757 lines
23 KiB
Python
757 lines
23 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Streamlit 前端 v3 - Agent 对话流可视化版本
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核心特性：
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1. 清晰展示每个 Agent 当前在做什么（任务标签）
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2. Agent 之间的对话流（类似聊天室）
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3. 实时活动指示器（高亮当前发言的 Agent）
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4. 对话历史完整记录
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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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import json
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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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try:
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from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager
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AUTOGEN_AVAILABLE = True
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except ImportError:
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AUTOGEN_AVAILABLE = False
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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 - Agent 对话流",
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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 - 突出对话流
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st.markdown("""
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<style>
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/* 主对话区域 */
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.chat-flow-container {
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background: #f0f2f6;
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padding: 20px;
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border-radius: 10px;
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margin-bottom: 20px;
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}
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/* Agent 对话气泡 */
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.agent-chat-bubble {
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padding: 15px 20px;
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margin: 10px 0;
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border-radius: 15px;
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max-width: 85%;
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position: relative;
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animation: bubble-in 0.3s ease;
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box-shadow: 0 2px 8px rgba(0,0,0,0.1);
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}
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@keyframes bubble-in {
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from {
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opacity: 0;
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transform: translateY(10px);
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}
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to {
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opacity: 1;
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transform: translateY(0);
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}
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}
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/* 不同 Agent 的气泡样式 */
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.pm-bubble {
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background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%);
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border: 2px solid #2196f3;
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margin-left: 0;
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}
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.qa-bubble {
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background: linear-gradient(135deg, #e8f5e9 0%, #c8e6c9 100%);
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border: 2px solid #4caf50;
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margin-left: 0;
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}
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.dev-bubble {
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background: linear-gradient(135deg, #fff3e0 0%, #ffe0b2 100%);
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border: 2px solid #ff9800;
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margin-left: 0;
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}
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.orchestrator-bubble {
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background: linear-gradient(135deg, #f3e5f5 0%, #e1bee7 100%);
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border: 2px solid #9c27b0;
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margin-left: 0;
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}
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.user-bubble {
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background: linear-gradient(135deg, #efebe9 0%, #d7ccc8 100%);
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border: 2px solid #795548;
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margin-left: auto;
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}
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/* Agent 头像和名称 */
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.agent-chat-header {
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display: flex;
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align-items: center;
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margin-bottom: 8px;
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font-weight: bold;
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}
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.agent-chat-avatar {
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font-size: 1.5rem;
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margin-right: 8px;
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}
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.agent-chat-name {
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font-size: 1rem;
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color: #333;
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}
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.agent-chat-time {
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margin-left: auto;
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font-size: 0.75rem;
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color: #666;
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}
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/* 任务标签 */
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.task-badge {
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display: inline-block;
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padding: 3px 10px;
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border-radius: 12px;
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font-size: 0.75rem;
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font-weight: bold;
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margin-left: 8px;
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color: white;
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}
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.pm-task { background: #2196f3; }
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.qa-task { background: #4caf50; }
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.dev-task { background: #ff9800; }
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.orchestrator-task { background: #9c27b0; }
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.user-task { background: #795548; }
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/* Agent 状态卡片 */
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.agent-status-card {
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padding: 15px;
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border-radius: 10px;
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text-align: center;
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border: 3px solid #e0e0e0;
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transition: all 0.3s ease;
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background: white;
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}
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.agent-status-card.active {
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border-color: #4caf50;
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background: linear-gradient(135deg, #e8f5e9 0%, #c8e6c9 100%);
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animation: agent-pulse 1.5s infinite;
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}
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@keyframes agent-pulse {
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0%, 100% { transform: scale(1); }
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50% { transform: scale(1.02); }
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}
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.agent-status-avatar {
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font-size: 2.5rem;
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margin-bottom: 5px;
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}
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.agent-status-name {
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font-weight: bold;
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font-size: 0.9rem;
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margin-bottom: 5px;
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}
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.agent-status-task {
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font-size: 0.75rem;
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color: #666;
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min-height: 20px;
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}
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.agent-status-indicator {
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display: inline-block;
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width: 8px;
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height: 8px;
