format file
This commit is contained in:
qingxu fu
parent
e8cf757dc0
commit
0b3f7b8821
@ -1,31 +1,32 @@
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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):
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import time
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from concurrent.futures import ThreadPoolExecutor
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from request_llm.bridge_chatgpt import predict_no_ui_long_connection
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# 用户反馈
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chatbot.append([inputs_show_user, ""]); msg = '正常'
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chatbot.append([inputs_show_user, ""])
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msg = '正常'
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yield chatbot, [], msg
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executor = ThreadPoolExecutor(max_workers=16)
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mutable = ["", time.time()]
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future = executor.submit(lambda:
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predict_no_ui_long_connection(inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable)
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)
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predict_no_ui_long_connection(
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inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable)
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)
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while True:
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# yield一次以刷新前端页面
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time.sleep(refresh_interval)
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# “喂狗”（看门狗）
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mutable[1] = time.time()
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if future.done(): break
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chatbot[-1] = [chatbot[-1][0], mutable[0]]; msg = "正常"
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if future.done():
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break
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chatbot[-1] = [chatbot[-1][0], mutable[0]]
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msg = "正常"
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yield chatbot, [], msg
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return future.result()
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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):
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import time
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from concurrent.futures import ThreadPoolExecutor
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@ -35,34 +36,46 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(inp
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executor = ThreadPoolExecutor(max_workers=max_workers)
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n_frag = len(inputs_array)
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# 用户反馈
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chatbot.append(["请开始多线程操作。", ""]); msg = '正常'
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chatbot.append(["请开始多线程操作。", ""])
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msg = '正常'
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yield chatbot, [], msg
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# 异步原子
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mutable = [["", time.time()] for _ in range(n_frag)]
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def _req_gpt(index, inputs, history, sys_prompt):
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gpt_say = predict_no_ui_long_connection(
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inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable[index]
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inputs=inputs, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt, observe_window=mutable[
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index]
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)
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return gpt_say
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# 异步任务开始
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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)]
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futures = [executor.submit(_req_gpt, index, inputs, history, sys_prompt) for index, inputs, history, sys_prompt in zip(
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range(len(inputs_array)), inputs_array, history_array, sys_prompt_array)]
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cnt = 0
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while True:
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# yield一次以刷新前端页面
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time.sleep(refresh_interval); cnt += 1
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time.sleep(refresh_interval)
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cnt += 1
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worker_done = [h.done() for h in futures]
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if all(worker_done): executor.shutdown(); break
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if all(worker_done):
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executor.shutdown()
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break
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# 更好的UI视觉效果
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observe_win = []
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# 每个线程都要“喂狗”（看门狗）
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for thread_index, _ in enumerate(worker_done): mutable[thread_index][1] = time.time()
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for thread_index, _ in enumerate(worker_done):
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mutable[thread_index][1] = time.time()
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# 在前端打印些好玩的东西
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for thread_index, _ in enumerate(worker_done):
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print_something_really_funny = "[ ...`"+mutable[thread_index][0][-scroller_max_len:].\
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replace('\n','').replace('```','...').replace(' ','.').replace('<br/>','.....').replace('$','.')+"`... ]"
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replace('\n', '').replace('```', '...').replace(
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' ', '.').replace('<br/>', '.....').replace('$', '.')+"`... ]"
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observe_win.append(print_something_really_funny)
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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)])
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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"
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stat_str = ''.join([f'执行中: {obs}\n\n' if not done else '已完成\n\n' for done, obs in zip(
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worker_done, observe_win)])
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chatbot[-1] = [chatbot[-1][0],
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f'多线程操作已经开始，完成情况: \n\n{stat_str}' + ''.join(['.']*(cnt % 10+1))]
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msg = "正常"
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yield chatbot, [], msg
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# 异步任务结束
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gpt_response_collection = []
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@ -72,23 +85,23 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(inp
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return gpt_response_collection
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def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
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def cut(txt_tocut, must_break_at_empty_line): # 递归
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def cut(txt_tocut, must_break_at_empty_line): # 递归
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if get_token_fn(txt_tocut) <= limit:
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return [txt_tocut]
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else:
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lines = txt_tocut.split('\n')
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estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
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estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
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estimated_line_cut = int(estimated_line_cut)
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for cnt in reversed(range(estimated_line_cut)):
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if must_break_at_empty_line:
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if lines[cnt] != "": continue
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if lines[cnt] != "":
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continue
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print(cnt)
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prev = "\n".join(lines[:cnt])
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post = "\n".join(lines[cnt:])
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if get_token_fn(prev) < limit: break
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if get_token_fn(prev) < limit:
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break
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if cnt == 0:
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print('what the fuck ?')
