import gradio as gr import sys # sys.path.append('./inference/') import bmtools from bmtools.agent.tools_controller import MTQuestionAnswerer, load_valid_tools from bmtools.agent.singletool import STQuestionAnswerer from langchain.schema import AgentFinish import os import requests from threading import Thread from multiprocessing import Process import time available_models = ["ChatGPT", "GPT-3.5"] DEFAULTMODEL = "GPT-3.5" tools_mappings = { "klarna": "https://www.klarna.com/", "chemical-prop": "http://127.0.0.1:8079/tools/chemical-prop/", "wolframalpha": "http://127.0.0.1:8079/tools/wolframalpha/", "weather": "http://127.0.0.1:8079/tools/weather/", "douban-film": "http://127.0.0.1:8079/tools/douban-film/", "wikipedia": "http://127.0.0.1:8079/tools/wikipedia/", "office-ppt": "http://127.0.0.1:8079/tools/office-ppt/", "bing_search": "http://127.0.0.1:8079/tools/bing_search/", "map": "http://127.0.0.1:8079/tools/map/", "stock": "http://127.0.0.1:8079/tools/stock/", "baidu-translation": "http://127.0.0.1:8079/tools/baidu-translation/", "nllb-translation": "http://127.0.0.1:8079/tools/nllb-translation/", } valid_tools_info = {} all_tools_list = [] gr.close_all() MAX_TURNS = 30 MAX_BOXES = MAX_TURNS * 2 return_msg = [] chat_history = "" tool_server_flag = False def run_tool_server(): def run_server(): server = bmtools.ToolServer() # server.load_tool("chemical-prop") server.load_tool("douban-film") # server.load_tool("weather") # server.load_tool("wikipedia") # server.load_tool("wolframalpha") # server.load_tool("bing_search") # server.load_tool("office-ppt") # server.load_tool("stock") # server.load_tool("map") # server.load_tool("nllb-translation") # server.load_tool("baidu-translation") # server.load_tool("tutorial") server.serve() # server = Thread(target=run_server) server = Process(target=run_server) server.start() global tool_server_flag tool_server_flag = True def load_tools(): global valid_tools_info global all_tools_list valid_tools_info = load_valid_tools(tools_mappings) all_tools_list = sorted(list(valid_tools_info.keys())) return gr.update(choices=all_tools_list) def set_environ(OPENAI_API_KEY: str, WOLFRAMALPH_APP_ID: str = "", WEATHER_API_KEYS: str = "", BING_SUBSCRIPT_KEY: str = "", ALPHA_VANTAGE_KEY: str = "", BING_MAP_KEY: str = "", BAIDU_TRANSLATE_KEY: str = "", BAIDU_SECRET_KEY: str = "") -> str: os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY os.environ["WOLFRAMALPH_APP_ID"] = WOLFRAMALPH_APP_ID os.environ["WEATHER_API_KEYS"] = WEATHER_API_KEYS os.environ["BING_SUBSCRIPT_KEY"] = BING_SUBSCRIPT_KEY os.environ["ALPHA_VANTAGE_KEY"] = ALPHA_VANTAGE_KEY os.environ["BING_MAP_KEY"] = BING_MAP_KEY os.environ["BAIDU_TRANSLATE_KEY"] = BAIDU_TRANSLATE_KEY os.environ["BAIDU_SECRET_KEY"] = BAIDU_SECRET_KEY if not tool_server_flag: run_tool_server() time.sleep(10) return gr.update(value="OK!") def show_avatar_imgs(tools_chosen): if len(tools_chosen) == 0: tools_chosen = list(valid_tools_info.keys()) img_template = ' avatar {} ' imgs = [valid_tools_info[tool]['avatar'] for tool in tools_chosen if valid_tools_info[tool]['avatar'] != None] imgs = ' '.join([img_template.format(img, img, tool ) for img, tool in zip(imgs, tools_chosen) ]) return [gr.update(value=''+imgs+'', visible=True), gr.update(visible=True)] def answer_by_tools(question, tools_chosen, model_chosen): global return_msg return_msg += [(question, None), (None, '...')] yield [gr.update(visible=True, value=return_msg), gr.update(), gr.update()] OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY', '') if len(tools_chosen) == 0: # if there is no tools chosen, we use all todo (TODO: What if the pool is too large.) tools_chosen = list(valid_tools_info.keys()) if len(tools_chosen) == 1: answerer = STQuestionAnswerer(OPENAI_API_KEY.strip(), stream_output=True, llm=model_chosen) agent_executor = answerer.load_tools(tools_chosen[0], valid_tools_info[tools_chosen[0]], prompt_type="react-with-tool-description", return_intermediate_steps=True) else: answerer = MTQuestionAnswerer(OPENAI_API_KEY.strip(), load_valid_tools({k: tools_mappings[k] for k in tools_chosen}), stream_output=True, llm=model_chosen) agent_executor = answerer.build_runner() global chat_history chat_history += "Question: " + question + "\n" question = chat_history for inter in agent_executor(question): if isinstance(inter, AgentFinish): continue