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| 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() |