443 lines
14 KiB
Python
443 lines
14 KiB
Python
"""
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Streamlit 前端 - 多智能体实时聊天界面
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提供可视化的 Agent 协同工作展示和人机交互
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"""
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import streamlit as st
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import os
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import sys
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from pathlib import Path
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from datetime import datetime
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import time
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from typing import Dict, Any, List
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# 添加项目根目录到路径
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
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from config.llm_config import (
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get_llm_config,
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PM_PROMPT,
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QA_PROMPT,
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DEV_PROMPT,
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ORCH_PROMPT
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)
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# 页面配置
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st.set_page_config(
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page_title="AutoGen SDLC 多智能体系统",
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page_icon="🤖",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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# 自定义 CSS 样式
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st.markdown("""
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<style>
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.agent-message {
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padding: 1rem;
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border-radius: 0.5rem;
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margin-bottom: 1rem;
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}
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.pm-agent { background-color: #e3f2fd; border-left: 4px solid #2196f3; }
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.qa-agent { background-color: #e8f5e9; border-left: 4px solid #4caf50; }
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.dev-agent { background-color: #fff3e0; border-left: 4px solid #ff9800; }
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.orchestrator { background-color: #f3e5f5; border-left: 4px solid #9c27b0; }
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.system { background-color: #fce4ec; border-left: 4px solid #e91e63; }
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.user { background-color: #efebe9; border-left: 4px solid #795548; }
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</style>
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""", unsafe_allow_html=True)
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def init_session_state():
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"""初始化 session state"""
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if "messages" not in st.session_state:
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st.session_state.messages = []
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if "is_running" not in st.session_state:
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st.session_state.is_running = False
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if "workflow_result" not in st.session_state:
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st.session_state.workflow_result = None
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if "conversation_history" not in st.session_state:
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st.session_state.conversation_history = []
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if "current_step" not in st.session_state:
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st.session_state.current_step = 0
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def create_agents(api_key: str, base_url: str, model: str):
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"""创建所有 Agent"""
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llm_config = get_llm_config(model=model, api_key=api_key, base_url=base_url)
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pm_agent = AssistantAgent(
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name="PM_Agent",
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system_message=PM_PROMPT,
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llm_config=llm_config,
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description="资深软件产品经理",
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human_input_mode="NEVER"
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)
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qa_agent = AssistantAgent(
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name="QA_Agent",
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system_message=QA_PROMPT,
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llm_config=llm_config,
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description="资深测试工程师",
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human_input_mode="NEVER"
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)
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dev_agent = AssistantAgent(
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name="Dev_Agent",
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system_message=DEV_PROMPT,
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llm_config=llm_config,
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description="资深软件工程师",
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human_input_mode="NEVER"
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)
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orchestrator = AssistantAgent(
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name="Orchestrator",
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system_message=ORCH_PROMPT,
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llm_config=llm_config,
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description="多智能体协调器",
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human_input_mode="NEVER"
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)
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user_proxy = UserProxyAgent(
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name="User_Proxy",
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human_input_mode="NEVER", # 修复：Web 环境不支持 TERMINAL
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max_consecutive_auto_reply=0,
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code_execution_config={
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"work_dir": "workspace",
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"use_docker": False,
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},
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is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE")
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)
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return pm_agent, qa_agent, dev_agent, orchestrator, user_proxy, llm_config
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def get_agent_color(agent_name: str) -> str:
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"""根据 Agent 名称返回颜色类"""
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color_map = {
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"PM_Agent": "pm-agent",
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"QA_Agent": "qa-agent",
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"Dev_Agent": "dev-agent",
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"Orchestrator": "orchestrator",
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"User_Proxy": "user",
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"system": "system"
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}
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return color_map.get(agent_name, "system")
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def display_message(agent_name: str, message: str, timestamp: str):
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"""显示单条消息"""
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color_class = get_agent_color(agent_name)
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# 头像映射
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avatar_map = {
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"PM_Agent": "📋",
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"QA_Agent": "✅",
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"Dev_Agent": "💻",
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"Orchestrator": "🎯",
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"User_Proxy": "👤",
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"system": "⚙️"
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}
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avatar = avatar_map.get(agent_name, "🤖")
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with st.chat_message(name=agent_name.lower().replace("_", ""), avatar=avatar):
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st.markdown(f"**{avatar} {agent_name}** *({timestamp})*")
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st.markdown(f"<div class='agent-message {color_class}'>{message}</div>",
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unsafe_allow_html=True)
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def export_conversation(messages: List[Dict]) -> str:
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"""导出对话为 Markdown"""
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md_content = "# AutoGen SDLC 对话历史\n\n"
