第一次提交
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442
frontend/streamlit_app.py
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442
frontend/streamlit_app.py
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"""
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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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725
frontend/streamlit_app_v2.py
Normal file
725
frontend/streamlit_app_v2.py
Normal file
@@ -0,0 +1,725 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Streamlit 前端 v2 - 增强版多智能体实时协作可视化界面
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功能：
|
||||
1. Agent 协作流程图（桑基图）
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2. 实时状态监控面板
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3. 分屏对话展示
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4. 生成的文件预览
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5. 时间线视图
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"""
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||||
import streamlit as st
|
||||
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
|
||||
import json
|
||||
|
||||
# 添加项目根目录到路径
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
try:
|
||||
from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
|
||||
AUTOGEN_AVAILABLE = True
|
||||
except ImportError:
|
||||
AUTOGEN_AVAILABLE = False
|
||||
|
||||
from config.llm_config import (
|
||||
get_llm_config,
|
||||
PM_PROMPT,
|
||||
QA_PROMPT,
|
||||
DEV_PROMPT,
|
||||
ORCH_PROMPT
|
||||
)
|
||||
|
||||
|
||||
# 页面配置
|
||||
st.set_page_config(
|
||||
page_title="AutoGen SDLC 多智能体协作平台",
|
||||
page_icon="🤖",
|
||||
layout="wide",
|
||||
initial_sidebar_state="expanded"
|
||||
)
|
||||
|
||||
# 自定义 CSS 样式
|
||||
st.markdown("""
|
||||
<style>
|
||||
/* Agent 消息样式 */
|
||||
.agent-message {
|
||||
padding: 1rem;
|
||||
border-radius: 0.5rem;
|
||||
margin-bottom: 1rem;
|
||||
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
|
||||
}
|
||||
.pm-agent { background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%); border-left: 5px solid #2196f3; }
|
||||
.qa-agent { background: linear-gradient(135deg, #e8f5e9 0%, #c8e6c9 100%); border-left: 5px solid #4caf50; }
|
||||
.dev-agent { background: linear-gradient(135deg, #fff3e0 0%, #ffe0b2 100%); border-left: 5px solid #ff9800; }
|
||||
.orchestrator { background: linear-gradient(135deg, #f3e5f5 0%, #e1bee7 100%); border-left: 5px solid #9c27b0; }
|
||||
.system { background: linear-gradient(135deg, #fce4ec 0%, #f8bbd9 100%); border-left: 5px solid #e91e63; }
|
||||
.user { background: linear-gradient(135deg, #efebe9 0%, #d7ccc8 100%); border-left: 5px solid #795548; }
|
||||
|
||||
/* 状态卡片 */
|
||||
.status-card {
|
||||
padding: 1.5rem;
|
||||
border-radius: 0.5rem;
|
||||
text-align: center;
|
||||
color: white;
|
||||
font-weight: bold;
|
||||
}
|
||||
.status-running { background: linear-gradient(135deg, #4caf50, #8bc34a); }
|
||||
.status-stopped { background: linear-gradient(135deg, #f44336, #e91e63); }
|
||||
.status-pending { background: linear-gradient(135deg, #ff9800, #ffc107); }
|
||||
|
||||
/* Agent 状态指示器 */
|
||||
.agent-status-indicator {
|
||||
display: inline-block;
|
||||
width: 12px;
|
||||
height: 12px;
|
||||
border-radius: 50%;
|
||||
margin-right: 8px;
|
||||
animation: pulse 2s infinite;
|
||||
}
|
||||
@keyframes pulse {
|
||||
0% { opacity: 1; }
|
||||
50% { opacity: 0.5; }
|
||||
100% { opacity: 1; }
|
||||
}
|
||||
|
||||
/* 文件卡片 */
|
||||
.file-card {
|
||||
padding: 1rem;
|
||||
border: 1px solid #ddd;
|
||||
border-radius: 0.5rem;
|
||||
margin-bottom: 0.5rem;
|
||||
background: #f9f9f9;
|
||||
}
|
||||
|
||||
/* 时间线 */
|
||||
.timeline-item {
|
||||
padding: 1rem;
|
||||
border-left: 3px solid #2196f3;
|
||||
margin-left: 1rem;
|
||||
position: relative;
|
||||
}
|
||||
.timeline-item::before {
|
||||
content: '';
|
||||
position: absolute;
|
||||
left: -8px;
|
||||
top: 1rem;
|
||||
width: 13px;
|
||||
height: 13px;
|
||||
border-radius: 50%;
|
||||
background: #2196f3;
|
||||
}
|
||||
</style>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
|
||||
# Agent 配置信息
|
||||
AGENT_CONFIG = {
|
||||
"PM_Agent": {
|
||||
"name": "产品经理",
|
||||
"avatar": "📋",
|
||||
"color": "#2196f3",
|
||||
"description": "需求分析与 SRS 生成",
|
||||
"icon": "📊"
|
||||
},
|
||||
"QA_Agent": {
|
||||
"name": "测试工程师",
|
||||
"avatar": "✅",
|
||||
"color": "#4caf50",
|
||||
"description": "测试用例设计与 TDD",
|
||||
"icon": "🧪"
|
||||
},
|
||||
"Dev_Agent": {
|
||||
"name": "开发工程师",
|
||||
"avatar": "💻",
|
||||
"color": "#ff9800",
|
||||
"description": "代码实现与修复",
|
||||
"icon": "⚙️"
|
||||
},
|
||||
"Orchestrator": {
|
||||
"name": "协调器",
|
||||
"avatar": "🎯",
|
||||
"color": "#9c27b0",
|
||||
"description": "流程调度与验证",
|
||||
"icon": "🎭"
|
||||
},
|
||||
"User_Proxy": {
|
||||
"name": "用户代理",
|
||||
"avatar": "👤",
|
||||
"color": "#795548",
|
||||
"description": "人机交互接口",
|
||||
"icon": "🔌"
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
def init_session_state():
|
||||
"""初始化 session state"""
|
||||
if "messages" not in st.session_state:
|
||||
st.session_state.messages = []
|
||||
if "is_running" not in st.session_state:
|
||||
st.session_state.is_running = False
|
||||
if "workflow_result" not in st.session_state:
|
||||
st.session_state.workflow_result = None
|
||||
if "conversation_history" not in st.session_state:
|
||||
st.session_state.conversation_history = []
|
||||
if "current_step" not in st.session_state:
|
||||
st.session_state.current_step = 0
|
||||
if "agent_stats" not in st.session_state:
|
||||
st.session_state.agent_stats = {
|
||||
"PM_Agent": {"messages": 0, "last_active": None},