result_str = [] return_msg.pop() if isinstance(inter, dict): result_str.append("Answer: {}".format(inter['output'])) chat_history += "Answer:" + inter['output'] + "\n" result_str.append("...") else: not_observation = inter[0].log if not not_observation.startswith('Thought:'): not_observation = "Thought: " + not_observation chat_history += not_observation not_observation = not_observation.replace('Thought:', 'Thought: ') not_observation = not_observation.replace('Action:', 'Action: ') not_observation = not_observation.replace('Action Input:', 'Action Input: ') result_str.append("{}".format(not_observation)) result_str.append("Action output:\n{}".format(inter[1])) chat_history += "\nAction output:" + inter[1] + "\n" result_str.append("...") return_msg += [(None, result) for result in result_str] yield [gr.update(visible=True, value=return_msg), gr.update(), gr.update()] return_msg.pop() if return_msg[-1][1].startswith("Answer: "): return_msg[-1] = (return_msg[-1][0], return_msg[-1][1].replace("Answer: ", "Final Answer: ")) yield [gr.update(visible=True, value=return_msg), gr.update(visible=True), gr.update(visible=False)] def retrieve(tools_search): if tools_search == "": return gr.update(choices=all_tools_list) else: url = "http://127.0.0.1:8079/retrieve" param = { "query": tools_search } response = requests.post(url, json=param) result = response.json() retrieved_tools = result["tools"] return gr.update(choices=retrieved_tools) def clear_history(): global return_msg global chat_history return_msg = [] chat_history = "" yield gr.update(visible=True, value=return_msg) with gr.Blocks() as demo: with gr.Row(): with gr.Column(scale=14): gr.Markdown("

BMTools

") with gr.Column(scale=1): gr.Markdown('') with gr.Row(): with gr.Column(scale=1): OPENAI_API_KEY = gr.Textbox(label="OpenAI API KEY:", placeholder="sk-...", type="text") # WOLFRAMALPH_APP_ID = gr.Textbox(label="WOLFRAMALPH APP ID:", type="text") # WEATHER_API_KEYS = gr.Textbox(label="WEATHER API KEYS:", type="text") # BING_SUBSCRIPT_KEY = gr.Textbox(label="BING SUBSCRIPT KEY:", type="text") # ALPHA_VANTAGE_KEY = gr.Textbox(label="ALPHA VANTAGE KEY:", type="text") # BING_MAP_KEY = gr.Textbox(label="BING MAP KEY:", type="text") # BAIDU_TRANSLATE_KEY = gr.Textbox(label="BAIDU TRANSLATE KEY:", type="text") # BAIDU_SECRET_KEY = gr.Textbox(label="BAIDU SECRET KEY:", type="text") key_set_btn = gr.Button(value="Set") with gr.Column(scale=4): with gr.Row(): with gr.Column(scale=0.85): txt = gr.Textbox(show_label=False, placeholder="Question here. Use Shift+Enter to add new line.", lines=1).style(container=False) with gr.Column(scale=0.15, min_width=0): buttonClear = gr.Button("Clear History") buttonStop = gr.Button("Stop", visible=False) chatbot = gr.Chatbot(show_label=False, visible=True).style(height=600) with gr.Column(scale=1): with gr.Column(): tools_search = gr.Textbox( lines=1, label="Tools Search", info="Please input some text to search tools.", ) buttonSearch = gr.Button("Clear") tools_chosen = gr.CheckboxGroup( choices=all_tools_list, value=["chemical-prop"], label="Tools provided", info="Choose the tools to solve your question.", ) model_chosen = gr.Dropdown( list(available_models), value=DEFAULTMODEL, multiselect=False, label="Model provided", info="Choose the model to solve your question, Default means ChatGPT." ) key_set_btn.click(fn=set_environ, inputs=[ OPENAI_API_KEY, # WOLFRAMALPH_APP_ID, # WEATHER_API_KEYS, # BING_SUBSCRIPT_KEY, # ALPHA_VANTAGE_KEY, # BING_MAP_KEY, # BAIDU_TRANSLATE_KEY, # BAIDU_SECRET_KEY ], outputs=key_set_btn) key_set_btn.click(fn=load_tools, outputs=tools_chosen) tools_search.change(retrieve, tools_search, tools_chosen) buttonSearch.click(lambda : [gr.update(value=""), gr.update(choices=all_tools_list)], [], [tools_search, tools_chosen]) txt.submit(lambda : [gr.update(value=''), gr.update(visible=False), gr.update(visible=True)], [], [txt, buttonClear, buttonStop]) inference_event = txt.submit(answer_by_tools, [txt, tools_chosen, model_chosen], [chatbot, buttonClear, buttonStop]) buttonStop.click(lambda : [gr.update(visible=True), gr.update(visible=False)], [], [buttonClear, buttonStop], cancels=[inference_event]) buttonClear.click(clear_history, [], chatbot) # demo.queue().launch(share=False, inbrowser=True, server_name="127.0.0.1", server_port=7001) demo.queue().launch()