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md_content += f"**导出时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
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md_content += "---\n\n"
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for msg in messages:
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timestamp = msg.get("timestamp", "")[:19].replace('T', ' ')
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agent = msg.get("agent_name", "Unknown")
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content = msg.get("message", "")
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md_content += f"### [{timestamp}] {agent}\n\n"
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md_content += f"{content}\n\n"
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md_content += "---\n\n"
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return md_content
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def main():
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"""主应用"""
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init_session_state()
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# ===== 左侧边栏 - 配置区域 =====
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with st.sidebar:
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st.title("⚙️ 配置")
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# API 配置
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st.subheader("模型配置")
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api_key = st.text_input(
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"API Key",
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type="password",
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value=os.getenv("DASHSCOPE_API_KEY", ""),
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help="阿里云 DashScope API Key"
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)
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base_url = st.text_input(
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"Base URL",
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value="https://dashscope.aliyuncs.com/compatible-mode/v1",
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help="API Base URL"
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)
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model = st.selectbox(
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"模型选择",
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options=["qwen3.5-flash", "qwen-max", "qwen-plus", "qwen-turbo"],
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index=0,
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help="选择使用的 Qwen 模型"
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)
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# Agent 参数
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st.subheader("Agent 参数")
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max_round = st.slider(
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"最大对话轮数",
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min_value=5,
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max_value=50,
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value=15,
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step=1
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)
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temperature = st.slider(
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"温度参数",
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min_value=0.0,
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max_value=1.0,
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value=0.7,
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step=0.1
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)
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st.divider()
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# 控制按钮
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st.subheader("控制")
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col1, col2 = st.columns(2)
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with col1:
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start_btn = st.button("▶️ 启动", use_container_width=True)
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with col2:
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stop_btn = st.button("⏸️ 暂停", use_container_width=True)
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st.divider()
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# 导出选项
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st.subheader("导出")
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if st.button("📄 导出 JSON", use_container_width=True):
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if st.session_state.messages:
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import json
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json_str = json.dumps(st.session_state.messages, ensure_ascii=False, indent=2)
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st.download_button(
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label="下载 JSON",
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data=json_str,
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file_name=f"conversation_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
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mime="application/json"
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)
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if st.button("📝 导出 Markdown", use_container_width=True):
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if st.session_state.messages:
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md_content = export_conversation(st.session_state.messages)
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st.download_button(
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label="下载 Markdown",
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data=md_content,
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file_name=f"conversation_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md",
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mime="text/markdown"
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)
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# ===== 主区域 - 聊天展示 =====
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st.title("🤖 AutoGen SDLC 多智能体协同系统")
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st.markdown("**基于 AutoGen + Qwen3.5-flash 的端到端软件交付系统**")
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# 状态指示器
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status_col1, status_col2, status_col3 = st.columns(3)
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with status_col1:
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st.metric("对话轮数", len(st.session_state.messages))
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with status_col2:
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status = "🟢 运行中" if st.session_state.is_running else "🔴 已停止"
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st.metric("系统状态", status)
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with status_col3:
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result_status = "✅ 成功" if st.session_state.workflow_result and st.session_state.workflow_result.get("success") else "⏳ 未开始"
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st.metric("工作流状态", result_status)
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st.divider()
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# 聊天历史展示区
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chat_container = st.container()
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with chat_container:
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if not st.session_state.messages:
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st.info("👈 请在左侧配置后，输入需求并点击启动按钮开始工作流")
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else:
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for msg in st.session_state.messages:
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display_message(
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agent_name=msg.get("agent_name", "Unknown"),
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message=msg.get("message", ""),
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timestamp=msg.get("timestamp", "")
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)
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# ===== 右侧边栏 - 进度和日志 =====
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with st.sidebar:
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st.divider()
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# 工作流进度
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st.subheader("📊 工作流进度")
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workflow_steps = [
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"需求分析",
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"测试设计",
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"代码实现",
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"测试执行",
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"最终验证"
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]
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current_step = st.session_state.get("current_step", 0)
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for i, step in enumerate(workflow_steps):
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if i < current_step:
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st.success(f"✅ {step}")
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elif i == current_step:
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st.info(f"🔄 {step}")
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else:
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st.write(f"⚪ {step}")
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# ===== 用户输入区域 =====
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st.divider()
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# 用户需求输入
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user_input = st.chat_input("请输入您的软件需求...")