|
||||
"QA_Agent": {"messages": 0, "last_active": None},
|
||||
"Dev_Agent": {"messages": 0, "last_active": None},
|
||||
"Orchestrator": {"messages": 0, "last_active": None},
|
||||
"User_Proxy": {"messages": 0, "last_active": None}
|
||||
}
|
||||
if "generated_files" not in st.session_state:
|
||||
st.session_state.generated_files = []
|
||||
if "active_agent" not in st.session_state:
|
||||
st.session_state.active_agent = None
|
||||
|
||||
|
||||
def create_agents(api_key: str, base_url: str, model: str):
|
||||
"""创建所有 Agent"""
|
||||
llm_config = get_llm_config(model=model, api_key=api_key, base_url=base_url)
|
||||
|
||||
pm_agent = AssistantAgent(
|
||||
name="PM_Agent",
|
||||
system_message=PM_PROMPT,
|
||||
llm_config=llm_config,
|
||||
description="资深软件产品经理",
|
||||
human_input_mode="NEVER"
|
||||
)
|
||||
|
||||
qa_agent = AssistantAgent(
|
||||
name="QA_Agent",
|
||||
system_message=QA_PROMPT,
|
||||
llm_config=llm_config,
|
||||
description="资深测试工程师",
|
||||
human_input_mode="NEVER"
|
||||
)
|
||||
|
||||
dev_agent = AssistantAgent(
|
||||
name="Dev_Agent",
|
||||
system_message=DEV_PROMPT,
|
||||
llm_config=llm_config,
|
||||
description="资深软件工程师",
|
||||
human_input_mode="NEVER"
|
||||
)
|
||||
|
||||
orchestrator = AssistantAgent(
|
||||
name="Orchestrator",
|
||||
system_message=ORCH_PROMPT,
|
||||
llm_config=llm_config,
|
||||
description="多智能体协调器",
|
||||
human_input_mode="NEVER"
|
||||
)
|
||||
|
||||
user_proxy = UserProxyAgent(
|
||||
name="User_Proxy",
|
||||
human_input_mode="NEVER", # 修复：Web 环境不支持 TERMINAL
|
||||
max_consecutive_auto_reply=0,
|
||||
code_execution_config={
|
||||
"work_dir": "workspace",
|
||||
"use_docker": False,
|
||||
},
|
||||
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE")
|
||||
)
|
||||
|
||||
return pm_agent, qa_agent, dev_agent, orchestrator, user_proxy, llm_config
|
||||
|
||||
|
||||
def display_agent_status_panel():
|
||||
"""显示 Agent 状态面板"""
|
||||
st.subheader("🎭 Agent 实时状态")
|
||||
|
||||
cols = st.columns(5)
|
||||
agent_names = list(AGENT_CONFIG.keys())
|
||||
|
||||
for i, (agent_name, config) in enumerate(AGENT_CONFIG.items()):
|
||||
with cols[i]:
|
||||
stats = st.session_state.agent_stats.get(agent_name, {})
|
||||
msg_count = stats.get("messages", 0)
|
||||
last_active = stats.get("last_active")
|
||||
|
||||
is_active = st.session_state.active_agent == agent_name
|
||||
|
||||
status_color = config["color"]
|
||||
if is_active:
|
||||
status_html = f'<span class="agent-status-indicator" style="background: {status_color}; animation: pulse 1s infinite;"></span>'
|
||||
else:
|
||||
status_html = f'<span style="display:inline-block;width:12px;height:12px;border-radius:50%;background:{status_color};opacity:0.3;"></span>'
|
||||
|
||||
st.markdown(f"""
|
||||
<div style="text-align:center;padding:1rem;border:2px {'solid' if is_active else 'dashed'} {status_color};border-radius:0.5rem;">
|
||||
<div style="font-size:2rem;">{config['avatar']}</div>
|
||||
<div style="font-weight:bold;color:{status_color};">{config['name']}</div>
|
||||
<div style="font-size:0.8rem;color:#666;">消息：{msg_count}</div>
|
||||
<div style="font-size:0.7rem;color:#999;">{status_html}{'工作中' if is_active else '等待中'}</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
|
||||
def display_workflow_diagram():
|
||||
"""显示工作流程图"""
|
||||
st.subheader("🔄 工作流程图")
|
||||
|
||||
# 使用 Mermaid 绘制流程图
|
||||
mermaid_code = """
|
||||
```mermaid
|
||||
graph LR
|
||||
A[👤 用户需求] --> B[📋 PM Agent]
|
||||
B --> C[📄 SRS 文档]
|
||||
C --> D[✅ QA Agent]
|
||||
D --> E[🧪 测试用例]
|
||||
E --> F[💻 Dev Agent]
|
||||
F --> G[💾 源代码]
|
||||
G --> H[🔧 自动测试]
|
||||
H -->|失败 | F
|
||||
H -->|通过 | I[🎯 Orchestrator]
|
||||
I --> J[✅ 最终报告]
|
||||
|
||||
style B fill:#e3f2fd,stroke:#2196f3,stroke-width:3px
|
||||
style D fill:#e8f5e9,stroke:#4caf50,stroke-width:3px
|
||||
style F fill:#fff3e0,stroke:#ff9800,stroke-width:3px
|
||||
style I fill:#f3e5f5,stroke:#9c27b0,stroke-width:3px
|
||||
```
|
||||
"""
|
||||
st.markdown(mermaid_code)
|
||||
|
||||
|
||||
def display_message(agent_name: str, message: str, timestamp: str, index: int):
|
||||
"""显示单条消息（增强版）"""
|
||||
config = AGENT_CONFIG.get(agent_name, {"name": agent_name, "avatar": "🤖", "color": "#999"})
|
||||
|
||||
# 折叠长消息
|
||||
if len(message) > 500:
|
||||
with st.expander(f"{config['avatar']} {config['name']} - {timestamp[:16]}"):
|
||||
st.markdown(message)
|
||||
else:
|
||||
with st.chat_message(name=agent_name.lower().replace("_", ""), avatar=config['avatar']):
|
||||
st.markdown(f"**{config['avatar']} {config['name']}** *({timestamp[:16]})*")
|
||||
st.markdown(message)
|
||||
|
||||
|
||||
def display_timeline():
|
||||
"""显示时间线视图"""
|
||||
st.subheader("⏱️ 执行时间线")
|
||||
|
||||
if not st.session_state.messages:
|
||||
st.info("暂无执行记录")
|
||||
return
|
||||
|
||||
# 按时间分组显示关键事件
|
||||
timeline_data = []
|
||||
for msg in st.session_state.messages:
|
||||
agent = msg.get("agent_name", "Unknown")
|
||||
timestamp = msg.get("timestamp", "")[:19].replace('T', ' ')
|
||||
message = msg.get("message", "")[:100]
|
||||
|
||||
if agent in AGENT_CONFIG:
|
||||
timeline_data.append({
|
||||
"time": timestamp,
|
||||
"agent": agent,
|
||||
"message": message,
|
||||
"icon": AGENT_CONFIG[agent]["avatar"]
|
||||
})
|
||||
|
||||
# 倒序显示（最新的在前）
|
||||
for item in reversed(timeline_data[-10:]): # 只显示最近 10 条
|
||||
with st.container():
|
||||
st.markdown(f"""
|
||||
<div class="timeline-item">
|
||||
<strong>{item['icon']} {item['time']}</strong><br>
|
||||
<span style="color:#666;">{AGENT_CONFIG.get(item['agent'], {}).get('name', item['agent'])}</span><br>
|
||||
{item['message']}...
