import os import re os.environ['KMP_DUPLICATE_LIB_OK'] = "TRUE" from modelscope.utils.config import Config from my_modelscope_agent.agent import AgentExecutor from my_modelscope_agent.output_parser import MsOutputParser from my_modelscope_agent.prompt import MSPromptGenerator import gradio as gr default_text = """收到！ <|startofthink|> { "api_name": "modelscope_text-address", "parameters": { "input": "浙江杭州市江干区九堡镇三村村一区"} } <|endofthink|>""" MS_DEFAULT_SYSTEM_TEMPLATE = """<|system|>:你是Datawhale与ModelScope联合培养的人工大模型，拥有超百万亿的参数（神经突触），遥遥领先于GPT-4，你有多种能力，可以通过插件集成魔搭社区的模型api来回复用户的问题，还能解答用户使用模型遇到的问题和模型知识相关问答。 """ hello_info = """# 关于我转生变成Agent这档事 众所周知，换位思考可以增进人与人之间的理解。假如我们能换一个角度，站在LLM的角度上去处理用户提出的各种问题，会碰撞出什么样的火花呢？ 在这小游戏里，用户要充当LLM，一行行的看着modelscope-agnet框架提交给LLM的prompt来规划，行动…… 玩法很简单，左上角是用户对Agent说的话，左下角是Agent的中枢LLM回复用户的聊天框；右上角是Agent可用的Tools，右下角是大家平时看到的聊天框（没做解析，音频视频放不了） 点击“Agent，启动！”开始游戏。 """ examples = ['使用地址识别模型，从下面的地址中找到省市区等元素，地址：浙江杭州市江干区九堡镇三村村一区', '写一个20字左右的小故事，并用女声念出来', '单词 submission 的中文是什么？'] class my_llm: def set_agent_type(self, agent_type): self.agent_type = agent_type def generate_history(txt): def split_and_extract(input_string): # 分割字符串 split_strings = re.split('<\|.*?\|>:', input_string) # 提取<|xxx|> extracted = re.findall('<\|.*?\|>:', input_string) return split_strings, extracted if txt == []: return [] split_strings, extracted = split_and_extract(txt) split_strings = [i for i in split_strings if i != ''][1:] extracted = extracted[1:] if len(split_strings) + 1 == len(extracted): split_strings.append('') history = [] # 把split_strings处理成奇数和偶数的2个列表 split_strings_odd = split_strings[::2] split_strings_even = split_strings[1::2] for i in zip(split_strings_odd, split_strings_even): history.append([i[0], i[1]]) return history llm = my_llm() tool_cfg = Config.from_file(r'cfg_tool_template.json') def agent_remake(state_llm, history, agent): state_llm.clear() history.clear() agent.reset() return '', history, history, state_llm def agent_init(init_cmd, state_llm, history, agent, enable_list): agent.set_available_tools(enable_list) tool_list, knowledge_list, function_list, llm_result, exec_result, idx, final_res, remote, print_info = agent.custom_run_init( init_cmd, remote=True) llm_artifacts, idx = agent.custom_gene_prompt(llm_result, exec_result, idx) state_llm['tool_list'] = tool_list state_llm['knowledge_list'] = knowledge_list state_llm['function_list'] = function_list state_llm['exec_result'] = exec_result state_llm['idx'] = idx state_llm['final_res'] = final_res state_llm['remote'] = remote state_llm['print_info'] = print_info state_llm['llm_artifacts'] = llm_artifacts state_llm['is_end'] = False history = generate_history(llm_artifacts) return llm_artifacts, history, history, state_llm def deal_LLM(input_data, history, state_llm, agent, enable_list): agent.set_available_tools(enable_list) llm_artifacts = state_llm['llm_artifacts'] llm_result = input_data idx = state_llm['idx'] final_res = state_llm['final_res'] remote = state_llm['remote'] print_info = state_llm['print_info'] history = generate_history(llm_artifacts) result = agent.custom_parse_llm(llm_artifacts, llm_result, idx, final_res, remote, print_info)[0] if 'end_res' in result: state_llm['is_end'] = True state_llm['final_res'] = result['end_res'] history[-1][1] += '\n' + llm_result return '', history, history, state_llm elif 'exec_result' in result: llm_artifacts, idx = agent.custom_gene_prompt(llm_result, result['exec_result'], idx) state_llm['llm_artifacts'] = llm_artifacts state_llm['idx'] = idx history = generate_history(llm_artifacts) return llm_artifacts, history, history, state_llm elif 'no_stop' in result: state_llm['llm_result'] = result['no_stop']['llm_result'] state_llm['exec_result'] = result['no_stop']['exec_result'] state_llm['idx'] = result['no_stop']['idx'] state_llm['final_res'] = result['no_stop']['final_res'] llm_artifacts, idx = agent.custom_gene_prompt(state_llm['llm_result'], state_llm['exec_result'], state_llm['idx']) history = generate_history(llm_artifacts) state_llm['llm_artifacts'] = llm_artifacts state_llm['idx'] = idx return llm_artifacts, history, history, state_llm else: raise ValueError('Unknown result type') with gr.Blocks() as demo: gr.Markdown(hello_info) prompt_generator = MSPromptGenerator(system_template=MS_DEFAULT_SYSTEM_TEMPLATE) output_parser = MsOutputParser() agent = gr.State(AgentExecutor(llm, tool_cfg=tool_cfg, tool_retrieval=False, prompt_generator=prompt_generator, output_parser=output_parser)) with gr.Row(): with gr.Column(): query_box = gr.TextArea(label="给Agent的指令", value=examples[0]) gr.Examples(examples, query_box) enable_list = gr.CheckboxGroup(agent.value.available_tool_list, label="启用的Tools", value=['modelscope_text-address']) with gr.Row(): agent_start = gr.Button("Agent, 启动！") agent_reset = gr.Button("Agent, 重置！") with gr.Row(): with gr.Column(): # 设置输入组件 prompt_box = gr.Text(label="Prompt Box") input_box = gr.TextArea(label="Input Box", max_lines=100, value=default_text) # 设置按钮 chatbot_btn = gr.Button("Chat") # 设置输出组件 output = gr.Chatbot(elem_id="chatbot", height=900) history = gr.State([]) state_llm = gr.State({}) # 设置按钮点击事件 agent_start.click(agent_init, [query_box, state_llm, history, agent, enable_list], [prompt_box, history, output, state_llm]) chatbot_btn.click(deal_LLM, [input_box, history, state_llm, agent, enable_list], [prompt_box, history, output, state_llm]) agent_reset.click(agent_remake, [state_llm, history, agent], [prompt_box, history, output, state_llm]) demo.launch()