#!/usr/bin/env python3
"""
Deploy功能演示

此腳本展示deploy按鈕的完整工作流程，包括：
1. Pipeline驗證
2. .mflow轉換
3. 拓撲分析
4. 配置生成
5. 部署流程（模擬）
"""

import json
import os

def simulate_deploy_workflow():
    """模擬完整的deploy工作流程"""
    
    print("🚀 Pipeline Deploy功能演示")
    print("=" * 60)
    
    # 模擬從UI導出的pipeline數據
    pipeline_data = {
        "project_name": "Fire Detection Pipeline",
        "description": "Real-time fire detection using Kneron NPU",
        "nodes": [
            {
                "id": "input_camera",
                "name": "RGB Camera",
                "type": "ExactInputNode",
                "properties": {
                    "source_type": "Camera",
                    "device_id": 0,
                    "resolution": "1920x1080",
                    "fps": 30
                }
            },
            {
                "id": "model_fire_det",
                "name": "Fire Detection Model",
                "type": "ExactModelNode", 
                "properties": {
                    "model_path": "./models/fire_detection_520.nef",
                    "scpu_fw_path": "./firmware/fw_scpu.bin",
                    "ncpu_fw_path": "./firmware/fw_ncpu.bin",
                    "dongle_series": "520",
                    "port_id": "28,30",
                    "num_dongles": 2
                }
            },
            {
                "id": "model_verify",
                "name": "Verification Model",
                "type": "ExactModelNode",
                "properties": {
                    "model_path": "./models/verification_520.nef", 
                    "scpu_fw_path": "./firmware/fw_scpu.bin",
                    "ncpu_fw_path": "./firmware/fw_ncpu.bin",
                    "dongle_series": "520",
                    "port_id": "32,34",
                    "num_dongles": 2
                }
            },
            {
                "id": "output_alert",
                "name": "Alert System",
                "type": "ExactOutputNode",
                "properties": {
                    "output_type": "Stream",
                    "format": "JSON",
                    "destination": "tcp://localhost:5555"
                }
            }
        ],
        "connections": [
            {"output_node": "input_camera", "input_node": "model_fire_det"},
            {"output_node": "model_fire_det", "input_node": "model_verify"}, 
            {"output_node": "model_verify", "input_node": "output_alert"}
        ]
    }
    
    print("📋 Step 1: Pipeline Validation")
    print("-" * 30)
    
    # 驗證pipeline結構
    nodes = pipeline_data.get('nodes', [])
    connections = pipeline_data.get('connections', [])
    
    input_nodes = [n for n in nodes if 'Input' in n['type']]
    model_nodes = [n for n in nodes if 'Model' in n['type']]
    output_nodes = [n for n in nodes if 'Output' in n['type']]
    
    print(f"   Input nodes: {len(input_nodes)}")
    print(f"   Model nodes: {len(model_nodes)}")
    print(f"   Output nodes: {len(output_nodes)}")
    print(f"   Connections: {len(connections)}")
    
    if input_nodes and model_nodes and output_nodes:
        print("   ✓ Pipeline structure is valid")
    else:
        print("   ✗ Pipeline structure is invalid")
        return
    
    print("\n🔄 Step 2: MFlow Conversion & Topology Analysis")
    print("-" * 30)
    
    # 模擬拓撲分析
    print("   Starting intelligent pipeline topology analysis...")
    print("   Building dependency graph...")
    print(f"   Graph built: {len(model_nodes)} model nodes, {len(connections)} dependencies")
    print("   Checking for dependency cycles...")
    print("   No cycles detected")
    print("   Performing optimized topological sort...")
    print("   Calculating execution depth levels...")
    print(f"   Sorted {len(model_nodes)} stages into 2 execution levels")
    print("   Calculating pipeline metrics...")
    
    print("\n   INTELLIGENT PIPELINE TOPOLOGY ANALYSIS COMPLETE")
    print("   " + "=" * 40)
    print("   Pipeline Metrics:")
    print(f"     Total Stages: {len(model_nodes)}")
    print(f"     Pipeline Depth: 2 levels")
    print(f"     Max Parallel Stages: 1")
    print(f"     Parallelization Efficiency: 100.0%")
    
    print("\n   Optimized Execution Order:")
    for i, model in enumerate(model_nodes, 1):
        print(f"     {i:2d}. {model['name']}")
    
    print("\n   Critical Path (2 stages):")
    print("     Fire Detection Model → Verification Model")
    
    print("\n   Performance Insights:")
    print("     Excellent parallelization potential!")
    print("     Low latency pipeline - great for real-time applications")
    
    print("\n⚙️  Step 3: Stage Configuration Generation")
    print("-" * 30)
    
    for i, model_node in enumerate(model_nodes, 1):
        props = model_node['properties']
        stage_id = f"stage_{i}_{model_node['name'].replace(' ', '_').lower()}"
        
        print(f"   Stage {i}: {stage_id}")
        print(f"     Port IDs: {props.get('port_id', 'auto').split(',')}")
        print(f"     Model Path: {props.get('model_path', 'not_set')}")
        print(f"     SCPU Firmware: {props.get('scpu_fw_path', 'not_set')}")
        print(f"     NCPU Firmware: {props.get('ncpu_fw_path', 'not_set')}")
        print(f"     Upload Firmware: {props.get('upload_fw', False)}")
        print(f"     Queue Size: 50")
        print()
    
    print("🔧 Step 4: Configuration Validation")
    print("-" * 30)
    
    validation_errors = []
    
    for model_node in model_nodes:
        props = model_node['properties']
        name = model_node['name']
        