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raise RuntimeError("存在一行极长的文本！")
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@ -102,22 +115,25 @@ def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit):
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except RuntimeError:
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return cut(txt, must_break_at_empty_line=False)
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def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
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def cut(txt_tocut, must_break_at_empty_line): # 递归
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def cut(txt_tocut, must_break_at_empty_line): # 递归
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if get_token_fn(txt_tocut) <= limit:
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return [txt_tocut]
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else:
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lines = txt_tocut.split('\n')
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estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
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estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines)
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estimated_line_cut = int(estimated_line_cut)
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cnt = 0
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for cnt in reversed(range(estimated_line_cut)):
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if must_break_at_empty_line:
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if lines[cnt] != "": continue
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if lines[cnt] != "":
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continue
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print(cnt)
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prev = "\n".join(lines[:cnt])
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post = "\n".join(lines[cnt:])
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if get_token_fn(prev) < limit: break
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if get_token_fn(prev) < limit:
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break
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if cnt == 0:
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# print('what the fuck ? 存在一行极长的文本！')
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raise RuntimeError("存在一行极长的文本！")
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@ -135,4 +151,3 @@ def breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn, limit):
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# 这个中文的句号是故意的，作为一个标识而存在
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res = cut(txt.replace('.', '。\n'), must_break_at_empty_line=False)
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return [r.replace('。\n', '.') for r in res]
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@ -2,6 +2,7 @@ from toolbox import CatchException, report_execption, write_results_to_file
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
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from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
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def read_and_clean_pdf_text(fp):
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"""
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**输入参数说明**
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@ -20,7 +21,8 @@ def read_and_clean_pdf_text(fp):
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- 清除重复的换行
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- 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
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"""
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import fitz, re
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import fitz
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import re
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import numpy as np
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# file_content = ""
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with fitz.open(fp) as doc:
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@ -31,10 +33,13 @@ def read_and_clean_pdf_text(fp):
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text_areas = page.get_text("dict") # 获取页面上的文本信息
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# 块元提取 for each word segment with in line for each line cross-line words for each block
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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])
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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])
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if index==0:
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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]
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meta_txt.extend([" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
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'- ', '') for t in text_areas['blocks'] if 'lines' in t])
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meta_font.extend([np.mean([np.mean([wtf['size'] for wtf in l['spans']])
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for l in t['lines']]) for t in text_areas['blocks'] if 'lines' in t])
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if index == 0:
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page_one_meta = [" ".join(["".join([wtf['text'] for wtf in l['spans']]) for l in t['lines']]).replace(
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'- ', '') for t in text_areas['blocks'] if 'lines' in t]
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def 把字符太少的块清除为回车(meta_txt):
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for index, block_txt in enumerate(meta_txt):
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@ -61,8 +66,10 @@ def read_and_clean_pdf_text(fp):
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for _ in range(100):
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for index, block_txt in enumerate(meta_txt):
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if starts_with_lowercase_word(block_txt):
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if meta_txt[index-1]!='\n': meta_txt[index-1] += ' '
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else: meta_txt[index-1] = ''
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if meta_txt[index-1] != '\n':
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meta_txt[index-1] += ' '
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else:
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meta_txt[index-1] = ''
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meta_txt[index-1] += meta_txt[index]
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meta_txt[index] = '\n'
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return meta_txt
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@ -72,13 +79,14 @@ def read_and_clean_pdf_text(fp):
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meta_txt = '\n'.join(meta_txt)
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# 清除重复的换行
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for _ in range(5):
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meta_txt = meta_txt.replace('\n\n','\n')
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meta_txt = meta_txt.replace('\n\n', '\n')
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# 换行 -> 双换行
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meta_txt = meta_txt.replace('\n', '\n\n')
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return meta_txt, page_one_meta
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@CatchException
