251 lines
12 KiB
Python
251 lines
12 KiB
Python
import os
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import sys
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sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
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import asyncio
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import uuid
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import streamlit as st
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from python_a2a import AgentNetwork, Message, TextContent, MessageRole, Task
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from langchain_openai import ChatOpenAI
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import json
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from datetime import datetime
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import pytz
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import re # 用于清理响应
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from create_logger import logger
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from app.prompts import SmartVoyagePrompts
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from conf import settings
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# 启动命令：streamlit run main.py
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# 设置页面配置
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st.set_page_config(page_title="基于A2A的SmartVoyage旅行助手系统", layout="wide", page_icon="🤖")
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# 自定义 CSS 打造高端大气科技感，优化对比度
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st.markdown("""
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<style>
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/* 聊天消息框样式 */
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.stChatMessage {
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background-color: #2c3e50 !important;
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border-radius: 12px !important;
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padding: 15px !important;
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margin-bottom: 15px !important;
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box-shadow: 0 3px 6px rgba(0,0,0,0.2) !important;
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}
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/* 用户消息框稍亮 */
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.stChatMessage.user {
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background-color: #34495e !important;
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}
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/* ✅ 核心：强制所有文字变为白色（包括 markdown 内部） */
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.stChatMessage .stMarkdown,
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.stChatMessage .stMarkdown p,
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.stChatMessage .stMarkdown span,
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.stChatMessage .stMarkdown div,
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.stChatMessage .stMarkdown strong,
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.stChatMessage .stMarkdown em,
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.stChatMessage .stMarkdown code {
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color: #ffffff !important;
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}
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/* 如果你想让 emoji 图标更亮一点 */
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.stChatMessage [data-testid="stChatMessageAvatarIcon"] {
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filter: brightness(1.2);
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}
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</style>
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""", unsafe_allow_html=True)
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# 初始化会话状态
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if "messages" not in st.session_state:
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st.session_state.messages = []
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if "agent_network" not in st.session_state:
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# 存储代理URL信息，便于查看
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st.session_state.agent_urls = {
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"WeatherQueryAssistant": "http://localhost:5005",
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"TicketQueryAssistant": "http://localhost:5006",
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"TicketOrderAssistant": "http://localhost:5007"
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}
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# 初始化网络
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network = AgentNetwork(name="Travel Assistant Network")
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network.add("WeatherQueryAssistant", "http://localhost:5005")
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network.add("TicketQueryAssistant", "http://localhost:5006")
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network.add("TicketOrderAssistant", "http://localhost:5007")
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st.session_state.agent_network = network
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# 加载配置并创建LLM
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st.session_state.llm = ChatOpenAI(
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model=settings.model_name,
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api_key=settings.api_key,
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base_url=settings.base_url,
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temperature=0.1
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)
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# 存储对话历史用于意图识别
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st.session_state.conversation_history = ""
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# 意图识别agent
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def intent_agent(user_input):
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# 创建意图识别链：提示模板 + LLM
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chain = SmartVoyagePrompts.intent_prompt() | st.session_state.llm
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# 调用LLM进行意图识别
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current_date = datetime.now(pytz.timezone('Asia/Shanghai')).strftime('%Y-%m-%d') # 获取当前日期（Asia/Shanghai时区）
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intent_response = chain.invoke(
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{"conversation_history": '\n'.join(st.session_state.conversation_history.split("\n")[-6:]), "query": user_input,
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"current_date": current_date}).content.strip()
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logger.info(f"意图识别原始响应: {intent_response}")
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# 清理响应：移除可能的Markdown代码块标记
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intent_response = re.sub(r'^```json\s*|\s*```$', '', intent_response).strip()
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logger.info(f"清理后响应: {intent_response}")
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intent_output = json.loads(intent_response)
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# 提取意图、改写问题和追问消息
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intents = intent_output.get("intents", [])
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user_queries = intent_output.get("user_queries", {})
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follow_up_message = intent_output.get("follow_up_message", "")
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logger.info(f"intents: {intents}||user_queries: {user_queries}||follow_up_message: {follow_up_message} ")
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return intents, user_queries, follow_up_message
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# 主界面布局
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st.title("🤖 基于A2A的SmartVoyage旅行智能助手")
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st.markdown("欢迎体验智能对话！输入问题，系统将精准识别意图并提供服务。")
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# 两栏布局：左侧对话，右侧 Agent Card
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col1, col2 = st.columns([2, 1])
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# 左侧对话区域
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with col1:
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st.subheader("💬 对话")
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# 对话历史
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# 输入框
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if prompt := st.chat_input("请输入您的问题..."):
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# 显示用户消息
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with st.chat_message("user"):
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st.markdown(prompt)
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st.session_state.messages.append({"role": "user", "content": prompt})
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st.session_state.conversation_history += f"\nUser: {prompt}"
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# 获取 LLM 和当前日期
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llm = st.session_state.llm
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current_date = datetime.now(pytz.timezone('Asia/Shanghai')).strftime('%Y-%m-%d')
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# 意图识别
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with st.spinner("正在分析您的意图..."):