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
|
||||
def display_generated_files():
|
||||
"""显示生成的文件"""
|
||||
st.subheader("📁 生成的文件")
|
||||
|
||||
workspace_dir = Path("workspace")
|
||||
if not workspace_dir.exists():
|
||||
st.info("工作目录为空")
|
||||
return
|
||||
|
||||
files = list(workspace_dir.glob("*"))
|
||||
|
||||
if not files:
|
||||
st.info("暂无生成的文件")
|
||||
return
|
||||
|
||||
for file_path in files:
|
||||
if file_path.is_file():
|
||||
file_size = file_path.stat().st_size
|
||||
file_type = file_path.suffix
|
||||
|
||||
icon_map = {
|
||||
".py": "🐍",
|
||||
".md": "📝",
|
||||
".txt": "📄",
|
||||
".json": "📊"
|
||||
}
|
||||
|
||||
icon = icon_map.get(file_type, "📎")
|
||||
|
||||
with st.container():
|
||||
st.markdown(f"""
|
||||
<div class="file-card">
|
||||
<div style="display:flex;justify-content:space-between;align-items:center;">
|
||||
<div>
|
||||
<span style="font-size:1.5rem;">{icon}</span>
|
||||
<strong>{file_path.name}</strong>
|
||||
<span style="color:#666;font-size:0.9rem;margin-left:1rem;">
|
||||
{file_size:,} bytes
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# 文件内容预览
|
||||
if st.button(f"👁️ 预览 {file_path.name}", key=f"preview_{file_path.name}"):
|
||||
try:
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
content = f.read()
|
||||
st.code(content[:2000] + ("..." if len(content) > 2000 else ""))
|
||||
except Exception as e:
|
||||
st.error(f"读取失败：{e}")
|
||||
|
||||
|
||||
def display_chat_interface():
|
||||
"""显示聊天界面"""
|
||||
st.subheader("💬 Agent 对话实录")
|
||||
|
||||
if not st.session_state.messages:
|
||||
st.info("👈 暂无对话，请在下方输入需求并启动工作流")
|
||||
else:
|
||||
# 使用容器显示消息
|
||||
chat_container = st.container()
|
||||
with chat_container:
|
||||
for idx, msg in enumerate(st.session_state.messages):
|
||||
display_message(
|
||||
agent_name=msg.get("agent_name", "Unknown"),
|
||||
message=msg.get("message", ""),
|
||||
timestamp=msg.get("timestamp", ""),
|
||||
index=idx
|
||||
)
|
||||
|
||||
|
||||
def export_conversation(messages: List[Dict]) -> str:
|
||||
"""导出对话为 Markdown"""
|
||||
md_content = "# AutoGen SDLC 对话历史\n\n"
|
||||
md_content += f"**导出时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
|
||||
md_content += "---\n\n"
|
||||
|
||||
for msg in messages:
|
||||
timestamp = msg.get("timestamp", "")[:19].replace('T', ' ')
|
||||
agent = msg.get("agent_name", "Unknown")
|
||||
content = msg.get("message", "")
|
||||
|
||||
config = AGENT_CONFIG.get(agent, {"avatar": "🤖", "name": agent})
|
||||
|
||||
md_content += f"### {config['avatar']} [{timestamp}] {config['name']}\n\n"
|
||||
md_content += f"{content}\n\n"
|
||||
md_content += "---\n\n"
|
||||
|
||||
return md_content
|
||||
|
||||
|
||||
def main():
|
||||
"""主应用"""
|
||||
init_session_state()
|
||||
|
||||
# ===== 顶部标题区 =====
|
||||
st.title("🤖 AutoGen SDLC 多智能体协作平台")
|
||||
st.markdown("**基于 AutoGen + Qwen3.5-flash 的端到端软件交付系统 · 实时可视化 Agent 协作**")
|
||||
|
||||
# ===== 侧边栏 - 配置区域 =====
|
||||
with st.sidebar:
|
||||
st.title("⚙️ 控制中心")
|
||||
|
||||
# API 配置
|
||||
st.subheader("🔑 模型配置")
|
||||
api_key = st.text_input(
|
||||
"API Key",
|
||||
type="password",
|
||||
value=os.getenv("DASHSCOPE_API_KEY", ""),
|
||||
help="阿里云 DashScope API Key"
|
||||
)
|
||||
|
||||
base_url = st.text_input(
|
||||
"Base URL",
|
||||
value="https://dashscope.aliyuncs.com/compatible-mode/v1",
|
||||
help="API Base URL"
|
||||
)
|
||||
|
||||
model = st.selectbox(
|
||||
"模型选择",
|
||||
options=["qwen3.5-flash", "qwen-max", "qwen-plus", "qwen-turbo"],
|
||||
index=0,
|
||||
help="选择使用的 Qwen 模型"
|
||||
)
|
||||
|
||||
# Agent 参数
|
||||
st.subheader("🎛️ Agent 参数")
|
||||
max_round = st.slider(
|
||||
"最大对话轮数",
|
||||
min_value=5,
|
||||
max_value=50,
|
||||
value=20,
|
||||
step=1
|
||||
)
|
||||
|
||||
temperature = st.slider(
|
||||
"温度参数",
|
||||
min_value=0.0,
|
||||
max_value=1.0,
|
||||
value=0.7,
|
||||
step=0.1
|
||||
)
|
||||
|
||||
st.divider()
|
||||
|
||||
# 控制按钮
|
||||
st.subheader("🎮 流程控制")
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
start_btn = st.button("▶️ 启动", use_container_width=True, type="primary")
|
||||
with col2:
|
||||
stop_btn = st.button("⏸️ 暂停", use_container_width=True)
|
||||
|
||||
st.divider()
|
||||
|
||||
# 导出选项
|
||||
st.subheader("💾 数据导出")
|
||||
if st.button("📄 导出 JSON", use_container_width=True):
|
||||
if st.session_state.messages:
|
||||
json_str = json.dumps(st.session_state.messages, ensure_ascii=False, indent=2)
