import threading from request_llm.bridge_chatgpt import predict_no_ui_long_connection from toolbox import CatchException, write_results_to_file, report_execption def extract_code_block_carefully(txt): splitted = txt.split('```') n_code_block_seg = len(splitted) - 1 if n_code_block_seg <= 1: return txt # 剩下的情况都开头除去 ``` 结尾除去一次 ``` txt_out = '```'.join(splitted[1:-1]) return txt_out def breakdown_txt_to_satisfy_token_limit(txt, get_token_fn, limit, must_break_at_empty_line=True): def cut(txt_tocut, must_break_at_empty_line): # 递归 if get_token_fn(txt_tocut) <= limit: return [txt_tocut] else: lines = txt_tocut.split('\n') estimated_line_cut = limit / get_token_fn(txt_tocut) * len(lines) estimated_line_cut = int(estimated_line_cut) for cnt in reversed(range(estimated_line_cut)): if must_break_at_empty_line: if lines[cnt] != "": continue print(cnt) prev = "\n".join(lines[:cnt]) post = "\n".join(lines[cnt:]) if get_token_fn(prev) < limit: break if cnt == 0: print('what the f?') raise RuntimeError("存在一行极长的文本!") print(len(post)) # 列表递归接龙 result = [prev] result.extend(cut(post, must_break_at_empty_line)) return result try: return cut(txt, must_break_at_empty_line=True) except RuntimeError: return cut(txt, must_break_at_empty_line=False) def break_txt_into_half_at_some_linebreak(txt): lines = txt.split('\n') n_lines = len(lines) pre = lines[:(n_lines//2)] post = lines[(n_lines//2):] return "\n".join(pre), "\n".join(post) @CatchException def 全项目切换英文(txt, top_p, temperature, chatbot, history, sys_prompt, WEB_PORT): # 第1步:清空历史,以免输入溢出 history = [] # 第2步:尝试导入依赖,如果缺少依赖,则给出安装建议 try: import openai, transformers except: report_execption(chatbot, history, a = f"解析项目: {txt}", 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 openai transformers```。") yield chatbot, history, '正常' return # 第3步:集合文件 import time, glob, os, shutil, re, openai os.makedirs('gpt_log/generated_english_version', exist_ok=True) os.makedirs('gpt_log/generated_english_version/crazy_functions', exist_ok=True) file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \ [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)] # file_manifest = ['./toolbox.py'] i_say_show_user_buffer = [] # 第4步:随便显示点什么防止卡顿的感觉 for index, fp in enumerate(file_manifest): # if 'test_project' in fp: continue with open(fp, 'r', encoding='utf-8') as f: file_content = f.read() 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)}' i_say_show_user_buffer.append(i_say_show_user) chatbot.append((i_say_show_user, "[Local Message] 等待多线程操作,中间过程不予显示.")) yield chatbot, history, '正常' # 第5步:Token限制下的截断与处理 MAX_TOKEN = 3000 from transformers import GPT2TokenizerFast print('加载tokenizer中') tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") get_token_fn = lambda txt: len(tokenizer(txt)["input_ids"]) print('加载tokenizer结束') # 第6步:任务函数 mutable_return = [None for _ in file_manifest] observe_window = [[""] for _ in file_manifest] def thread_worker(fp,index): if index > 10: time.sleep(60) print('Openai 限制免费用户每分钟20次请求,降低请求频率中。') with open(fp, 'r', encoding='utf-8') as f: file_content = f.read() 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}```' try: gpt_say = "" # 分解代码文件 file_content_breakdown = breakdown_txt_to_satisfy_token_limit(file_content, get_token_fn, MAX_TOKEN) for file_content_partial in file_content_breakdown: i_say = i_say_template(fp, file_content_partial) # # ** gpt request ** gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, top_p=top_p, temperature=temperature, history=[], sys_prompt=sys_prompt, observe_window=observe_window[index]) gpt_say_partial = extract_code_block_carefully(gpt_say_partial) gpt_say += gpt_say_partial mutable_return[index] = gpt_say except ConnectionAbortedError as token_exceed_err: print('至少一个线程任务Token溢出而失败', e) except Exception as e: print('至少一个线程任务意外失败', e) # 第7步:所有线程同时开始执行任务函数 handles = [threading.Thread(target=thread_worker, args=(fp,index)) for index, fp in enumerate(file_manifest)] for h in handles: h.daemon = True h.start() chatbot.append(('开始了吗?', f'多线程操作已经开始')) yield chatbot, history, '正常' # 第8步:循环轮询各个线程是否执行完毕 cnt = 0 while True: cnt += 1 time.sleep(0.2) th_alive = [h.is_alive() for h in handles] if not any(th_alive): break # 更好的UI视觉效果 observe_win = [] for thread_index, alive in enumerate(th_alive): observe_win.append("[ ..."+observe_window[thread_index][0][-60:].replace('\n','').replace('```','...').replace(' ','.').replace('
','.....').replace('$','.')+"... ]") 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)] stat_str = ''.join(stat) 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))) yield chatbot, history, '正常' # 第9步:把结果写入文件 for index, h in enumerate(handles): h.join() # 这里其实不需要join了,肯定已经都结束了 fp = file_manifest[index] gpt_say = mutable_return[index] i_say_show_user = i_say_show_user_buffer[index] where_to_relocate = f'gpt_log/generated_english_version/{fp}' if gpt_say is not None: with open(where_to_relocate, 'w+', encoding='utf-8') as f: f.write(gpt_say) else: # 失败 shutil.copyfile(file_manifest[index], where_to_relocate) 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)}')) history.append(i_say_show_user); history.append(gpt_say) yield chatbot, history, '正常' time.sleep(1) # 第10步:备份一个文件 res = write_results_to_file(history) chatbot.append(("生成一份任务执行报告", res)) yield chatbot, history, '正常'