import markdown, mdtex2html, threading from show_math import convert as convert_math from functools import wraps def predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, top_p, temperature, history=[], sys_prompt=''): """ 调用简单的predict_no_ui接口,但是依然保留了些许界面心跳功能,当对话太长时,会自动采用二分法截断 """ import time try: from config_private import TIMEOUT_SECONDS, MAX_RETRY except: from config import TIMEOUT_SECONDS, MAX_RETRY from predict import predict_no_ui # 多线程的时候,需要一个mutable结构在不同线程之间传递信息 # list就是最简单的mutable结构,我们第一个位置放gpt输出,第二个位置传递报错信息 mutable = [None, ''] # multi-threading worker def mt(i_say, history): while True: try: mutable[0] = predict_no_ui(inputs=i_say, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt) break except ConnectionAbortedError as e: if len(history) > 0: history = [his[len(his)//2:] for his in history if his is not None] mutable[1] = 'Warning! History conversation is too long, cut into half. ' else: i_say = i_say[:len(i_say)//2] mutable[1] = 'Warning! Input file is too long, cut into half. ' except TimeoutError as e: mutable[0] = '[Local Message] Failed with timeout.' raise TimeoutError # 创建新线程发出http请求 thread_name = threading.Thread(target=mt, args=(i_say, history)); thread_name.start() # 原来的线程则负责持续更新UI,实现一个超时倒计时,并等待新线程的任务完成 cnt = 0 while thread_name.is_alive(): cnt += 1 chatbot[-1] = (i_say_show_user, f"[Local Message] {mutable[1]}waiting gpt response {cnt}/{TIMEOUT_SECONDS*2*(MAX_RETRY+1)}"+''.join(['.']*(cnt%4))) yield chatbot, history, '正常' time.sleep(1) # 把gpt的输出从mutable中取出来 gpt_say = mutable[0] if gpt_say=='[Local Message] Failed with timeout.': raise TimeoutError return gpt_say def write_results_to_file(history, file_name=None): """ 将对话记录history以Markdown格式写入文件中。如果没有指定文件名,则使用当前时间生成文件名。 """ import os, time if file_name is None: # file_name = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md' file_name = 'chatGPT分析报告' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md' os.makedirs('./gpt_log/', exist_ok=True) with open(f'./gpt_log/{file_name}', 'w', encoding = 'utf8') as f: f.write('# chatGPT 分析报告\n') for i, content in enumerate(history): if i%2==0: f.write('## ') f.write(content) f.write('\n\n') res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}') print(res) return res def regular_txt_to_markdown(text): """ 将普通文本转换为Markdown格式的文本。 """ text = text.replace('\n', '\n\n') text = text.replace('\n\n\n', '\n\n') text = text.replace('\n\n\n', '\n\n') return text def CatchException(f): """ 装饰器函数,捕捉函数f中的异常并封装到一个生成器中返回,并显示到聊天当中。 """ @wraps(f) def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT): try: yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT) except Exception as e: import traceback from check_proxy import check_proxy try: from config_private import proxies except: from config import proxies tb_str = regular_txt_to_markdown(traceback.format_exc()) chatbot[-1] = (chatbot[-1][0], f"[Local Message] 实验性函数调用出错: \n\n {tb_str} \n\n 当前代理可用性: \n\n {check_proxy(proxies)}") yield chatbot, history, f'异常 {e}' return decorated def report_execption(chatbot, history, a, b): """ 向chatbot中添加错误信息 """ chatbot.append((a, b)) history.append(a); history.append(b) def text_divide_paragraph(text): """ 将文本按照段落分隔符分割开,生成带有段落标签的HTML代码。 """ if '```' in text: # careful input return text else: # wtf input lines = text.split("\n") for i, line in enumerate(lines): if i!=0: lines[i] = "

"+lines[i].replace(" ", " ")+"

" text = "".join(lines) return text def markdown_convertion(txt): """ 将Markdown格式的文本转换为HTML格式。如果包含数学公式,则先将公式转换为HTML格式。 """ if ('$' in txt) and ('```' not in txt): return markdown.markdown(txt,extensions=['fenced_code','tables']) + '

' + \ markdown.markdown(convert_math(txt, splitParagraphs=False),extensions=['fenced_code','tables']) else: return markdown.markdown(txt,extensions=['fenced_code','tables']) def format_io(self, y): """ 将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化,并将输出部分的Markdown和数学公式转换为HTML格式。 """ if y is None or y == []: return [] i_ask, gpt_reply = y[-1] i_ask = text_divide_paragraph(i_ask) # 输入部分太自由,预处理一波 y[-1] = ( None if i_ask is None else markdown.markdown(i_ask, extensions=['fenced_code','tables']), None if gpt_reply is None else markdown_convertion(gpt_reply) ) return y def find_free_port(): """ 返回当前系统中可用的未使用端口。 """ import socket from contextlib import closing with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: s.bind(('', 0)) s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) return s.getsockname()[1] def extract_archive(file_path, dest_dir): import zipfile import tarfile import os # Get the file extension of the input file file_extension = os.path.splitext(file_path)[1] # Extract the archive based on its extension if file_extension == '.zip': with zipfile.ZipFile(file_path, 'r') as zipobj: zipobj.extractall(path=dest_dir) print("Successfully extracted zip archive to {}".format(dest_dir)) elif file_extension in ['.tar', '.gz', '.bz2']: with tarfile.open(file_path, 'r:*') as tarobj: tarobj.extractall(path=dest_dir) print("Successfully extracted tar archive to {}".format(dest_dir)) else: return def find_recent_files(directory): """ me: find files that is created with in one minutes under a directory with python, write a function gpt: here it is! """ import os import time current_time = time.time() one_minute_ago = current_time - 60 recent_files = [] for filename in os.listdir(directory): file_path = os.path.join(directory, filename) if file_path.endswith('.log'): continue created_time = os.path.getctime(file_path) if created_time >= one_minute_ago: recent_files.append(file_path) return recent_files def on_file_uploaded(files, chatbot, txt): if len(files) == 0: return chatbot, txt import shutil, os, time, glob from toolbox import extract_archive try: shutil.rmtree('./private_upload/') except: pass time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) os.makedirs(f'private_upload/{time_tag}', exist_ok=True) for file in files: file_origin_name = os.path.basename(file.orig_name) shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}') extract_archive(f'private_upload/{time_tag}/{file_origin_name}', dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract') moved_files = [fp for fp in glob.glob('private_upload/**/*', recursive=True)] txt = f'private_upload/{time_tag}' moved_files_str = '\t\n\n'.join(moved_files) chatbot.append(['我上传了文件,请查收', f'[Local Message] 收到以下文件: \n\n{moved_files_str}\n\n调用路径参数已自动修正到: \n\n{txt}\n\n现在您可以直接选择任意实现性功能']) return chatbot, txt def on_report_generated(files, chatbot): from toolbox import find_recent_files report_files = find_recent_files('gpt_log') if len(report_files) == 0: return report_files, chatbot # files.extend(report_files) chatbot.append(['汇总报告如何远程获取?', '汇总报告已经添加到右侧文件上传区,请查收。']) return report_files, chatbot