remote-sensing/api_server.py

"""
API Server for Land Classification Model Training
Cho phép chọn dữ liệu và cấu hình training qua giao diện web
"""

from fastapi import FastAPI, BackgroundTasks, HTTPException, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from fastapi.responses import HTMLResponse, FileResponse
from pydantic import BaseModel
from typing import Optional, List
import uvicorn
import joblib
import json
from datetime import datetime
from pathlib import Path
import sys
import numpy as np
import xarray as xr
import rasterio
from rasterio.transform import from_bounds
import asyncio
import hashlib
import traceback

# Import report generator
from report_generator import generate_training_report, generate_prediction_report

# Import planetary computer libraries (conditional)
try:
    from pystac_client import Client
    import planetary_computer
    import odc.stac
except ImportError:
    Client = None
    planetary_computer = None
    odc = None

app = FastAPI(title="Land Classification Training API", version="1.0.0")

# Enable CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Global training status``
training_status = {
    "is_training": False,
    "progress": "",
    "error": None,
    "result": None,
    "start_time": None,
    "end_time": None,
    "cancel_requested": False
}

# Global prediction status
prediction_status = {
    "is_predicting": False,
    "progress": "",
    "error": None,
    "result": None,
    "output_file": None,
    "start_time": None,
    "end_time": None
}

# Batch prediction queue
batch_queue = []
batch_results = []


class TrainingConfig(BaseModel):
    """Cấu hình training"""
    # Khu vực (bbox)
    min_lon: float = 105.6
    min_lat: float = 9.3
    max_lon: float = 106.2
    max_lat: float = 9.8
    
    # Thời gian
    start_date: str = "2023-03-01"
    end_date: str = "2023-05-31"
    
    # Dữ liệu
    max_scenes: int = 12
    cloud_cover: int = 30
    resolution: int = 20  # 10m hoặc 20m
    
    # Model parameters
    model_type: str = "xgboost"  # xgboost, random_forest, decision_tree, svm, cnn
    n_estimators: int = 100
    max_depth: int = 20
    learning_rate: float = 0.1
    use_gpu: bool = True
    
    # Train/test split
    test_size: float = 0.2  # Tỷ lệ dữ liệu dùng làm test (0-1)
    
    # Cache
    use_cache: bool = True  # Cache dataset để test nhanh hơn
    
    # Training data
    training_shapefile: str = "train/ST_training data_updated_1130points_new.shp"


class PredictionConfig(BaseModel):
    """Cấu hình dự đoán"""
    # Model to use
    model_filename: str
    
    # Khu vực (bbox)
    min_lon: float = 105.6
    min_lat: float = 9.3
    max_lon: float = 106.2
    max_lat: float = 9.8
    
    # Thời gian
    start_date: str = "2023-03-01"
    end_date: str = "2023-05-31"
    
    # Dữ liệu
    max_scenes: int = 12
    cloud_cover: int = 30
    resolution: int = 20


class TrainingStatus(BaseModel):
    """Trạng thái training"""
    is_training: bool
    progress: str
    error: Optional[str]
    result: Optional[dict]
    start_time: Optional[str]
    end_time: Optional[str]


class NDVIConfig(BaseModel):
    """Cấu hình tính NDVI time series"""
    bbox: List[float]  # [min_lon, min_lat, max_lon, max_lat]
    start_date: str
    end_date: str
    max_cloud_cover: int = 30
    resolution: int = 20


class ChangeDetectionWorkflowRequest(BaseModel):
    """Request for change detection workflow"""
    prediction_result: dict
    bbox: List[float]


class ComparePeriodsPredictionConfig(BaseModel):
    """Compare predictions between two time periods"""
    model_filename: str
    min_lon: float
    min_lat: float
    max_lon: float
    max_lat: float
    current_period: dict  # {start_date, end_date}
    prediction_period: dict  # {start_date, end_date}
    max_scenes: int = 12
    cloud_cover: int = 30
    resolution: int = 20
    export_ndvi: bool = True
    export_classification: bool = True


class PredictionWithNDVIConfig(BaseModel):
    """Cấu hình predict kết hợp land classification và NDVI"""
    model_filename: str
    min_lon: float
    min_lat: float
    max_lon: float
    max_lat: float
    start_date: str
    end_date: str
    max_scenes: int = 12
    cloud_cover: int = 30
    resolution: int = 20
    export_ndvi: bool = True  # Export NDVI raster
    export_classification: bool = True  # Export classification raster