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if user_input:
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# 添加用户消息到历史
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user_msg = {
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"timestamp": datetime.now().isoformat(),
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"agent_name": "User",
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"role": "user",
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"message": user_input
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}
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st.session_state.messages.append(user_msg)
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st.session_state.conversation_history.append(user_msg)
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# 如果正在运行，添加到队列等待处理
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if st.session_state.is_running:
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# 这里会触发工作流执行
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pass
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# ===== 工作流执行逻辑 =====
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if start_btn:
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if not api_key:
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st.error("请先设置 API Key！")
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st.stop()
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# 获取最后一条用户消息
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user_messages = [m for m in st.session_state.messages if m.get("role") == "user"]
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if not user_messages:
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st.warning("请先输入需求！")
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st.stop()
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latest_requirement = user_messages[-1]["message"]
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# 设置运行状态
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st.session_state.is_running = True
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st.session_state.current_step = 0
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# 创建占位符用于实时更新
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status_placeholder = st.empty()
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message_placeholder = st.empty()
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with status_placeholder:
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st.info("🚀 正在启动 SDLC 工作流...")
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try:
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# 创建 Agent
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pm_agent, qa_agent, dev_agent, orchestrator, user_proxy, llm_config = create_agents(
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api_key=api_key,
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base_url=base_url,
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model=model
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)
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# 创建 GroupChat
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groupchat = GroupChat(
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agents=[pm_agent, qa_agent, dev_agent, orchestrator, user_proxy],
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messages=[],
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max_round=max_round,
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speaker_selection_method="round_robin"
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)
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manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)
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# 构建初始消息
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initial_message = f"""
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请启动完整的 SDLC 流程：
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【用户需求】
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{latest_requirement}
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【工作流程】
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1. PM_Agent → 生成 SRS
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2. QA_Agent → 生成测试用例
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3. Dev_Agent → 编写代码
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4. User_Proxy → 执行测试
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5. Orchestrator → 汇总报告
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开始协作！
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"""
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# 记录第一条消息
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first_msg = {
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"timestamp": datetime.now().isoformat(),
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"agent_name": "Orchestrator",
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"role": "assistant",
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"message": f"🎯 启动 SDLC 工作流，需求：{latest_requirement[:100]}..."
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}
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st.session_state.messages.append(first_msg)
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st.session_state.current_step = 0
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# 启动对话（简化版本，实际应该用异步）
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with message_placeholder:
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st.info("💬 Agent 们正在协作中，请稍候...")
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# 这里简化处理，实际应该使用异步回调
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chat_result = user_proxy.initiate_chat(
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manager,
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message=initial_message,
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max_turns=max_round
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)
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# 记录结果
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for msg in groupchat.messages:
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chat_msg = {
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"timestamp": datetime.now().isoformat(),
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"agent_name": msg.get("name", "Unknown"),
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"role": msg.get("role", "assistant"),
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"message": msg.get("content", "")
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}
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st.session_state.messages.append(chat_msg)
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# 更新状态
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st.session_state.workflow_result = {
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"success": True,
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"summary": chat_result.summary if hasattr(chat_result, 'summary') else "完成"
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}
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st.session_state.current_step = 5 # 完成所有步骤
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st.session_state.is_running = False
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# 刷新显示
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st.rerun()
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except Exception as e:
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st.session_state.is_running = False
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st.error(f"❌ 错误：{str(e)}")
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st.session_state.workflow_result = {"success": False, "error": str(e)}
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if stop_btn:
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st.session_state.is_running = False
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st.info("⏸️ 工作流已暂停")
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if __name__ == "__main__":
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main()
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