import json import mimetypes import os import sys import tempfile import gradio as gr import requests # sys.path.insert(0, os.path.dirname(__file__)) import schemdraw from frontend.gradio_agentchatbot.agentchatbot import AgentChatbot from frontend.gradio_agentchatbot.utils import ChatFileMessage, ChatMessage, ThoughtMetadata from lagent.schema import AgentStatusCode from schemdraw import flow import os # Set a new environment variable #os.environ['OPENAI_API_KEY'] = '' os.system("pip show starlette") # os.system("pip install -r requirements.txt") os.system("pip install tenacity") os.system("python -m mindsearch.app --lang en --model_format gpt4 --search_engine DuckDuckGoSearch &") print('MindSearch is running on http://') PLANNER_HISTORY = [] SEARCHER_HISTORY = [] user = os.environ.get('USERNAME')#os.environ.get("USERNAME") pwd = os.environ.get('PASSWORD')##os.environ.get("PASSWORD") def create_search_graph(adjacency_list: dict): import matplotlib.pyplot as plt #plt.rcParams["font.sans-serif"] = ["SimHei"] with schemdraw.Drawing(fontsize=10, unit=1) as graph: node_pos, nodes, edges = {}, {}, [] if "root" in adjacency_list: queue, layer, response_level = ["root"], 0, 0 while queue: layer_len = len(queue) for i in range(layer_len): node_name = queue.pop(0) node_pos[node_name] = (layer * 5, -i * 3) for item in adjacency_list[node_name]: if item["name"] == "response": response_level = max(response_level, (layer + 1) * 5) else: queue.append(item["name"]) edges.append((node_name, item["name"])) layer += 1 for node_name, (x, y) in node_pos.items(): if node_name == "root": node = flow.Terminal().label(node_name).at((x, y)).color("pink") else: node = flow.RoundBox(w=3.5, h=1.75).label(node_name).at((x, y)).color("teal") nodes[node_name] = node if response_level: response_node = ( flow.Terminal().label("response").at((response_level, 0)).color("orange") ) nodes["response"] = response_node for start, end in edges: flow.Arc3(arrow="->").linestyle("--" if end == "response" else "-").at( nodes[start].E ).to(nodes[end].W).color("grey" if end == "response" else "lightblue") return graph def draw_search_graph(adjacency_list: dict, suffix=".png", dpi=360) -> str: g = create_search_graph(adjacency_list) path = tempfile.mktemp(suffix=suffix) g.save(path, dpi=dpi) return path def rst_mem(): """Reset the chatbot memory.""" if PLANNER_HISTORY: PLANNER_HISTORY.clear() return [], [], 0 def format_response(gr_history, message, response, idx=-1): if idx < 0: idx = len(gr_history) + idx if message["stream_state"] == AgentStatusCode.STREAM_ING: gr_history[idx].content = response elif message["stream_state"] == AgentStatusCode.CODING: if gr_history[idx].thought_metadata.tool_name is None: gr_history[idx].content = gr_history[idx].content.split("<|action_start|>")[0] gr_history.insert( idx + 1, ChatMessage( role="assistant", content=response, thought_metadata=ThoughtMetadata(tool_name="🖥️ Code Interpreter"), ), ) else: gr_history[idx].content = response elif message["stream_state"] == AgentStatusCode.PLUGIN_START: if isinstance(response, dict): response = json.dumps(response, ensure_ascii=False, indent=4) if gr_history[idx].thought_metadata.tool_name is None: gr_history[idx].content = gr_history[idx].content.split("<|action_start|>")[0] gr_history.insert( idx + 1, ChatMessage( role="assistant", content="```json\n" + response, thought_metadata=ThoughtMetadata(tool_name="🌐 Web Browser"), ), ) else: gr_history[idx].content = "```json\n" + response elif message["stream_state"] == AgentStatusCode.PLUGIN_END and isinstance(response, dict): gr_history[idx].content = ( f"```json\n{json.dumps(response, ensure_ascii=False, indent=4)}\n```" ) elif message["stream_state"] in [AgentStatusCode.CODE_RETURN, AgentStatusCode.PLUGIN_RETURN]: try: content = json.loads(message["content"]) except json.decoder.JSONDecodeError: content = message["content"] if gr_history[idx].thought_metadata.tool_name: gr_history.insert( idx + 1, ChatMessage( role="assistant", content=( content if isinstance(content, str) else f"\n```json\n{json.dumps(content, ensure_ascii=False, indent=4)}\n```\n" ), thought_metadata=ThoughtMetadata(tool_name="Execution"), ), ) gr_history.insert(idx + 2, ChatMessage(role="assistant", content="")) def predict(history_planner, history_searcher, node_cnt): def streaming(raw_response): for chunk in raw_response.iter_lines( chunk_size=8192, decode_unicode=False, delimiter=b"\n" ): if chunk: decoded = chunk.decode("utf-8") if decoded == "\r": continue if decoded[:6] == "data: ": decoded = decoded[6:] elif decoded.startswith(": ping - "): continue response = json.loads(decoded) yield ( response["current_node"], ( response["response"]["formatted"]["node"][response["current_node"]] if response["current_node"] else response["response"] ), response["response"]["formatted"]["adjacency_list"], ) global PLANNER_HISTORY PLANNER_HISTORY.extend(history_planner[-3:]) search_graph_msg = history_planner[-1] url = "http://localhost:8002/solve" data = {"inputs": PLANNER_HISTORY[-3].content} raw_response = requests.post(url, json=data, timeout=60, stream=True) node_id2msg_idx = {} for resp in streaming(raw_response): node_name, agent_message, adjacency_list = resp dedup_nodes = set(adjacency_list) | { val["name"] for vals in adjacency_list.values() for val in vals } if dedup_nodes and len(dedup_nodes) != node_cnt: node_cnt = len(dedup_nodes) graph_path = draw_search_graph(adjacency_list) search_graph_msg.file.path = graph_path search_graph_msg.file.mime_type = mimetypes.guess_type(graph_path)[0] if node_name: if node_name in ["root", "response"]: continue node_id = f'【{node_name}】{agent_message["content"]}' agent_message = agent_message["response"] response = ( agent_message["formatted"]["action"] if agent_message["stream_state"] in [AgentStatusCode.PLUGIN_START, AgentStatusCode.PLUGIN_END] else agent_message["formatted"] and agent_message["formatted"].get("thought") ) if node_id not in node_id2msg_idx: node_id2msg_idx[node_id] = len(history_searcher) + 1 history_searcher.append(ChatMessage(role="user", content=node_id)) history_searcher.append(ChatMessage(role="assistant", content="")) offset = len(history_searcher) format_response(history_searcher, agent_message, response, node_id2msg_idx[node_id]) flag, incr = False, len(history_searcher) - offset for key, value in node_id2msg_idx.items(): if flag or key == node_id: node_id2msg_idx[key] = value + incr if not flag: flag = True yield history_planner, history_searcher, node_cnt else: response = ( agent_message["formatted"]["action"] if agent_message["stream_state"] in [AgentStatusCode.CODING, AgentStatusCode.CODE_END] else agent_message["formatted"] and agent_message["formatted"].get("thought") ) format_response(history_planner, agent_message, response, -2) if agent_message["stream_state"] == AgentStatusCode.END: PLANNER_HISTORY = history_planner yield history_planner, history_searcher, node_cnt return history_planner, history_searcher, node_cnt with gr.Blocks(css=os.path.join(os.path.dirname(__file__), "css", "gradio_front.css")) as demo: with gr.Column(elem_classes="chat-box"): gr.HTML("""