# Serve change detection interface page (moved here after app is defined)
@app.get("/change-detection", response_class=HTMLResponse)
async def change_detection_page():
    html_file = Path(__file__).parent / "change_detection_interface.html"
    if html_file.exists():
        return FileResponse(html_file)
    else:
        return HTMLResponse("<h2>Change Detection Interface not found.</h2>")


@app.get("/", response_class=HTMLResponse)
async def root():
    """Serve main index page with tabs"""
    html_file = Path(__file__).parent / "index.html"
    if html_file.exists():
        return FileResponse(html_file)
    else:
        return HTMLResponse("""
        <html>
            <head><title>Land Classification System</title></head>
            <body>
                <h1>Land Classification System</h1>
                <p>API Documentation: <a href="/docs">/docs</a></p>
                <p>Training: <a href="/training">/training</a></p>
                <p>Prediction: <a href="/prediction">/prediction</a></p>
                <p>Dashboard: <a href="/dashboard">/dashboard</a></p>
            </body>
        </html>
        """)


@app.get("/training", response_class=HTMLResponse)
async def training_page():
    """Serve training interface"""
    html_file = Path(__file__).parent / "training_interface.html"
    if html_file.exists():
        return FileResponse(html_file)
    else:
        raise HTTPException(status_code=404, detail="Training interface không tồn tại")


@app.get("/prediction", response_class=HTMLResponse)
async def prediction_page():
    """Serve prediction interface"""
    html_file = Path(__file__).parent / "prediction_interface.html"
    if html_file.exists():
        return FileResponse(html_file)
    else:
        raise HTTPException(status_code=404, detail="Prediction interface không tồn tại")


@app.get("/dashboard", response_class=HTMLResponse)
async def dashboard():
    """Serve dashboard visualization"""
    html_file = Path(__file__).parent / "dashboard.html"
    if html_file.exists():
        return FileResponse(html_file)
    else:
        raise HTTPException(status_code=404, detail="Dashboard không tồn tại")


@app.get("/batch", response_class=HTMLResponse)
async def batch_page():
    """Serve batch processing interface"""
    html_file = Path(__file__).parent / "batch_interface.html"
    if html_file.exists():
        return FileResponse(html_file)
    else:
        raise HTTPException(status_code=404, detail="Batch interface không tồn tại")


@app.get("/ndvi", response_class=HTMLResponse)
async def ndvi_page():
    """Serve NDVI time series interface"""
    html_file = Path(__file__).parent / "ndvi_interface.html"
    if html_file.exists():
        return FileResponse(html_file)
    else:
        raise HTTPException(status_code=404, detail="NDVI interface không tồn tại")


@app.get("/reports", response_class=HTMLResponse)
async def reports_page():
    """Serve reports management interface"""
    html_file = Path(__file__).parent / "reports_interface.html"
    if html_file.exists():
        return FileResponse(html_file)
    else:
        raise HTTPException(status_code=404, detail="Reports interface không tồn tại")


@app.get("/api/config/presets")
async def get_presets():
    """Lấy các preset cấu hình sẵn"""
    return {
        "presets": [
            {
                "name": "PC - Nhỏ (3 tháng, 20m, 12 scenes)",
                "config": {
                    "min_lon": 105.6, "min_lat": 9.3, "max_lon": 106.2, "max_lat": 9.8,
                    "start_date": "2023-03-01", "end_date": "2023-05-31",
                    "max_scenes": 12, "cloud_cover": 30, "resolution": 20,
                    "test_size": 0.2
                }
            },
            {
                "name": "Server - Trung bình (6 tháng, 10m, 30 scenes)",
                "config": {
                    "min_lon": 105.5, "min_lat": 9.2, "max_lon": 106.4, "max_lat": 10.0,
                    "start_date": "2023-01-01", "end_date": "2023-06-30",
                    "max_scenes": 30, "cloud_cover": 30, "resolution": 10,
                    "test_size": 0.2
                }
            },
            {
                "name": "Full - Lớn (1 năm, 10m, 60 scenes)",
                "config": {
                    "min_lon": 105.5, "min_lat": 9.2, "max_lon": 106.4, "max_lat": 10.0,
                    "start_date": "2022-09-01", "end_date": "2023-10-01",
                    "max_scenes": 60, "cloud_cover": 50, "resolution": 10,
                    "test_size": 0.2
                }
            }
        ]
    }


