143 lines
6.8 KiB
Python
143 lines
6.8 KiB
Python
# 本源代码中, ⭐ = 关键步骤
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"""
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测试：
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- 裁剪图像，保留下半部分
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- 交换图像的蓝色通道和红色通道
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- 将图像转为灰度图像
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- 将csv文件转excel表格
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Testing:
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- Crop the image, keeping the bottom half.
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- Swap the blue channel and red channel of the image.
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- Convert the image to grayscale.
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- Convert the CSV file to an Excel spreadsheet.
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"""
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from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
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from toolbox import report_execption, get_log_folder, update_ui_lastest_msg, Singleton
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from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
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from crazy_functions.crazy_utils import input_clipping, try_install_deps
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from crazy_functions.agent_fns.persistent import GradioMultiuserManagerForPersistentClasses
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from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
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from crazy_functions.agent_fns.echo_agent import EchoDemo
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import time
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class AutoGenWorker(PluginMultiprocessManager):
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def gpt_academic_print_override(self, user_proxy, message, sender):
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self.child_conn.send(PipeCom("show", sender.name + '\n\n---\n\n' + message['content']))
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def gpt_academic_get_human_input(self, user_proxy, message):
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# ⭐⭐ 子进程
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patience = 300
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begin_waiting_time = time.time()
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self.child_conn.send(PipeCom("interact", message))
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while True:
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time.sleep(0.5)
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if self.child_conn.poll():
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wait_success = True
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break
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if time.time() - begin_waiting_time > patience:
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self.child_conn.send(PipeCom("done", ""))
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wait_success = False
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break
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if wait_success:
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return self.child_conn.recv().content
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else:
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raise TimeoutError("等待用户输入超时")
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def do_audogen(self, input):
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# ⭐⭐ 子进程
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input = input.content
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with ProxyNetworkActivate("AutoGen"):
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from autogen import AssistantAgent, UserProxyAgent
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config_list = [{
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'model': 'gpt-3.5-turbo-16k',
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'api_key': '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',
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},]
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autogen_work_dir = get_log_folder('autogen')
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code_execution_config={"work_dir": autogen_work_dir, "use_docker":True}
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# create an AssistantAgent instance named "assistant"
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assistant = AssistantAgent(
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name="assistant",
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llm_config={
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"config_list": config_list,
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}
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)
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# create a UserProxyAgent instance named "user_proxy"
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user_proxy = UserProxyAgent(
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name="user_proxy",
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human_input_mode="ALWAYS",
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is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
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)
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# assistant = AssistantAgent("assistant", llm_config={"config_list": config_list}, code_execution_config=code_execution_config)
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# user_proxy = UserProxyAgent("user_proxy", code_execution_config=code_execution_config)
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user_proxy._print_received_message = lambda a,b: self.gpt_academic_print_override(user_proxy, a, b)
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assistant._print_received_message = lambda a,b: self.gpt_academic_print_override(user_proxy, a, b)
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user_proxy.get_human_input = lambda a: self.gpt_academic_get_human_input(user_proxy, a)
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# user_proxy.initiate_chat(assistant, message=input)
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try:
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user_proxy.initiate_chat(assistant, message=input)
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except Exception as e:
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tb_str = '```\n' + trimmed_format_exc() + '```'
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self.child_conn.send(PipeCom("done", "AutoGen 执行失败: \n\n" + tb_str))
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def subprocess_worker(self, child_conn):
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# ⭐⭐ 子进程
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self.child_conn = child_conn
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while True:
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msg = self.child_conn.recv() # PipeCom
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self.do_audogen(msg)
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@CatchException
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def 多智能体终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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"""
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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
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llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
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plugin_kwargs 插件模型的参数
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chatbot 聊天显示框的句柄，用于显示给用户
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history 聊天历史，前情提要
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system_prompt 给gpt的静默提醒
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web_port 当前软件运行的端口号
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"""
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# 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 autogen
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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 pyautogen```。")
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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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chatbot.get_cookies()['lock_plugin'] = None
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persistent_class_multi_user_manager = GradioMultiuserManagerForPersistentClasses()
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user_uuid = chatbot.get_cookies().get('uuid')
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persistent_key = f"{user_uuid}->多智能体终端"
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if persistent_class_multi_user_manager.already_alive(persistent_key):
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# 当已经存在一个正在运行的多智能体终端时，直接将用户输入传递给它，而不是再次启动一个新的多智能体终端
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print('[debug] feed new user input')
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executor = persistent_class_multi_user_manager.get(persistent_key)
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exit_reason = yield from executor.main_process_ui_control(txt, create_or_resume="resume")
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else:
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# 运行多智能体终端 (首次)
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print('[debug] create new executor instance')
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history = []
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chatbot.append(["正在启动: 多智能体终端", "插件动态生成, 执行开始, 作者 Microsoft & Binary-Husky."])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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executor = AutoGenWorker(llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)
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persistent_class_multi_user_manager.set(persistent_key, executor)
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exit_reason = yield from executor.main_process_ui_control(txt, create_or_resume="create")
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if exit_reason == "wait_feedback":
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# 当用户点击了“等待反馈”按钮时，将executor存储到cookie中，等待用户的再次调用
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executor.chatbot.get_cookies()['lock_plugin'] = 'crazy_functions.多智能体->多智能体终端'
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else:
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executor.chatbot.get_cookies()['lock_plugin'] = None
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yield from update_ui(chatbot=executor.chatbot, history=executor.history) # 更新状态
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