import markdown import importlib import traceback import inspect import re from latex2mathml.converter import convert as tex2mathml from functools import wraps, lru_cache ############################### 插件输入输出接驳区 ####################################### class ChatBotWithCookies(list): def __init__(self, cookie): self._cookies = cookie def write_list(self, list): for t in list: self.append(t) def get_list(self): return [t for t in self] def get_cookies(self): return self._cookies def ArgsGeneralWrapper(f): """ 装饰器函数,用于重组输入参数,改变输入参数的顺序与结构。 """ def decorated(cookies, max_length, llm_model, txt, txt2, top_p, temperature, chatbot, history, system_prompt, *args): txt_passon = txt if txt == "" and txt2 != "": txt_passon = txt2 # 引入一个有cookie的chatbot cookies.update({ 'top_p':top_p, 'temperature':temperature, }) llm_kwargs = { 'api_key': cookies['api_key'], 'llm_model': llm_model, 'top_p':top_p, 'max_length': max_length, 'temperature':temperature, } plugin_kwargs = { # 目前还没有 } chatbot_with_cookie = ChatBotWithCookies(cookies) chatbot_with_cookie.write_list(chatbot) yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args) return decorated def update_ui(chatbot, history, msg='正常', **kwargs): # 刷新界面 """ 刷新用户界面 """ assert isinstance(chatbot, ChatBotWithCookies), "在传递chatbot的过程中不要将其丢弃。必要时,可用clear将其清空,然后用for+append循环重新赋值。" yield chatbot.get_cookies(), chatbot, history, msg 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: from check_proxy import check_proxy from toolbox import get_conf proxies, = get_conf('proxies') tb_str = '```\n' + traceback.format_exc() + '```' if chatbot is None or len(chatbot) == 0: chatbot = [["插件调度异常", "异常原因"]] 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 from update_ui(chatbot=chatbot, history=history, msg=f'异常 {e}') # 刷新界面 return decorated def HotReload(f): """ HotReload的装饰器函数,用于实现Python函数插件的热更新。 函数热更新是指在不停止程序运行的情况下,更新函数代码,从而达到实时更新功能。 在装饰器内部,使用wraps(f)来保留函数的元信息,并定义了一个名为decorated的内部函数。 内部函数通过使用importlib模块的reload函数和inspect模块的getmodule函数来重新加载并获取函数模块, 然后通过getattr函数获取函数名,并在新模块中重新加载函数。 最后,使用yield from语句返回重新加载过的函数,并在被装饰的函数上执行。 最终,装饰器函数返回内部函数。这个内部函数可以将函数的原始定义更新为最新版本,并执行函数的新版本。 """ @wraps(f) def decorated(*args, **kwargs): fn_name = f.__name__ f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name) yield from f_hot_reload(*args, **kwargs) return decorated ####################################### 其他小工具 ##################################### def get_reduce_token_percent(text): """ * 此函数未来将被弃用 """ try: # text = "maximum context length is 4097 tokens. However, your messages resulted in 4870 tokens" pattern = r"(\d+)\s+tokens\b" match = re.findall(pattern, text) EXCEED_ALLO = 500 # 稍微留一点余地,否则在回复时会因余量太少出问题 max_limit = float(match[0]) - EXCEED_ALLO current_tokens = float(match[1]) ratio = max_limit/current_tokens assert ratio > 0 and ratio < 1 return ratio, str(int(current_tokens-max_limit)) except: return 0.5, '不详' 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 import 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): try: # 这个bug没找到触发条件,暂时先这样顶一下 if type(content) != str: content = str(content) except: continue 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 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): 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。 """ pre = '
' suf = '
' markdown_extension_configs = { 'mdx_math': { 'enable_dollar_delimiter': True, 'use_gitlab_delimiters': False, }, } find_equation_pattern = r'\n', '') return content if ('$' in txt) and ('```' not in txt): # 有$标识的公式符号,且没有代码段```的标识 # convert everything to html format split = markdown.markdown(text='---') convert_stage_1 = markdown.markdown(text=txt, extensions=['mdx_math', 'fenced_code', 'tables', 'sane_lists'], extension_configs=markdown_extension_configs) convert_stage_1 = markdown_bug_hunt(convert_stage_1) # re.DOTALL: Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s). # 1. convert to easy-to-copy tex (do not render math) convert_stage_2_1, n = re.subn(find_equation_pattern, replace_math_no_render, convert_stage_1, flags=re.DOTALL) # 2. convert to rendered equation convert_stage_2_2, n = re.subn(find_equation_pattern, replace_math_render, convert_stage_1, flags=re.DOTALL) # cat them together return pre + convert_stage_2_1 + f'{split}' + convert_stage_2_2 + suf else: return pre + markdown.markdown(txt, extensions=['fenced_code', 'codehilite', 'tables', 'sane_lists']) + suf def close_up_code_segment_during_stream(gpt_reply): """ 在gpt输出代码的中途(输出了前面的```,但还没输出完后面的```),补上后面的``` Args: gpt_reply (str): GPT模型返回的回复字符串。 Returns: str: 返回一个新的字符串,将输出代码片段的“后面的```”补上。 """ if '```' not in gpt_reply: return gpt_reply if gpt_reply.endswith('```'): return gpt_reply # 排除了以上两个情况,我们 segments = gpt_reply.split('```') n_mark = len(segments) - 1 if n_mark % 2 == 1: # print('输出代码片段中!') return gpt_reply+'\n```' else: return gpt_reply 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 gpt_reply = close_up_code_segment_during_stream(gpt_reply) # 当代码输出半截的时候,试着补上后个``` 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)) # 第三方库,需要预先pip install rarfile # 此外,Windows上还需要安装winrar软件,配置其Path环境变量,如"C:\Program Files\WinRAR"才可以 elif file_extension == '.rar': try: import rarfile with rarfile.RarFile(file_path) as rf: rf.extractall(path=dest_dir) print("Successfully extracted rar archive to {}".format(dest_dir)) except: print("Rar format requires additional dependencies to install") return '\n\n需要安装pip install rarfile来解压rar文件' # 第三方库,需要预先pip install py7zr elif file_extension == '.7z': try: import py7zr with py7zr.SevenZipFile(file_path, mode='r') as f: f.extractall(path=dest_dir) print("Successfully extracted 7z archive to {}".format(dest_dir)) except: print("7z format requires additional dependencies to install") return '\n\n需要安装pip install py7zr来解压7z文件' else: return '' 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.getmtime(file_path) if created_time >= one_minute_ago: if os.path.isdir(file_path): continue recent_files.append(file_path) return recent_files def on_file_uploaded(files, chatbot, txt, txt2, checkboxes): if len(files) == 0: return chatbot, txt import shutil import os import time import 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) err_msg = '' 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}') err_msg += 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)] if "底部输入区" in checkboxes: txt = "" txt2 = f'private_upload/{time_tag}' else: txt = f'private_upload/{time_tag}' txt2 = "" 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}' + f'\n\n调用路径参数已自动修正到: \n\n{txt}' + f'\n\n现在您点击任意“红颜色”标识的函数插件时,以上文件将被作为输入参数'+err_msg]) return chatbot, txt, txt2 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 None, 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”(可能处于折叠状态),请查收。']) return report_files, chatbot def is_openai_api_key(key): API_MATCH = re.match(r"sk-[a-zA-Z0-9]{48}$", key) return bool(API_MATCH) def is_api2d_key(key): if key.startswith('fk') and len(key) == 41: return True else: return False def is_any_api_key(key): if ',' in key: keys = key.split(',') for k in keys: if is_any_api_key(k): return True return False else: return is_openai_api_key(key) or is_api2d_key(key) def what_keys(keys): avail_key_list = {'OpenAI Key':0, "API2D Key":0} key_list = keys.split(',') for k in key_list: if is_openai_api_key(k): avail_key_list['OpenAI Key'] += 1 for k in key_list: if is_api2d_key(k): avail_key_list['API2D Key'] += 1 return f"检测到: OpenAI Key {avail_key_list['OpenAI Key']} 个,API2D Key {avail_key_list['API2D Key']} 个" def select_api_key(keys, llm_model): import random avail_key_list = [] key_list = keys.split(',') if llm_model.startswith('gpt-'): for k in key_list: if is_openai_api_key(k): avail_key_list.append(k) if llm_model.startswith('api2d-'): for k in key_list: if is_api2d_key(k): avail_key_list.append(k) if len(avail_key_list) == 0: raise RuntimeError(f"您提供的api-key不满足要求,不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源。") api_key = random.choice(avail_key_list) # 随机负载均衡 return api_key @lru_cache(maxsize=128) def read_single_conf_with_lru_cache(arg): from colorful import print亮红, print亮绿, print亮蓝 try: r = getattr(importlib.import_module('config_private'), arg) except: r = getattr(importlib.import_module('config'), arg) # 在读取API_KEY时,检查一下是不是忘了改config if arg == 'API_KEY': print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和API2D的api-key。也支持同时填写多个api-key,如API_KEY=\"openai-key1,openai-key2,api2d-key3\"") print亮蓝(f"[API_KEY] 您既可以在config.py中修改api-key(s),也可以在问题输入区输入临时的api-key(s),然后回车键提交后即可生效。") if is_any_api_key(r): print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功") else: print亮红( "[API_KEY] 正确的 API_KEY 是'sk'开头的51位密钥(OpenAI),或者 'fk'开头的41位密钥,请在config文件中修改API密钥之后再运行。") if arg == 'proxies': if r is None: print亮红('[PROXY] 网络代理状态:未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议:检查USE_PROXY选项是否修改。') else: print亮绿('[PROXY] 网络代理状态:已配置。配置信息如下:', r) assert isinstance(r, dict), 'proxies格式错误,请注意proxies选项的格式,不要遗漏括号。' return r def get_conf(*args): # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到 res = [] for arg in args: r = read_single_conf_with_lru_cache(arg) res.append(r) return res def clear_line_break(txt): txt = txt.replace('\n', ' ') txt = txt.replace(' ', ' ') txt = txt.replace(' ', ' ') return txt class DummyWith(): """ 这段代码定义了一个名为DummyWith的空上下文管理器, 它的作用是……额……没用,即在代码结构不变得情况下取代其他的上下文管理器。 上下文管理器是一种Python对象,用于与with语句一起使用, 以确保一些资源在代码块执行期间得到正确的初始化和清理。 上下文管理器必须实现两个方法,分别为 __enter__()和 __exit__()。 在上下文执行开始的情况下,__enter__()方法会在代码块被执行前被调用, 而在上下文执行结束时,__exit__()方法则会被调用。 """ def __enter__(self): return self def __exit__(self, exc_type, exc_value, traceback): return def run_gradio(demo, auth, port, custom_path): import uvicorn import gradio as gr from fastapi import FastAPI app = FastAPI() if custom_path != "/": @app.get("/") def read_main(): return {"message": f"Gradio is running at: {custom_path}"} app = gr.mount_gradio_app(app, demo, path=custom_path) uvicorn.run(app, host="0.0.0.0", port=port) # , auth=auth