@app.get("/api/training/status", response_model=TrainingStatus)
async def get_training_status():
    """Kiểm tra trạng thái training"""
    return training_status


@app.post("/api/training/start")
async def start_training(config: TrainingConfig, background_tasks: BackgroundTasks):
    """Bắt đầu training với config đã chọn"""
    global training_status
    
    if training_status["is_training"]:
        raise HTTPException(status_code=400, detail="Training đang chạy, vui lòng đợi")
    
    # Reset status
    training_status = {
        "is_training": True,
        "progress": "Đang khởi tạo...",
        "error": None,
        "result": None,
        "start_time": datetime.now().isoformat(),
        "end_time": None
    }
    
    # Run training in background
    background_tasks.add_task(run_training, config)
    
    return {"message": "Training đã bắt đầu", "status": training_status}


@app.post("/api/training/stop")
async def stop_training():
    """Dừng training (nếu đang chạy)"""
    global training_status
    
    if not training_status["is_training"]:
        return {"message": "Không có training nào đang chạy"}
    
    # Set cancel flag - the training will check this and stop
    training_status["cancel_requested"] = True
    training_status["progress"] = "Đang hủy training..."
    
    return {"message": "Đang dừng training..."}


@app.post("/api/cache/clear")
async def clear_cache():
    """Xóa cache dataset"""
    import shutil
    cache_dir = Path("dataset_cache")
    
    if not cache_dir.exists():
        return {"message": "Không có cache để xóa", "deleted": 0}
    
    # Count files
    cache_files = list(cache_dir.glob("*.joblib"))
    count = len(cache_files)
    
    # Delete all cache files
    for cache_file in cache_files:
        try:
            cache_file.unlink()
        except:
            pass
    
    return {"message": f"Đã xóa {count} file cache", "deleted": count}


@app.get("/api/cache/info")
async def get_cache_info():
    """Lấy thông tin về cache với metadata đầy đủ, tự động xóa cache cũ có lazy data"""
    cache_dir = Path("dataset_cache")
    
    if not cache_dir.exists():
        return {"exists": False, "files": [], "total_size_mb": 0}
    
    cache_files = []
    total_size = 0
    deleted_count = 0
    
    for cache_file in cache_dir.glob("*.joblib"):
        # Skip if file doesn't exist (race condition)
        if not cache_file.exists():
            continue
            
        size = cache_file.stat().st_size
        
        # Try to load metadata from cache and check if it's valid
        metadata = {}
        is_valid = True
        try:
            cached_data = joblib.load(cache_file)
            
            # Check if cache contains lazy data (will cause 403 errors)
            if isinstance(cached_data, dict) and "s2_data" in cached_data:
                s2_data_temp = cached_data["s2_data"]
                is_lazy = False
                try:
                    is_lazy = any(hasattr(s2_data_temp[var].data, 'chunks') for var in s2_data_temp.data_vars)
                except:
                    pass
                
                if is_lazy:
                    print(f"[CLEANUP] Deleting cache with lazy data: {cache_file.name}")
                    cache_file.unlink()
                    deleted_count += 1
                    is_valid = False
            
            if is_valid and isinstance(cached_data, dict):
                metadata = {
                    "bbox": cached_data.get("bbox", []),
                    "time_range": cached_data.get("time_range", ""),
                    "resolution": cached_data.get("resolution", 20),
                    "n_samples": len(cached_data.get("features", [])),
                    "created": cached_data.get("timestamp", "")
                }
                # Parse time_range to get start/end dates
                if metadata["time_range"]:
                    time_parts = metadata["time_range"].split("/")
                    if len(time_parts) == 2:
                        metadata["start_date"] = time_parts[0]
                        metadata["end_date"] = time_parts[1]
                # Parse bbox to get min/max lon/lat
                if metadata["bbox"] and len(metadata["bbox"]) == 4:
                    metadata["min_lon"] = metadata["bbox"][0]
                    metadata["min_lat"] = metadata["bbox"][1]
                    metadata["max_lon"] = metadata["bbox"][2]
                    metadata["max_lat"] = metadata["bbox"][3]
        except Exception as e:
            print(f"[CLEANUP] Error loading cache {cache_file.name}: {e}. Deleting...")
            try:
                cache_file.unlink()
                deleted_count += 1
                is_valid = False
            except:
                pass
        
