件
rename_file = f'./gpt_log/总结论文-{os.path.basename(pdf_path)}' rename_file = f'./gpt_log/总结论文-{os.path.basename(pdf_path)}'
if os.path.exists(rename_file): os.remove(rename_file) if os.path.exists(rename_file):
shutil.copyfile(pdf_path, rename_file); os.remove(rename_file)
if os.path.exists(pdf_path): os.remove(pdf_path) shutil.copyfile(pdf_path, rename_file)
if os.path.exists(pdf_path):
os.remove(pdf_path)
chatbot.append(("给出输出文件清单", str(generated_conclusion_files))) chatbot.append(("给出输出文件清单", str(generated_conclusion_files)))
yield chatbot, history, msg yield chatbot, history, msg