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try:
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# 意图识别过程
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intents, user_queries, follow_up_message = intent_agent(prompt)
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# 根据意图输出生成响应
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if "out_of_scope" in intents:
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# 如果意图超出范围，返回大模型直接回复
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response = follow_up_message
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st.session_state.conversation_history += f"\nAssistant: {response}"
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elif follow_up_message != "":
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# 如果有追问消息，则直接返回
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response = follow_up_message
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st.session_state.conversation_history += f"\nAssistant: {response}" # 更新历史
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else: # 处理有效意图
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responses = [] # 存储每个意图的响应列表
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routed_agents = [] # 记录路由到的代理列表
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for intent in intents:
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logger.info(f"处理意图：{intent}")
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# 根据意图确定代理名称
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if intent == "weather":
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agent_name = "WeatherQueryAssistant"
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elif intent in ["flight", "train", "concert"]:
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agent_name = "TicketQueryAssistant"
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elif intent == "order":
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agent_name = "TicketOrderAssistant"
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else:
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agent_name = None
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# 不同意图处理方式
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if intent == "attraction":
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# 对于景点推荐，直接使用LLM生成
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chain = SmartVoyagePrompts.attraction_prompt() | llm
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rec_response = chain.invoke({"query": prompt}).content.strip()
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responses.append(rec_response)
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elif agent_name:
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# 对于代理意图，则调用代理
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# 1）获取问题
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query_str = user_queries.get(intent, {})
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logger.info(f"{agent_name} 查询：{query_str}")
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# 2）获取代理实例
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agent = st.session_state.agent_network.get_agent(agent_name)
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# 3）构建历史对话信息+新查询，然后调用代理
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chat_history = '\n'.join(st.session_state.conversation_history.split("\n")[-7:-1]) + f'\nUser: {query_str}'
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message = Message(content=TextContent(text=chat_history), role=MessageRole.USER)
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task = Task(id="task-" + str(uuid.uuid4()), message=message.to_dict())
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raw_response = asyncio.run(agent.send_task_async(task))
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logger.info(f"{agent_name} 原始响应: {raw_response}") # 记录原始响应日志
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# 4）处理结果
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if raw_response.status.state == 'completed': # 正常结果
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agent_result = raw_response.artifacts[0]['parts'][0]['text']
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else: # 异常结果
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agent_result = raw_response.status.message['content']['text']
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# 根据代理类型总结响应
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if agent_name == "WeatherQueryAssistant":
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chain = SmartVoyagePrompts.summarize_weather_prompt() | llm
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final_response = chain.invoke(
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{"query": query_str, "raw_response": agent_result}).content.strip()
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elif agent_name == "TicketQueryAssistant":
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chain = SmartVoyagePrompts.summarize_ticket_prompt() | llm
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final_response = chain.invoke(
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{"query": query_str, "raw_response": agent_result}).content.strip()
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else:
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final_response = agent_result
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# 5）添加到历史
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responses.append(final_response) # 添加到响应列表
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routed_agents.append(agent_name) # 记录路由代理
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else:
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# 不支持的意图
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responses.append("暂不支持此意图。")
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response = "\n\n".join(responses)
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if routed_agents:
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logger.info(f"路由到代理：{routed_agents}")
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st.session_state.conversation_history += f"\nAssistant: {response}"
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# 显示助手消息
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with st.chat_message("assistant"):
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st.markdown(response)
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st.session_state.messages.append({"role": "assistant", "content": response})
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except json.JSONDecodeError as json_err:
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logger.error(f"意图识别JSON解析失败")
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error_message = f"意图识别JSON解析失败：{str(json_err)}。请重试。"
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with st.chat_message("assistant"):
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st.markdown(error_message)
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st.session_state.messages.append({"role": "assistant", "content": error_message})
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except Exception as e:
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logger.error(f"处理异常: {str(e)}")
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error_message = f"处理失败：{str(e)}。请重试。"
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with st.chat_message("assistant"):
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st.markdown(error_message)
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st.session_state.messages.append({"role": "assistant", "content": error_message})
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# 右侧 Agent Card 区域
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with col2:
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st.subheader("🛠️ AgentCard")
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for agent_name in st.session_state.agent_network.agents.keys():
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agent_card = st.session_state.agent_network.get_agent_card(agent_name)
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agent_url = st.session_state.agent_urls.get(agent_name, "未知地址")
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with st.expander(f"Agent: {agent_name}", expanded=False):
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st.markdown(f"<div class='card-title'>技能</div>", unsafe_allow_html=True)
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st.markdown(f"<div class='card-content'>{agent_card.skills}</div>", unsafe_allow_html=True)
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st.markdown(f"<div class='card-title'>描述</div>", unsafe_allow_html=True)
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st.markdown(f"<div class='card-content'>{agent_card.description}</div>", unsafe_allow_html=True)
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st.markdown(f"<div class='card-title'>地址</div>", unsafe_allow_html=True)
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st.markdown(f"<div class='card-content'>{agent_url}</div>", unsafe_allow_html=True)
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st.markdown(f"<div class='card-title'>状态</div>", unsafe_allow_html=True)
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st.markdown(f"<div class='card-content'>在线</div>", unsafe_allow_html=True)
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# 页脚
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st.markdown("---")
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st.markdown('<div class="footer">Powered by 黑马程序员 | 基于Agent2Agent的旅行助手系统 v2.0</div>', unsafe_allow_html=True)
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