|
||||
st.download_button(
|
||||
label="下载 JSON",
|
||||
data=json_str,
|
||||
file_name=f"conversation_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
|
||||
mime="application/json"
|
||||
)
|
||||
|
||||
if st.button("📝 导出 Markdown", use_container_width=True):
|
||||
if st.session_state.messages:
|
||||
md_content = export_conversation(st.session_state.messages)
|
||||
st.download_button(
|
||||
label="下载 Markdown",
|
||||
data=md_content,
|
||||
file_name=f"conversation_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md",
|
||||
mime="text/markdown"
|
||||
)
|
||||
|
||||
# ===== 主区域 - 三栏布局 =====
|
||||
# 第一行：状态指标
|
||||
status_col1, status_col2, status_col3, status_col4 = st.columns(4)
|
||||
|
||||
with status_col1:
|
||||
total_msgs = len(st.session_state.messages)
|
||||
st.metric("💬 总消息数", total_msgs)
|
||||
|
||||
with status_col2:
|
||||
status = "🟢 运行中" if st.session_state.is_running else "🔴 已停止"
|
||||
status_class = "running" if st.session_state.is_running else "stopped"
|
||||
st.markdown(f"""
|
||||
<div class="status-card status-{status_class}">
|
||||
{status}
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
with status_col3:
|
||||
result_status = "✅ 成功" if st.session_state.workflow_result and st.session_state.workflow_result.get("success") else "⏳ 未开始"
|
||||
st.metric("📊 工作流状态", result_status)
|
||||
|
||||
with status_col4:
|
||||
files_count = len(list(Path("workspace").glob("*"))) if Path("workspace").exists() else 0
|
||||
st.metric("📁 生成文件数", files_count)
|
||||
|
||||
st.divider()
|
||||
|
||||
# 第二行：左侧 - Agent 状态 + 流程图，右侧 - 时间线
|
||||
left_col, right_col = st.columns([2, 1])
|
||||
|
||||
with left_col:
|
||||
display_agent_status_panel()
|
||||
st.divider()
|
||||
display_workflow_diagram()
|
||||
|
||||
with right_col:
|
||||
display_timeline()
|
||||
|
||||
st.divider()
|
||||
|
||||
# 第三行：聊天界面
|
||||
display_chat_interface()
|
||||
|
||||
# ===== 底部 - 用户需求输入 =====
|
||||
st.divider()
|
||||
|
||||
# 用户输入
|
||||
user_input = st.chat_input("💡 请输入您的软件需求，例如：我需要一个电池健康状态预测 API...")
|
||||
|
||||
if user_input:
|
||||
# 添加用户消息
|
||||
user_msg = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"agent_name": "User_Proxy",
|
||||
"role": "user",
|
||||
"message": user_input
|
||||
}
|
||||
st.session_state.messages.append(user_msg)
|
||||
st.session_state.conversation_history.append(user_msg)
|
||||
|
||||
# 更新统计
|
||||
st.session_state.agent_stats["User_Proxy"]["messages"] += 1
|
||||
st.session_state.agent_stats["User_Proxy"]["last_active"] = datetime.now().isoformat()
|
||||
|
||||
# 刷新显示
|
||||
st.rerun()
|
||||
|
||||
# ===== 工作流执行逻辑 =====
|
||||
if start_btn:
|
||||
if not api_key:
|
||||
st.error("请先设置 API Key！")
|
||||
st.stop()
|
||||
|
||||
if not AUTOGEN_AVAILABLE:
|
||||
st.error("请先安装 AutoGen: pip install pyautogen")
|
||||
st.stop()
|
||||
|
||||
# 获取最后一条用户消息
|
||||
user_messages = [m for m in st.session_state.messages if m.get("role") == "user"]
|
||||
if not user_messages:
|
||||
st.warning("请先输入需求！")
|
||||
st.stop()
|
||||
|
||||
latest_requirement = user_messages[-1]["message"]
|
||||
|
||||
# 设置运行状态
|
||||
st.session_state.is_running = True
|
||||
st.session_state.current_step = 0
|
||||
|
||||
# 创建进度占位符
|
||||
progress_placeholder = st.empty()
|
||||
|
||||
with progress_placeholder:
|
||||
st.info("🚀 正在启动 SDLC 工作流，请稍候...")
|
||||
|
||||
try:
|
||||
# 创建 Agent
|
||||
pm_agent, qa_agent, dev_agent, orchestrator, user_proxy, llm_config = create_agents(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
model=model
|
||||
)
|
||||
|
||||
# 创建 GroupChat
|
||||
groupchat = GroupChat(
|
||||
agents=[pm_agent, qa_agent, dev_agent, orchestrator, user_proxy],
|
||||
messages=[],
|
||||
max_round=max_round,
|
||||
speaker_selection_method="round_robin"
|
||||
)
|
||||
|
||||
manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)
|
||||
|
||||
# 构建初始消息
|
||||
initial_message = f"""
|
||||
请启动完整的 SDLC 流程：
|
||||
|
||||
【用户需求】
|
||||
{latest_requirement}
|
||||
|
||||
【工作流程】
|
||||
1. PM_Agent → 生成 SRS
|
||||
2. QA_Agent → 生成测试用例
|
||||
3. Dev_Agent → 编写代码
|
||||
4. User_Proxy → 执行测试
|
||||
5. Orchestrator → 汇总报告
|
||||
|
||||
开始协作！
|
||||
"""
|
||||
|
||||
# 记录第一条消息
|
||||
first_msg = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"agent_name": "Orchestrator",
|
||||
"role": "assistant",
|
||||
"message": f"🎯 启动 SDLC 工作流，需求：{latest_requirement[:100]}..."