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def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, sys_prompt, WEB_PORT):
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import glob
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@ -92,7 +100,8 @@ def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, sys_prompt,
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# 尝试导入依赖，如果缺少依赖，则给出安装建议
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try:
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import fitz, tiktoken
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import fitz
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import tiktoken
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except:
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report_execption(chatbot, history,
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a=f"解析项目: {txt}",
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@ -129,13 +138,8 @@ def 批量翻译PDF文档(txt, top_p, temperature, chatbot, history, sys_prompt,
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yield from 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt)
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def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, history, sys_prompt):
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import time
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import glob
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import os
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import fitz
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import tiktoken
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TOKEN_LIMIT_PER_FRAGMENT = 1600
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generated_conclusion_files = []
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@ -145,14 +149,15 @@ def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, histor
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# 递归地切割PDF文件
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
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enc = tiktoken.get_encoding("gpt2")
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get_token_num = lambda txt: len(enc.encode(txt))
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def get_token_num(txt): return len(enc.encode(txt))
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# 分解文本
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paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
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paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
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txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
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page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
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txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
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# 为了更好的效果，我们剥离Introduction之后的部分
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paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
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paper_meta = page_one_fragments[0].split('introduction')[0].split(
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'Introduction')[0].split('INTRODUCTION')[0]
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# 单线，获取文章meta信息
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paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
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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}",
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@ -163,21 +168,25 @@ def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, histor
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)
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# 多线，翻译
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gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
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inputs_array = [f"以下是你需要翻译的文章段落：\n{frag}" for frag in paper_fragments],
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inputs_show_user_array = [f"" for _ in paper_fragments],
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inputs_array=[
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f"以下是你需要翻译的文章段落：\n{frag}" for frag in paper_fragments],
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inputs_show_user_array=[f"" for _ in paper_fragments],
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top_p=top_p, temperature=temperature,
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chatbot=chatbot,
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history_array=[[paper_meta] for _ in paper_fragments],
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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],
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max_workers=16 # OpenAI所允许的最大并行过载
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sys_prompt_array=[
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"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],
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max_workers=16 # OpenAI所允许的最大并行过载
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)
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final = ["", paper_meta_info + '\n\n---\n\n---\n\n---\n\n']
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final.extend(gpt_response_collection)
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create_report_file_name = f"{os.path.basename(fp)}.trans.md"
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res = write_results_to_file(final, file_name=create_report_file_name)
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generated_conclusion_files.append(f'./gpt_log/{create_report_file_name}')
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chatbot.append((f"{fp}完成了吗？", res)); msg = "完成"
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generated_conclusion_files.append(
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f'./gpt_log/{create_report_file_name}')
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chatbot.append((f"{fp}完成了吗？", res))
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msg = "完成"
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yield chatbot, history, msg
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# 准备文件的下载
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@ -185,8 +194,10 @@ def 解析PDF(file_manifest, project_folder, top_p, temperature, chatbot, histor
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for pdf_path in generated_conclusion_files:
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# 重命名文件
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rename_file = f'./gpt_log/总结论文-{os.path.basename(pdf_path)}'
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if os.path.exists(rename_file): os.remove(rename_file)
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shutil.copyfile(pdf_path, rename_file);
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if os.path.exists(pdf_path): os.remove(pdf_path)
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if os.path.exists(rename_file):
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os.remove(rename_file)
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shutil.copyfile(pdf_path, rename_file)
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if os.path.exists(pdf_path):
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os.remove(pdf_path)
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chatbot.append(("给出输出文件清单", str(generated_conclusion_files)))
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yield chatbot, history, msg
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