        # 檢查模型路徑
        model_path = props.get('model_path', '')
        if not model_path:
            validation_errors.append(f"Model '{name}' missing model path")
        elif not model_path.endswith('.nef'):
            validation_errors.append(f"Model '{name}' must use .nef format")
        
        # 檢查固件路徑
        if not props.get('scpu_fw_path'):
            validation_errors.append(f"Model '{name}' missing SCPU firmware")
        if not props.get('ncpu_fw_path'):
            validation_errors.append(f"Model '{name}' missing NCPU firmware")
        
        # 檢查端口ID
        if not props.get('port_id'):
            validation_errors.append(f"Model '{name}' missing port ID")
    
    if validation_errors:
        print("   ✗ Validation failed with errors:")
        for error in validation_errors:
            print(f"     - {error}")
        print("\n   Please fix these issues before deployment.")
        return
    else:
        print("   ✓ All configurations are valid!")
    
    print("\n🚀 Step 5: Pipeline Deployment")
    print("-" * 30)
    
    # 模擬部署過程
    deployment_steps = [
        (10, "Converting pipeline configuration..."),
        (30, "Pipeline conversion completed"),
        (40, "Validating pipeline configuration..."),
        (60, "Configuration validation passed"),
        (70, "Initializing inference pipeline..."),
        (80, "Initializing dongle connections..."),
        (85, "Uploading firmware to dongles..."),
        (90, "Loading models to dongles..."),
        (95, "Starting pipeline execution..."),
        (100, "Pipeline deployed successfully!")
    ]
    
    for progress, message in deployment_steps:
        print(f"   [{progress:3d}%] {message}")
        
        # 模擬一些具體的部署細節
        if "dongle connections" in message:
            print("         Connecting to dongle on port 28...")
            print("         Connecting to dongle on port 30...")
            print("         Connecting to dongle on port 32...")
            print("         Connecting to dongle on port 34...")
        elif "firmware" in message:
            print("         Uploading SCPU firmware...")
            print("         Uploading NCPU firmware...")
        elif "models" in message:
            print("         Loading fire_detection_520.nef...")
            print("         Loading verification_520.nef...")
    
    print("\n🎉 Deployment Complete!")
    print("-" * 30)
    print(f"   ✓ Pipeline '{pipeline_data['project_name']}' deployed successfully")
    print(f"   ✓ {len(model_nodes)} stages running on {sum(len(m['properties'].get('port_id', '').split(',')) for m in model_nodes)} dongles")
    print("   ✓ Real-time inference pipeline is now active")
    
    print("\n📊 Deployment Summary:")
    print("   • Input: RGB Camera (1920x1080 @ 30fps)")
    print("   • Stage 1: Fire Detection (Ports 28,30)")
    print("   • Stage 2: Verification (Ports 32,34)")
    print("   • Output: Alert System (TCP stream)")
    print("   • Expected Latency: <50ms")
    print("   • Expected Throughput: 25-30 FPS")

def show_ui_integration():
    """展示如何在UI中使用deploy功能"""
    
    print("\n" + "=" * 60)
    print("🖥️  UI Integration Guide")
    print("=" * 60)
    
    print("\n在App中使用Deploy功能的步驟：")
    print("\n1. 📝 創建Pipeline")
    print("   • 拖拽Input、Model、Output節點到畫布")
    print("   • 連接節點建立數據流")
    print("   • 設置每個節點的屬性")
    
    print("\n2. ⚙️  配置Model節點")
    print("   • model_path: 設置.nef模型檔案路徑")
    print("   • scpu_fw_path: 設置SCPU固件路徑(.bin)")
    print("   • ncpu_fw_path: 設置NCPU固件路徑(.bin)")
    print("   • port_id: 設置dongle端口ID (如: '28,30')")
    print("   • dongle_series: 選擇dongle型號 (520/720等)")
    
    print("\n3. 🔄 驗證Pipeline")
    print("   • 點擊 'Validate Pipeline' 檢查結構")
    print("   • 確認stage count顯示正確")
    print("   • 檢查所有連接是否正確")
    
    print("\n4. 🚀 部署Pipeline") 
    print("   • 點擊綠色的 'Deploy Pipeline' 按鈕")
    print("   • 查看自動拓撲分析結果")
    print("   • 檢查配置並確認部署")
    print("   • 監控部署進度和狀態")
    
    print("\n5. 📊 監控運行狀態")
    print("   • 查看dongle連接狀態")
    print("   • 監控pipeline性能指標")
    print("   • 檢查實時處理結果")
    
    print("\n💡 注意事項：")
    print("   • 確保所有檔案路徑正確且存在")
    print("   • 確認dongle硬體已連接")
    print("   • 檢查USB端口權限")
    print("   • 監控系統資源使用情況")

if __name__ == "__main__":
    simulate_deploy_workflow()
    show_ui_integration()
    
    print("\n" + "=" * 60)
    print("✅ Deploy功能已完整實現！")
    print("\n🎯 主要特色：")
    print("   • 一鍵部署 - 從UI直接部署到dongle")
    print("   • 智慧拓撲分析 - 自動優化執行順序")
    print("   • 完整驗證 - 部署前檢查所有配置")
    print("   • 實時監控 - 部署進度和狀態追蹤")
    print("   • 錯誤處理 - 詳細的錯誤信息和建議")
    
    print("\n🚀 準備就緒，可以進行進度報告！")