|
||||
}
|
||||
st.session_state.messages.append(first_msg)
|
||||
|
||||
# 更新统计
|
||||
st.session_state.agent_stats["Orchestrator"]["messages"] += 1
|
||||
st.session_state.active_agent = "Orchestrator"
|
||||
|
||||
# 执行对话
|
||||
chat_result = user_proxy.initiate_chat(
|
||||
manager,
|
||||
message=initial_message,
|
||||
max_turns=max_round
|
||||
)
|
||||
|
||||
# 记录所有对话
|
||||
for msg in groupchat.messages:
|
||||
chat_msg = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"agent_name": msg.get("name", "Unknown"),
|
||||
"role": msg.get("role", "assistant"),
|
||||
"message": msg.get("content", "")
|
||||
}
|
||||
|
||||
# 避免重复添加
|
||||
if chat_msg not in st.session_state.messages:
|
||||
st.session_state.messages.append(chat_msg)
|
||||
|
||||
# 更新统计
|
||||
agent_name = msg.get("name", "Unknown")
|
||||
if agent_name in st.session_state.agent_stats:
|
||||
st.session_state.agent_stats[agent_name]["messages"] += 1
|
||||
st.session_state.agent_stats[agent_name]["last_active"] = datetime.now().isoformat()
|
||||
|
||||
# 扫描生成的文件
|
||||
if Path("workspace").exists():
|
||||
files = list(Path("workspace").glob("*"))
|
||||
st.session_state.generated_files = [str(f) for f in files if f.is_file()]
|
||||
|
||||
# 更新状态
|
||||
st.session_state.workflow_result = {
|
||||
"success": True,
|
||||
"summary": chat_result.summary if hasattr(chat_result, 'summary') else "完成"
|
||||
}
|
||||
st.session_state.current_step = 5
|
||||
st.session_state.is_running = False
|
||||
st.session_state.active_agent = None
|
||||
|
||||
progress_placeholder.success("✅ SDLC 工作流完成！")
|
||||
|
||||
# 刷新显示
|
||||
st.rerun()
|
||||
|
||||
except Exception as e:
|
||||
st.session_state.is_running = False
|
||||
st.session_state.active_agent = None
|
||||
st.error(f"❌ 错误：{str(e)}")
|
||||
st.session_state.workflow_result = {"success": False, "error": str(e)}
|
||||
|
||||
if stop_btn:
|
||||
st.session_state.is_running = False
|
||||
st.session_state.active_agent = None
|
||||
st.info("⏸️ 工作流已暂停")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
652
frontend/streamlit_app_v3.py
Normal file
652
frontend/streamlit_app_v3.py
Normal file
@@ -0,0 +1,652 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Streamlit 前端 v3 - Agent 对话流可视化版本
|
||||
核心特性：
|
||||
1. 清晰展示每个 Agent 当前在做什么（任务标签）
|
||||
2. Agent 之间的对话流（类似聊天室）
|
||||
3. 实时活动指示器（高亮当前发言的 Agent）
|
||||
4. 对话历史完整记录
|
||||
"""
|
||||
import streamlit as st
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
import time
|
||||
from typing import Dict, Any, List
|
||||
import json
|
||||
|
||||
# 添加项目根目录到路径
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
try:
|
||||
from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
|
||||
AUTOGEN_AVAILABLE = True
|
||||
except ImportError:
|
||||
AUTOGEN_AVAILABLE = False
|
||||
|
||||
from config.llm_config import (
|
||||
get_llm_config,
|
||||
PM_PROMPT,
|
||||
QA_PROMPT,
|
||||
DEV_PROMPT,
|
||||
ORCH_PROMPT
|
||||
)
|
||||
|
||||
|
||||
# 页面配置
|
||||
st.set_page_config(
|
||||
page_title="AutoGen SDLC - Agent 对话流",
|
||||
page_icon="💬",
|
||||
layout="wide",
|
||||
initial_sidebar_state="expanded"
|
||||
)
|
||||
|
||||
# 自定义 CSS 样式 - 突出对话流
|
||||
st.markdown("""
|
||||
<style>
|
||||
/* 主对话区域 */
|
||||
.chat-flow-container {
|
||||
background: #f0f2f6;
|
||||
padding: 20px;
|
||||
border-radius: 10px;
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
|
||||
/* Agent 对话气泡 */
|
||||
.agent-chat-bubble {
|
||||
padding: 15px 20px;
|
||||
margin: 10px 0;
|
||||
border-radius: 15px;
|
||||
max-width: 85%;
|
||||
position: relative;
|
||||
animation: bubble-in 0.3s ease;
|
||||
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
|
||||
}
|
||||
|
||||
@keyframes bubble-in {
|
||||
from {
|
||||
opacity: 0;
|
||||
transform: translateY(10px);
|
||||
}
|
||||
to {
|
||||
opacity: 1;
|
||||
transform: translateY(0);
|
||||
}
|
||||
}
|
||||
|
||||
/* 不同 Agent 的气泡样式 */
|
||||
.pm-bubble {
|
||||
background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%);
|
||||
border: 2px solid #2196f3;
|
||||
margin-left: 0;
|
||||
}
|
||||
|
||||
.qa-bubble {
|
||||
background: linear-gradient(135deg, #e8f5e9 0%, #c8e6c9 100%);
|
||||
border: 2px solid #4caf50;
|
||||
margin-left: 0;
|
||||
}
|
||||
|
||||
.dev-bubble {
|
||||
background: linear-gradient(135deg, #fff3e0 0%, #ffe0b2 100%);
|
||||
border: 2px solid #ff9800;
|
||||
margin-left: 0;
|
||||
}
|
||||
|
||||
.orchestrator-bubble {
|
||||
background: linear-gradient(135deg, #f3e5f5 0%, #e1bee7 100%);
|
||||
border: 2px solid #9c27b0;
|
||||
margin-left: 0;
|
||||
}
|
||||
|
||||
.user-bubble {
|
||||
background: linear-gradient(135deg, #efebe9 0%, #d7ccc8 100%);
|
||||
border: 2px solid #795548;
|
||||
margin-left: auto;
|
||||
}
|
||||
|
||||
/* Agent 头像和名称 */
|
||||
.agent-chat-header {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
margin-bottom: 8px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
.agent-chat-avatar {
|
||||
font-size: 1.5rem;
|
||||
margin-right: 8px;
|
||||
}
|
||||
|
||||
.agent-chat-name {