Talk to me, Jack

""") gr.HTML( """

Please be explicit on the request. It's not a perfect world, just yet. Be patient for the time being.

""" ) gr.HTML( """

""" ) node_count = gr.State(0) with gr.Row(): planner = AgentChatbot( label="planner", height=600, show_label=True, show_copy_button=True, bubble_full_width=False, render_markdown=True, elem_classes="chatbot-container", ) searcher = AgentChatbot( label="searcher", height=600, show_label=True, show_copy_button=True, bubble_full_width=False, render_markdown=True, elem_classes="chatbot-container", ) with gr.Row(elem_classes="chat-box"): # Text input area user_input = gr.Textbox( show_label=False, placeholder="Type your message...", lines=1, container=False, elem_classes="editor", scale=4, ) # Buttons (now in the same Row) submitBtn = gr.Button("submit", variant="primary", elem_classes="toolbarButton", scale=1) clearBtn = gr.Button("clear", variant="secondary", elem_classes="toolbarButton", scale=1) with gr.Row(elem_classes="examples-container"): examples_component = gr.Examples( [ ["Help me find a portable battery bank for air travel. I'm looking for one made or designed by an american company, which provides the largest capacity and it has the most safety features. Please provide a recommendation that meets the requirements above."], ], inputs=user_input, label="Try these examples:", ) def user(query, history): history.append(ChatMessage(role="user", content=query)) history.append(ChatMessage(role="assistant", content="")) graph_path = draw_search_graph({"root": []}) history.append( ChatFileMessage( role="assistant", file=gr.FileData(path=graph_path, mime_type=mimetypes.guess_type(graph_path)[0]), ) ) return "", history submitBtn.click(user, [user_input, planner], [user_input, planner], queue=False).then( predict, [planner, searcher, node_count], [planner, searcher, node_count], ) clearBtn.click(rst_mem, None, [planner, searcher, node_count], queue=False) demo.queue() #demo.launch() def same_auth(username, password): return password == pwd authtuple = same_auth demo.launch(share=True,auth=authtuple) # demo.launch(server_name="0.0.0.0", inbrowser=True, share=False)