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border-radius: 50%;
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margin-right: 5px;
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}
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.status-active {
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background: #4caf50;
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animation: dot-pulse 1s infinite;
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}
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.status-waiting {
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background: #ffc107;
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}
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.status-inactive {
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background: #e0e0e0;
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}
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@keyframes dot-pulse {
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0%, 100% { opacity: 1; }
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50% { opacity: 0.3; }
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}
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/* 对话流指示器 */
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.chat-flow-indicator {
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display: flex;
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align-items: center;
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margin: 10px 0;
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padding: 10px;
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background: white;
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border-radius: 8px;
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}
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.flow-arrow {
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font-size: 1.5rem;
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color: #2196f3;
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margin: 0 10px;
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}
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/* 当前活动提示 */
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.active-agent-banner {
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background: linear-gradient(135deg, #4caf50, #8bc34a);
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color: white;
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padding: 10px 20px;
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border-radius: 8px;
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text-align: center;
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font-weight: bold;
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margin-bottom: 20px;
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animation: banner-pulse 2s infinite;
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}
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@keyframes banner-pulse {
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0%, 100% { opacity: 1; }
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50% { opacity: 0.8; }
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}
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</style>
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""", unsafe_allow_html=True)
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# Agent 配置
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AGENT_CONFIG = {
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"PM_Agent": {
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"name": "产品经理",
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"avatar": "📋",
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"color": "#2196f3",
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"bubble_class": "pm-bubble",
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"task_class": "pm-task",
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"default_task": "待命"
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},
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"QA_Agent": {
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"name": "测试工程师",
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"avatar": "✅",
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"color": "#4caf50",
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"bubble_class": "qa-bubble",
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"task_class": "qa-task",
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"default_task": "待命"
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},
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"Dev_Agent": {
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"name": "开发工程师",
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"avatar": "💻",
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"color": "#ff9800",
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"bubble_class": "dev-bubble",
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"task_class": "dev-task",
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"default_task": "待命"
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},
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"Orchestrator": {
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"name": "协调器",
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"avatar": "🎯",
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"color": "#9c27b0",
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"bubble_class": "orchestrator-bubble",
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"task_class": "orchestrator-task",
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"default_task": "待命"
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},
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"User_Proxy": {
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"name": "用户代理",
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"avatar": "👤",
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"color": "#795548",
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"bubble_class": "user-bubble",
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"task_class": "user-task",
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"default_task": "待命"
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}
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}
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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 "current_agent" not in st.session_state:
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st.session_state.current_agent = None
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if "agent_tasks" not in st.session_state:
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st.session_state.agent_tasks = {
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"PM_Agent": "待命",
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"QA_Agent": "待命",
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"Dev_Agent": "待命",
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"Orchestrator": "待命",
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"User_Proxy": "待命"
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}
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if "agent_stats" not in st.session_state:
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st.session_state.agent_stats = {agent: 0 for agent in AGENT_CONFIG}
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def display_agent_status_row():
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"""显示 Agent 状态行 - 突出当前活动的 Agent"""
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st.subheader("🎭 Agent 实时状态")
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cols = st.columns(5)
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for i, (agent_key, config) in enumerate(AGENT_CONFIG.items()):
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with cols[i]:
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is_active = st.session_state.current_agent == agent_key
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current_task = st.session_state.agent_tasks.get(agent_key, "待命")
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msg_count = st.session_state.agent_stats.get(agent_key, 0)
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status_class = "active" if is_active else ""
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indicator_class = "status-active" if is_active else "status-inactive"
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st.markdown(f"""
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<div class="agent-status-card {status_class}">
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<div class="agent-status-avatar">{config['avatar']}</div>
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<div class="agent-status-name">
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<span class="agent-status-indicator {indicator_class}"></span>
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{config['name']}
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</div>
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<div class="agent-status-task">
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📝 {current_task}
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</div>
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<div style="font-size:0.7rem;color:#999;margin-top:5px;">
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💬 {msg_count} 条消息
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</div>
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</div>
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""", unsafe_allow_html=True)
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def display_active_agent_banner():
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"""显示当前活动 Agent 的横幅"""
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if st.session_state.current_agent:
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config = AGENT_CONFIG.get(st.session_state.current_agent, {})
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st.markdown(f"""
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<div class="active-agent-banner">
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{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...')}
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</div>
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""", unsafe_allow_html=True)
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def display_chat_flow():
