139 lines
6.6 KiB
Python
139 lines
6.6 KiB
Python
import threading
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from request_llm.bridge_all import predict_no_ui_long_connection
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from toolbox import update_ui
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from toolbox import CatchException, write_results_to_file, report_execption
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit
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def extract_code_block_carefully(txt):
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splitted = txt.split('```')
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n_code_block_seg = len(splitted) - 1
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if n_code_block_seg <= 1: return txt
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# 剩下的情况都开头除去 ``` 结尾除去一次 ```
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txt_out = '```'.join(splitted[1:-1])
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return txt_out
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def break_txt_into_half_at_some_linebreak(txt):
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lines = txt.split('\n')
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n_lines = len(lines)
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pre = lines[:(n_lines//2)]
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post = lines[(n_lines//2):]
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return "\n".join(pre), "\n".join(post)
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@CatchException
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def 全项目切换英文(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
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# 第1步：清空历史，以免输入溢出
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history = []
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# 第2步：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 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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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 tiktoken```。")
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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# 第3步：集合文件
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import time, glob, os, shutil, re
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os.makedirs('gpt_log/generated_english_version', exist_ok=True)
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os.makedirs('gpt_log/generated_english_version/crazy_functions', exist_ok=True)
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file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
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[f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
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# file_manifest = ['./toolbox.py']
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i_say_show_user_buffer = []
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# 第4步：随便显示点什么防止卡顿的感觉
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for index, fp in enumerate(file_manifest):
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# if 'test_project' in fp: continue
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with open(fp, 'r', encoding='utf-8', errors='replace') as f:
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file_content = f.read()
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i_say_show_user =f'[{index}/{len(file_manifest)}] 接下来请将以下代码中包含的所有中文转化为英文，只输出转化后的英文代码，请用代码块输出代码: {os.path.abspath(fp)}'
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i_say_show_user_buffer.append(i_say_show_user)
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chatbot.append((i_say_show_user, "[Local Message] 等待多线程操作，中间过程不予显示."))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# 第5步：Token限制下的截断与处理
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MAX_TOKEN = 3000
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from request_llm.bridge_all import model_info
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=()))
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# 第6步：任务函数
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mutable_return = [None for _ in file_manifest]
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observe_window = [[""] for _ in file_manifest]
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def thread_worker(fp,index):
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if index > 10:
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time.sleep(60)
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print('Openai 限制免费用户每分钟20次请求，降低请求频率中。')
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with open(fp, 'r', encoding='utf-8', errors='replace') as f:
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file_content = f.read()
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i_say_template = lambda fp, file_content: f'接下来请将以下代码中包含的所有中文转化为英文，只输出代码，文件名是{fp}，文件代码是 ```{file_content}```'
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try:
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gpt_say = ""
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# 分解代码文件
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file_content_breakdown = breakdown_txt_to_satisfy_token_limit(file_content, get_token_fn, MAX_TOKEN)
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for file_content_partial in file_content_breakdown:
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i_say = i_say_template(fp, file_content_partial)
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# # ** gpt request **
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gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=observe_window[index])
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gpt_say_partial = extract_code_block_carefully(gpt_say_partial)
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gpt_say += gpt_say_partial
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mutable_return[index] = gpt_say
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except ConnectionAbortedError as token_exceed_err:
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print('至少一个线程任务Token溢出而失败', e)
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except Exception as e:
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print('至少一个线程任务意外失败', e)
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# 第7步：所有线程同时开始执行任务函数
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handles = [threading.Thread(target=thread_worker, args=(fp,index)) for index, fp in enumerate(file_manifest)]
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for h in handles:
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h.daemon = True
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h.start()
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chatbot.append(('开始了吗？', f'多线程操作已经开始'))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# 第8步：循环轮询各个线程是否执行完毕
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cnt = 0
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while True:
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cnt += 1
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time.sleep(0.2)
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th_alive = [h.is_alive() for h in handles]
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if not any(th_alive): break
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# 更好的UI视觉效果
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observe_win = []
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for thread_index, alive in enumerate(th_alive):
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observe_win.append("[ ..."+observe_window[thread_index][0][-60:].replace('\n','').replace('```','...').replace(' ','.').replace('<br/>','.....').replace('$','.')+"... ]")
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stat = [f'执行中: {obs}\n\n' if alive else '已完成\n\n' for alive, obs in zip(th_alive, observe_win)]
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stat_str = ''.join(stat)
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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)))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# 第9步：把结果写入文件
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for index, h in enumerate(handles):
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h.join() # 这里其实不需要join了，肯定已经都结束了
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fp = file_manifest[index]
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gpt_say = mutable_return[index]
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i_say_show_user = i_say_show_user_buffer[index]
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where_to_relocate = f'gpt_log/generated_english_version/{fp}'
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if gpt_say is not None:
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with open(where_to_relocate, 'w+', encoding='utf-8') as f:
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f.write(gpt_say)
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else: # 失败
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shutil.copyfile(file_manifest[index], where_to_relocate)
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chatbot.append((i_say_show_user, f'[Local Message] 已完成{os.path.abspath(fp)}的转化，\n\n存入{os.path.abspath(where_to_relocate)}'))
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history.append(i_say_show_user); history.append(gpt_say)
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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time.sleep(1)
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# 第10步：备份一个文件
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res = write_results_to_file(history)
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chatbot.append(("生成一份任务执行报告", res))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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