from collections.abc import Callable, Iterable, Mapping from typing import Any from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc from toolbox import promote_file_to_downloadzone, get_log_folder from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive from .crazy_utils import input_clipping, try_install_deps from multiprocessing import Process, Pipe import os import time templete = """ ```python import ... # Put dependencies here, e.g. import numpy as np class TerminalFunction(object): # Do not change the name of the class, The name of the class must be `TerminalFunction` def run(self, path): # The name of the function must be `run`, it takes only a positional argument. # rewrite the function you have just written here ... return generated_file_path ``` """ def inspect_dependency(chatbot, history): yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 return True def get_code_block(reply): import re pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks matches = re.findall(pattern, reply) # find all code blocks in text if len(matches) == 1: return matches[0].strip('python') # code block for match in matches: if 'class TerminalFunction' in match: return match.strip('python') # code block raise RuntimeError("GPT is not generating proper code.") def gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history): # 输入 prompt_compose = [ f'Your job:\n' f'1. write a single Python function, which takes a path of a `{file_type}` file as the only argument and returns a `string` containing the result of analysis or the path of generated files. \n', f"2. You should write this function to perform following task: " + txt + "\n", f"3. Wrap the output python function with markdown codeblock." ] i_say = "".join(prompt_compose) demo = [] # 第一步 gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( inputs=i_say, inputs_show_user=i_say, llm_kwargs=llm_kwargs, chatbot=chatbot, history=demo, sys_prompt= r"You are a programmer." ) history.extend([i_say, gpt_say]) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新 # 第二步 prompt_compose = [ "If previous stage is successful, rewrite the function you have just written to satisfy following templete: \n", templete ] i_say = "".join(prompt_compose); inputs_show_user = "If previous stage is successful, rewrite the function you have just written to satisfy executable templete. " gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( inputs=i_say, inputs_show_user=inputs_show_user, llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, sys_prompt= r"You are a programmer." ) code_to_return = gpt_say history.extend([i_say, gpt_say]) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新 # # 第三步 # i_say = "Please list to packages to install to run the code above. Then show me how to use `try_install_deps` function to install them." # i_say += 'For instance. `try_install_deps(["opencv-python", "scipy", "numpy"])`' # installation_advance = yield from request_gpt_model_in_new_thread_with_ui_alive( # inputs=i_say, inputs_show_user=inputs_show_user, # llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, # sys_prompt= r"You are a programmer." # ) # # # 第三步 # i_say = "Show me how to use `pip` to install packages to run the code above. " # i_say += 'For instance. `pip install -r opencv-python scipy numpy`' # installation_advance = yield from request_gpt_model_in_new_thread_with_ui_alive( # inputs=i_say, inputs_show_user=i_say, # llm_kwargs=llm_kwargs, chatbot=chatbot, history=history, # sys_prompt= r"You are a programmer." # ) installation_advance = "" return code_to_return, installation_advance, txt, file_type, llm_kwargs, chatbot, history def make_module(code): module_file = 'gpt_fn_' + gen_time_str().replace('-','_') with open(f'{get_log_folder()}/{module_file}.py', 'w', encoding='utf8') as f: f.write(code) def get_class_name(class_string): import re # Use regex to extract the class name class_name = re.search(r'class (\w+)\(', class_string).group(1) return class_name class_name = get_class_name(code) return f"{get_log_folder().replace('/', '.')}.{module_file}->{class_name}" def init_module_instance(module): import importlib module_, class_ = module.split('->') init_f = getattr(importlib.import_module(module_), class_) return init_f() def for_immediate_show_off_when_possible(file_type, fp, chatbot): if file_type in ['png', 'jpg']: image_path = os.path.abspath(fp) chatbot.append(['这是一张图片, 展示如下:', f'本地文件地址:
`{image_path}`
'+ f'本地文件预览:
' ]) return chatbot def subprocess_worker(instance, file_path, return_dict): return_dict['result'] = instance.run(file_path) def have_any_recent_upload_files(chatbot): _5min = 5 * 60 if not chatbot: return False # chatbot is None most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None) if not most_recent_uploaded: return False # most_recent_uploaded is None if time.time() - most_recent_uploaded["time"] < _5min: return True # most_recent_uploaded is new else: return False # most_recent_uploaded is too old def get_recent_file_prompt_support(chatbot): most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None) path = most_recent_uploaded['path'] return path @CatchException def 虚空终端CodeInterpreter(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): """ txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径 llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行 plugin_kwargs 插件模型的参数，暂时没有用武之地 chatbot 聊天显示框的句柄，用于显示给用户 history 聊天历史，前情提要 system_prompt 给gpt的静默提醒 web_port 当前软件运行的端口号 """ raise NotImplementedError # 清空历史，以免输入溢出 history = []; clear_file_downloadzone(chatbot) # 基本信息：功能、贡献者 chatbot.append([ "函数插件功能？", "CodeInterpreter开源版, 此插件处于开发阶段, 建议暂时不要使用, 插件初始化中 ..." ]) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 if have_any_recent_upload_files(chatbot): file_path = get_recent_file_prompt_support(chatbot) else: chatbot.append(["文件检索", "没有发现任何近期上传的文件。"]) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 读取文件 if ("recently_uploaded_files" in plugin_kwargs) and (plugin_kwargs["recently_uploaded_files"] == ""): plugin_kwargs.pop("recently_uploaded_files") recently_uploaded_files = plugin_kwargs.get("recently_uploaded_files", None) file_path = recently_uploaded_files[-1] file_type = file_path.split('.')[-1] # 粗心检查 if is_the_upload_folder(txt): chatbot.append([ "...", f"请在输入框内填写需求，然后再次点击该插件（文件路径 {file_path} 已经被记忆）" ]) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 return # 开始干正事 for j in range(5): # 最多重试5次 try: code, installation_advance, txt, file_type, llm_kwargs, chatbot, history = \ yield from gpt_interact_multi_step(txt, file_type, llm_kwargs, chatbot, history) code = get_code_block(code) res = make_module(code) instance = init_module_instance(res) break except Exception as e: chatbot.append([f"第{j}次代码生成尝试，失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"]) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 代码生成结束, 开始执行 try: import multiprocessing manager = multiprocessing.Manager() return_dict = manager.dict() p = multiprocessing.Process(target=subprocess_worker, args=(instance, file_path, return_dict)) # only has 10 seconds to run p.start(); p.join(timeout=10) if p.is_alive(): p.terminate(); p.join() p.close() res = return_dict['result'] # res = instance.run(file_path) except Exception as e: chatbot.append(["执行失败了", f"错误追踪\n```\n{trimmed_format_exc()}\n```\n"]) # chatbot.append(["如果是缺乏依赖，请参考以下建议", installation_advance]) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 return # 顺利完成，收尾 res = str(res) if os.path.exists(res): chatbot.append(["执行成功了，结果是一个有效文件", "结果：" + res]) new_file_path = promote_file_to_downloadzone(res, chatbot=chatbot) chatbot = for_immediate_show_off_when_possible(file_type, new_file_path, chatbot) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新 else: chatbot.append(["执行成功了，结果是一个字符串", "结果：" + res]) yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新 """ 测试： 裁剪图像，保留下半部分 交换图像的蓝色通道和红色通道 将图像转为灰度图像 将csv文件转excel表格 """