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"""显示 Agent 对话流 - 核心功能"""
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st.subheader("💬 Agent 对话流")
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if not st.session_state.messages:
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st.info("👈 暂无对话，请启动工作流")
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return
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# 使用容器显示对话流
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chat_container = st.container()
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for idx, msg in enumerate(st.session_state.messages):
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agent_key = msg.get("agent_key", "Unknown")
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config = AGENT_CONFIG.get(agent_key, {"name": "未知", "avatar": "🤖", "bubble_class": ""})
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task_class = AGENT_CONFIG.get(agent_key, {}).get("task_class", "")
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timestamp = msg.get("timestamp", "")
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if timestamp:
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time_str = datetime.fromisoformat(timestamp).strftime("%H:%M:%S")
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else:
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time_str = datetime.now().strftime("%H:%M:%S")
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content = msg.get("content", "")
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task = msg.get("task", "工作中")
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# 显示对话气泡
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st.markdown(f"""
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<div class="agent-chat-bubble {config['bubble_class']}">
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<div class="agent-chat-header">
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<span class="agent-chat-avatar">{config['avatar']}</span>
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<span class="agent-chat-name">{config['name']}</span>
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<span class="task-badge {task_class}">{task}</span>
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<span class="agent-chat-time">{time_str}</span>
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</div>
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<div style="color:#333;line-height:1.6;">{content}</div>
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</div>
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""", unsafe_allow_html=True)
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# 显示对话流指示器（下一条消息的箭头）
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if idx < len(st.session_state.messages) - 1:
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next_msg = st.session_state.messages[idx + 1]
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next_agent = next_msg.get("agent_key", "")
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next_config = AGENT_CONFIG.get(next_agent, {})
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if next_agent != agent_key:
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st.markdown(f"""
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<div class="chat-flow-indicator">
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<div style="flex:1;"></div>
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<div class="flow-arrow">⬇️</div>
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<div style="flex:1;text-align:center;color:#666;font-size:0.8rem;">
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传递给 {next_config.get('name', 'Unknown')}
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</div>
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</div>
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""", unsafe_allow_html=True)
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def save_generated_files():
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"""从对话中提取并保存生成的文件到 workspace 目录"""
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workspace_dir = Path("workspace")
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workspace_dir.mkdir(parents=True, exist_ok=True)
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files_saved = []
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# 遍历所有消息，提取文件内容
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for msg in st.session_state.messages:
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content = msg.get("content", "")
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agent_key = msg.get("agent_key", "")
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# PM Agent 生成 SRS.md
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if agent_key == "PM_Agent" and ("SRS" in content or "软件需求规格" in content):
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srs_file = workspace_dir / "SRS.md"
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# 提取 SRS 内容（查找包含 FR- 或 NFR- 的段落）
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srs_content = content
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if "功能性需求" in content:
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srs_content = content[content.find("功能性需求"):]
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with open(srs_file, 'w', encoding='utf-8') as f:
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f.write(f"# 软件需求规格说明书 (SRS)\n\n")
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f.write(f"**生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
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f.write(srs_content)
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files_saved.append(str(srs_file))
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# QA Agent 生成测试文件
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if agent_key == "QA_Agent" and ("test_" in content or "def test_" in content):
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test_file = workspace_dir / "test_battery_health.py"
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# 提取 Python 代码
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if "```python" in content:
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code = content.split("```python")[1].split("```")[0]
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else:
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code = content
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with open(test_file, 'w', encoding='utf-8') as f:
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f.write(f'"""\n电池健康状态测试用例\n生成时间：{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n"""\n\n')
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f.write(code)
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files_saved.append(str(test_file))
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# Dev Agent 生成源代码
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if agent_key == "Dev_Agent" and ("def " in content or "class " in content):
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src_file = workspace_dir / "src_battery_health.py"
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# 提取 Python 代码
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if "```python" in content:
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code = content.split("```python")[1].split("```")[0]
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else:
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code = content
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with open(src_file, 'w', encoding='utf-8') as f:
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f.write(f'"""\n电池健康状态计算模块\n生成时间：{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n"""\n\n')
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f.write(code)
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files_saved.append(str(src_file))
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# Orchestrator 生成最终报告
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if agent_key == "Orchestrator" and ("完成" in content and "SDLC" in content):
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report_file = workspace_dir / "FINAL_REPORT.md"
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with open(report_file, 'w', encoding='utf-8') as f:
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f.write(f"# SDLC 项目最终报告\n\n")
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f.write(f"**生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
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f.write("## 项目概述\n\n")
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f.write("基于 AutoGen 多智能体系统完成的软件交付项目。\n\n")
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f.write("## 生成文件列表\n\n")
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for i, saved_file in enumerate(files_saved, 1):
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f.write(f"{i}. {saved_file}\n")
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f.write(f"\n## 项目总结\n\n{content}\n")
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files_saved.append(str(report_file))
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return files_saved
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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",
|
||
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"
|
||
)
|
||
|
||
st.divider()
|
||
|
||
# 显示生成的文件
|
||
st.subheader("📁 生成的文件")
|
||
workspace_dir = Path("workspace")
|
||
if workspace_dir.exists():
|
||
files = list(workspace_dir.glob("*"))
|
||
if files:
|
||
for file in files:
|
||
if file.is_file():
|
||
with st.expander(f"📄 {file.name}"):
|
||
try:
|
||
content = file.read_text(encoding='utf-8')
|
||
st.code(content[:500] + ("..." if len(content) > 500 else ""))
|
||
st.download_button(
|
||
label="⬇️ 下载",
|
||
data=content,
|
||
file_name=file.name,
|
||
mime="text/plain",
|
||
key=f"download_{file.name}"
|
||
)
|
||
except Exception as e:
|
||
st.error(f"读取失败：{e}")
|
||
else:
|
||
st.info("工作目录为空，请先运行工作流")
|
||
else:
|
||
st.info("工作目录不存在")
|
||
|
||
# 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")
|
||
|
||
# 保存生成的文件
|
||
progress_placeholder.info("💾 正在保存生成的文件...")
|
||
saved_files = save_generated_files()
|
||
|
||
if saved_files:
|
||
progress_placeholder.success(f"✅ SDLC 工作流完成！已保存 {len(saved_files)} 个文件到 workspace/ 目录")
|
||
else:
|
||
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()
|