|
||||
font-size: 1rem;
|
||||
color: #333;
|
||||
}
|
||||
|
||||
.agent-chat-time {
|
||||
margin-left: auto;
|
||||
font-size: 0.75rem;
|
||||
color: #666;
|
||||
}
|
||||
|
||||
/* 任务标签 */
|
||||
.task-badge {
|
||||
display: inline-block;
|
||||
padding: 3px 10px;
|
||||
border-radius: 12px;
|
||||
font-size: 0.75rem;
|
||||
font-weight: bold;
|
||||
margin-left: 8px;
|
||||
color: white;
|
||||
}
|
||||
|
||||
.pm-task { background: #2196f3; }
|
||||
.qa-task { background: #4caf50; }
|
||||
.dev-task { background: #ff9800; }
|
||||
.orchestrator-task { background: #9c27b0; }
|
||||
.user-task { background: #795548; }
|
||||
|
||||
/* Agent 状态卡片 */
|
||||
.agent-status-card {
|
||||
padding: 15px;
|
||||
border-radius: 10px;
|
||||
text-align: center;
|
||||
border: 3px solid #e0e0e0;
|
||||
transition: all 0.3s ease;
|
||||
background: white;
|
||||
}
|
||||
|
||||
.agent-status-card.active {
|
||||
border-color: #4caf50;
|
||||
background: linear-gradient(135deg, #e8f5e9 0%, #c8e6c9 100%);
|
||||
animation: agent-pulse 1.5s infinite;
|
||||
}
|
||||
|
||||
@keyframes agent-pulse {
|
||||
0%, 100% { transform: scale(1); }
|
||||
50% { transform: scale(1.02); }
|
||||
}
|
||||
|
||||
.agent-status-avatar {
|
||||
font-size: 2.5rem;
|
||||
margin-bottom: 5px;
|
||||
}
|
||||
|
||||
.agent-status-name {
|
||||
font-weight: bold;
|
||||
font-size: 0.9rem;
|
||||
margin-bottom: 5px;
|
||||
}
|
||||
|
||||
.agent-status-task {
|
||||
font-size: 0.75rem;
|
||||
color: #666;
|
||||
min-height: 20px;
|
||||
}
|
||||
|
||||
.agent-status-indicator {
|
||||
display: inline-block;
|
||||
width: 8px;
|
||||
height: 8px;
|
||||
border-radius: 50%;
|
||||
margin-right: 5px;
|
||||
}
|
||||
|
||||
.status-active {
|
||||
background: #4caf50;
|
||||
animation: dot-pulse 1s infinite;
|
||||
}
|
||||
|
||||
.status-waiting {
|
||||
background: #ffc107;
|
||||
}
|
||||
|
||||
.status-inactive {
|
||||
background: #e0e0e0;
|
||||
}
|
||||
|
||||
@keyframes dot-pulse {
|
||||
0%, 100% { opacity: 1; }
|
||||
50% { opacity: 0.3; }
|
||||
}
|
||||
|
||||
/* 对话流指示器 */
|
||||
.chat-flow-indicator {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
margin: 10px 0;
|
||||
padding: 10px;
|
||||
background: white;
|
||||
border-radius: 8px;
|
||||
}
|
||||
|
||||
.flow-arrow {
|
||||
font-size: 1.5rem;
|
||||
color: #2196f3;
|
||||
margin: 0 10px;
|
||||
}
|
||||
|
||||
/* 当前活动提示 */
|
||||
.active-agent-banner {
|
||||
background: linear-gradient(135deg, #4caf50, #8bc34a);
|
||||
color: white;
|
||||
padding: 10px 20px;
|
||||
border-radius: 8px;
|
||||
text-align: center;
|
||||
font-weight: bold;
|
||||
margin-bottom: 20px;
|
||||
animation: banner-pulse 2s infinite;
|
||||
}
|
||||
|
||||
@keyframes banner-pulse {
|
||||
0%, 100% { opacity: 1; }
|
||||
50% { opacity: 0.8; }
|
||||
}
|
||||
</style>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
|
||||
# Agent 配置
|
||||
AGENT_CONFIG = {
|
||||
"PM_Agent": {
|
||||
"name": "产品经理",
|
||||
"avatar": "📋",
|
||||
"color": "#2196f3",
|
||||
"bubble_class": "pm-bubble",
|
||||
"task_class": "pm-task",
|
||||
"default_task": "待命"
|
||||
},
|
||||
"QA_Agent": {
|
||||
"name": "测试工程师",
|
||||
"avatar": "✅",
|
||||
"color": "#4caf50",
|
||||
"bubble_class": "qa-bubble",
|
||||
"task_class": "qa-task",
|
||||
"default_task": "待命"
|
||||
},
|
||||
"Dev_Agent": {
|
||||
"name": "开发工程师",
|
||||
"avatar": "💻",
|
||||
"color": "#ff9800",
|
||||
"bubble_class": "dev-bubble",
|
||||
"task_class": "dev-task",
|
||||
"default_task": "待命"
|
||||
},
|
||||
"Orchestrator": {
|
||||
"name": "协调器",
|
||||
"avatar": "🎯",
|
||||
"color": "#9c27b0",
|
||||
"bubble_class": "orchestrator-bubble",
|
||||
"task_class": "orchestrator-task",
|
||||
"default_task": "待命"
|
||||
},
|
||||
"User_Proxy": {
|
||||
"name": "用户代理",
|
||||
"avatar": "👤",
|
||||
"color": "#795548",
|
||||
"bubble_class": "user-bubble",
|
||||
"task_class": "user-task",
|
||||
"default_task": "待命"
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
def init_session_state():
|
||||
"""初始化 session state"""
|
||||
if "messages" not in st.session_state:
|
||||
st.session_state.messages = []
|
||||
if "is_running" not in st.session_state:
|
||||
st.session_state.is_running = False
|
||||
if "current_agent" not in st.session_state:
|
||||
st.session_state.current_agent = None
|
||||
if "agent_tasks" not in st.session_state:
|
||||
st.session_state.agent_tasks = {
|
||||
"PM_Agent": "待命",
|
||||
"QA_Agent": "待命",
|
||||
"Dev_Agent": "待命",
|
||||
"Orchestrator": "待命",
|
||||
"User_Proxy": "待命"
|
||||
}
|
||||
if "agent_stats" not in st.session_state:
|
||||
st.session_state.agent_stats = {agent: 0 for agent in AGENT_CONFIG}
|
||||
|
||||
|
||||
def display_agent_status_row():
|
||||
"""显示 Agent 状态行 - 突出当前活动的 Agent"""
|
||||
st.subheader("🎭 Agent 实时状态")
|
||||
|
||||
cols = st.columns(5)
|
||||
|
||||
for i, (agent_key, config) in enumerate(AGENT_CONFIG.items()):
|
||||
with cols[i]:
|
||||
is_active = st.session_state.current_agent == agent_key
|
||||
current_task = st.session_state.agent_tasks.get(agent_key, "待命")
|
||||
msg_count = st.session_state.agent_stats.get(agent_key, 0)
|
||||
|
||||
status_class = "active" if is_active else ""
|
||||
indicator_class = "status-active" if is_active else "status-inactive"
|
||||
|
||||
st.markdown(f"""
|
||||
<div class="agent-status-card {status_class}">
|
||||
<div class="agent-status-avatar">{config['avatar']}</div>
|
||||
<div class="agent-status-name">
|
||||
<span class="agent-status-indicator {indicator_class}"></span>
|
||||
{config['name']}
|
||||
</div>
|
||||
<div class="agent-status-task">
|
||||
📝 {current_task}
|
||||
</div>
|
||||
<div style="font-size:0.7rem;color:#999;margin-top:5px;">
|
||||
💬 {msg_count} 条消息
|
||||
</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
|
||||
def display_active_agent_banner():
|
||||
"""显示当前活动 Agent 的横幅"""
|
||||
if st.session_state.current_agent:
|
||||
config = AGENT_CONFIG.get(st.session_state.current_agent, {})
|
||||
st.markdown(f"""
|
||||
<div class="active-agent-banner">
|
||||
{config.get('avatar', '🤖')} 当前发言：{config.get('name', 'Unknown')} - {st.session_state.agent_tasks.get(st.session_state.current_agent, '工作中...')}
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
|
||||
def display_chat_flow():
|
||||
"""显示 Agent 对话流 - 核心功能"""
|
||||
st.subheader("💬 Agent 对话流")
|
||||
|
||||
if not st.session_state.messages:
|
||||
st.info("👈 暂无对话，请启动工作流")
|
||||
return
|
||||
|
||||
# 使用容器显示对话流
|
||||
chat_container = st.container()
|
||||
|
||||
for idx, msg in enumerate(st.session_state.messages):
|
||||
agent_key = msg.get("agent_key", "Unknown")
|
||||
config = AGENT_CONFIG.get(agent_key, {"name": "未知", "avatar": "🤖", "bubble_class": ""})
|
||||
task_class = AGENT_CONFIG.get(agent_key, {}).get("task_class", "")
|
||||
|
||||
timestamp = msg.get("timestamp", "")
|
||||
if timestamp:
|
||||
time_str = datetime.fromisoformat(timestamp).strftime("%H:%M:%S")
|
||||
else:
|
||||
time_str = datetime.now().strftime("%H:%M:%S")
|
||||
|
||||
content = msg.get("content", "")
|
||||
task = msg.get("task", "工作中")
|
||||
|
||||
# 显示对话气泡
|
||||
st.markdown(f"""
|
||||
<div class="agent-chat-bubble {config['bubble_class']}">
|
||||
<div class="agent-chat-header">
|
||||
<span class="agent-chat-avatar">{config['avatar']}</span>
|
||||
<span class="agent-chat-name">{config['name']}</span>
|
||||
<span class="task-badge {task_class}">{task}</span>
|
||||
<span class="agent-chat-time">{time_str}</span>
|
||||
</div>
|
||||
<div style="color:#333;line-height:1.6;">{content}</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
# 显示对话流指示器（下一条消息的箭头）
|
||||
if idx < len(st.session_state.messages) - 1:
|
||||
next_msg = st.session_state.messages[idx + 1]
|
||||
next_agent = next_msg.get("agent_key", "")
|
||||
next_config = AGENT_CONFIG.get(next_agent, {})
|
||||
|
||||
if next_agent != agent_key:
|
||||
st.markdown(f"""
|
||||
<div class="chat-flow-indicator">
|
||||
<div style="flex:1;"></div>
|
||||
<div class="flow-arrow">⬇️</div>
|
||||
<div style="flex:1;text-align:center;color:#666;font-size:0.8rem;">
|
||||
传递给 {next_config.get('name', 'Unknown')}
|
||||
</div>
|
||||
</div>
|
||||
""", unsafe_allow_html=True)
|
||||
|
||||
|
||||
def create_agents(api_key: str, base_url: str, model: str):
|
||||
"""创建所有 Agent"""
|
||||
llm_config = get_llm_config(model=model, api_key=api_key, base_url=base_url)
|
||||
|
||||
pm_agent = AssistantAgent(
|
||||
name="PM_Agent",
|
||||
system_message=PM_PROMPT,
|
||||
llm_config=llm_config,
|
||||
description="资深软件产品经理",
|
||||
human_input_mode="NEVER"
|
||||
)
|
||||
|
||||
qa_agent = AssistantAgent(
|
||||
name="QA_Agent",
|
||||
system_message=QA_PROMPT,
|
||||
llm_config=llm_config,
|
||||
description="资深测试工程师",
|
||||
human_input_mode="NEVER"
|
||||
)
|
||||
|
||||
dev_agent = AssistantAgent(
|
||||
name="Dev_Agent",
|
||||
system_message=DEV_PROMPT,
|
||||
llm_config=llm_config,
|
||||
description="资深软件工程师",
|
||||
human_input_mode="NEVER"
|
||||
)
|
||||
|
||||
orchestrator = AssistantAgent(
|
||||
name="Orchestrator",
|
||||
system_message=ORCH_PROMPT,
|
||||
llm_config=llm_config,
|
||||
description="多智能体协调器",
|
||||
human_input_mode="NEVER"
|
||||
)
|
||||
|
||||
user_proxy = UserProxyAgent(
|
||||
name="User_Proxy",
|
||||
human_input_mode="NEVER",
|
||||
max_consecutive_auto_reply=0,
|
||||
code_execution_config={
|
||||
"work_dir": "workspace",
|
||||
"use_docker": False,
|
||||
},
|
||||
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE")
|
||||
)
|
||||
|
||||
return pm_agent, qa_agent, dev_agent, orchestrator, user_proxy, llm_config
|
||||
|
||||
|
||||
def add_message(agent_key: str, content: str, task: str = "工作中"):
|
||||
"""添加消息到对话流"""
|
||||
msg = {
|
||||
"agent_key": agent_key,
|
||||
"content": content,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"task": task
|
||||
}
|
||||
st.session_state.messages.append(msg)
|
||||
|
||||
# 更新统计
|
||||
st.session_state.agent_stats[agent_key] = st.session_state.agent_stats.get(agent_key, 0) + 1
|
||||
|
||||
# 更新当前 Agent
|
||||
st.session_state.current_agent = agent_key
|
||||
|
||||
# 更新任务
|
||||
st.session_state.agent_tasks[agent_key] = task
|
||||
|
||||
|
||||
def main():
|
||||
"""主应用"""
|
||||
init_session_state()
|
||||
|
||||
# 标题
|
||||
st.title("💬 AutoGen SDLC - Agent 对话流可视化")
|
||||
st.markdown("**清晰展示每个 Agent 在做什么 · 实时追踪 Agent 之间的对话交互**")
|
||||
|
||||
# 侧边栏配置
|
||||
with st.sidebar:
|
||||
st.title("⚙️ 控制中心")
|
||||
|
||||
api_key = st.text_input("API Key", type="password", value=os.getenv("DASHSCOPE_API_KEY", ""))
|
||||
base_url = st.text_input("Base URL", value="https://dashscope.aliyuncs.com/compatible-mode/v1")
|
||||
model = st.selectbox("模型选择", ["qwen3.5-flash", "qwen-max", "qwen-plus", "qwen-turbo"], index=0)
|
||||
|
||||
max_round = st.slider("最大对话轮数", 5, 50, 20)
|
||||
|
||||
st.divider()
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
start_btn = st.button("▶️ 启动对话流", type="primary", use_container_width=True)
|
||||
with col2:
|
||||
stop_btn = st.button("⏸️ 暂停", use_container_width=True)
|
||||
|
||||
st.divider()
|
||||
|
||||
if st.button("🗑️ 清空对话", use_container_width=True):
|
||||
st.session_state.messages = []
|
||||
st.session_state.current_agent = None
|
||||
st.session_state.agent_tasks = {k: "待命" for k in AGENT_CONFIG}
|
||||
st.session_state.agent_stats = {k: 0 for k in AGENT_CONFIG}
|
||||
st.rerun()
|
||||
|
||||
if st.button("📥 导出对话", use_container_width=True):
|
||||
if st.session_state.messages:
|
||||
json_str = json.dumps(st.session_state.messages, ensure_ascii=False, indent=2)
|
||||
st.download_button(
|
||||
label="下载 JSON",
|
||||
data=json_str,
|
||||
file_name=f"agent_chat_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json",
|
||||
mime="application/json"
|
||||
)
|
||||
|
||||
# 主界面
|
||||
display_agent_status_row()
|
||||
st.divider()
|
||||
|
||||
display_active_agent_banner()
|
||||
|
||||
display_chat_flow()
|
||||
|
||||
# 用户输入
|
||||
st.divider()
|
||||
user_input = st.chat_input("💡 输入需求，或点击启动按钮开始...")
|
||||
|
||||
if user_input:
|
||||
add_message("User_Proxy", user_input, "提出需求")
|
||||
st.rerun()
|
||||
|
||||
# 启动工作流
|
||||
if start_btn:
|
||||
if not api_key:
|
||||
st.error("请先设置 API Key")
|
||||
st.stop()
|
||||
|
||||
if not AUTOGEN_AVAILABLE:
|
||||
st.error("请先安装 AutoGen: pip install pyautogen")
|
||||
st.stop()
|
||||
|
||||
# 获取需求
|
||||
user_msgs = [m for m in st.session_state.messages if m.get("agent_key") == "User_Proxy"]
|
||||
if not user_msgs:
|
||||
add_message("User_Proxy", "请开发一个电池健康状态预测 API", "提出需求")
|
||||
|
||||
latest_requirement = user_msgs[-1]["content"] if user_msgs else "请开发一个电池健康状态预测 API"
|
||||
|
||||
st.session_state.is_running = True
|
||||
|
||||
# 进度提示
|
||||
progress_placeholder = st.empty()
|
||||
progress_placeholder.info("🚀 启动 SDLC 工作流，Agent 开始协作...")
|
||||
|
||||
try:
|
||||
# 创建 Agent
|
||||
pm_agent, qa_agent, dev_agent, orchestrator, user_proxy, llm_config = create_agents(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
model=model
|
||||
)
|
||||
|
||||
# 添加 Orchestrator 启动消息
|
||||
add_message("Orchestrator", f"🚀 启动 SDLC 工作流！用户需求：{latest_requirement[:100]}...", "启动流程")
|
||||
|
||||
# 创建 GroupChat
|
||||
groupchat = GroupChat(
|
||||
agents=[pm_agent, qa_agent, dev_agent, orchestrator, user_proxy],
|
||||
messages=[],
|
||||
max_round=max_round,
|
||||
speaker_selection_method="round_robin"
|
||||
)
|
||||
|
||||
manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)
|
||||
|
||||
# 初始消息
|
||||
initial_message = f"""
|
||||
请启动完整的 SDLC 流程：
|
||||
|
||||
【用户需求】
|
||||
{latest_requirement}
|
||||
|
||||
【工作流程】
|
||||
1. PM_Agent → 生成 SRS 文档
|
||||
2. QA_Agent → 生成测试用例
|
||||
3. Dev_Agent → 编写代码
|
||||
4. User_Proxy → 执行测试
|
||||
5. Orchestrator → 汇总报告
|
||||
|
||||
开始协作！每个步骤完成后请明确说明。
|
||||
"""
|
||||
|
||||
# 执行对话
|
||||
with st.spinner("💬 Agent 们正在协作中，请稍候..."):
|
||||
chat_result = user_proxy.initiate_chat(
|
||||
manager,
|
||||
message=initial_message,
|
||||
max_turns=max_round
|
||||
)
|
||||
|
||||
# 记录所有对话
|
||||
for msg in groupchat.messages:
|
||||
agent_name = msg.get("name", "Unknown")
|
||||
content = msg.get("content", "")
|
||||
|
||||
# 推断任务
|
||||
task_map = {
|
||||
"PM_Agent": "需求分析",
|
||||
"QA_Agent": "测试设计",
|
||||
"Dev_Agent": "代码实现",
|
||||
"Orchestrator": "流程协调",
|
||||
"User_Proxy": "测试执行"
|
||||
}
|
||||
task = task_map.get(agent_name, "工作中")
|
||||
|
||||
add_message(agent_name, content, task)
|
||||
|
||||
# 完成
|
||||
add_message("Orchestrator", "✅ SDLC 流程完成！所有任务已完成。", "总结完成")
|
||||
|
||||
progress_placeholder.success("✅ SDLC 工作流完成！查看上方的对话流了解详情。")
|
||||
st.session_state.is_running = False
|
||||
|
||||
st.rerun()
|
||||
|
||||
except Exception as e:
|
||||
st.session_state.is_running = False
|
||||
st.session_state.current_agent = None
|
||||
progress_placeholder.error(f"❌ 错误：{str(e)}")
|
||||
st.error("请检查 API Key 和网络连接")
|
||||
|
||||
if stop_btn:
|
||||
st.session_state.is_running = False
|
||||
st.session_state.current_agent = None
|
||||
st.info("⏸️ 工作流已暂停")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user