        # Only add valid cache files to the list
        if is_valid:
            total_size += size
            cache_files.append({
                "filename": cache_file.name,
                "size_mb": round(size / 1024 / 1024, 2),
                "modified": datetime.fromtimestamp(cache_file.stat().st_mtime).isoformat(),
                "metadata": metadata
            })
    
    # Sort by modified time (newest first)
    cache_files.sort(key=lambda x: x["modified"], reverse=True)
    
    if deleted_count > 0:
        print(f"[CLEANUP] Deleted {deleted_count} invalid cache files")
    
    return {
        "exists": True,
        "files": cache_files,
        "count": len(cache_files),
        "total_size_mb": round(total_size / 1024 / 1024, 2),
        "deleted_invalid": deleted_count
    }



@app.get("/api/models/list")
async def list_models():
    """Liệt kê các model đã train"""
    model_dir = Path("model_train")
    if not model_dir.exists():
        return {"models": []}

    models = []
    # List all .joblib model files (actual trained models)
    for model_file in model_dir.glob("*.joblib"):
        # Skip any file that contains '_info' in its name
        if '_info' in model_file.stem:
            continue
            
        info = {}
        # Try to find corresponding .json info file
        # Remove .joblib and try with _info.json
        base_name = model_file.stem  # e.g., "model_cnn_20251221_163841"
        info_file = model_dir / f"{base_name}_info.json"
        
        if info_file.exists():
            try:
                with open(info_file) as f:
                    info = json.load(f)
            except Exception as e:
                info = {"error": str(e)}
        
        size_mb = round(model_file.stat().st_size / 1024 / 1024, 2)
        created = datetime.fromtimestamp(model_file.stat().st_mtime).isoformat()
        
        models.append({
            "filename": model_file.name,
            "created": created,
            "size_mb": size_mb,
            "info": info
        })

    # Sort by creation time (newest first)
    models.sort(key=lambda x: x["created"], reverse=True)
    return {"models": models}


# ============ REPORTS API ============

@app.get("/api/reports/list")
async def list_reports():
    """Liệt kê các báo cáo đã tạo"""
    reports_dir = Path("reports")
    reports_dir.mkdir(exist_ok=True)
    
    reports = []
    for report_file in reports_dir.glob("*.html"):
        # Determine report type from filename
        if "training" in report_file.name:
            report_type = "training"
        elif "prediction" in report_file.name:
            report_type = "prediction"
        else:
            report_type = "unknown"
        
        report_info = {
            "filename": report_file.name,
            "type": report_type,
            "created": datetime.fromtimestamp(report_file.stat().st_mtime).isoformat(),
            "size_kb": round(report_file.stat().st_size / 1024, 2),
            "view_url": f"/api/reports/view/{report_file.name}",
            "download_url": f"/api/reports/download/{report_file.name}",
            "is_batch_job": False,
            "batch_metadata": None
        }
        
        # Check if this is a batch job report
        if report_type == "prediction":
            predictions_dir = Path("predictions")
            # Look for batch metadata JSON files that reference this report
            for json_file in predictions_dir.glob("batch_*.json"):
                try:
                    import json
                    with open(json_file, 'r') as f:
                        metadata = json.load(f)
                        if metadata.get("report_filename") == report_file.name or \
                           (metadata.get("batch_job_id") and report_file.name.endswith('.html')):
                            report_info["is_batch_job"] = True
                            report_info["batch_metadata"] = {
                                "batch_job_id": metadata.get("batch_job_id"),
                                "batch_name": metadata.get("batch_name"),
                                "batch_timestamp": metadata.get("batch_timestamp")
                            }
                            break
                except Exception as e:
                    pass
        
        reports.append(report_info)
    
    # Sort by creation time (newest first)
    reports.sort(key=lambda x: x["created"], reverse=True)
    return {"reports": reports, "count": len(reports)}


@app.get("/api/reports/view/{filename}", response_class=HTMLResponse)
async def view_report(filename: str):
    """Xem báo cáo HTML trực tiếp"""
    reports_dir = Path("reports")
    file_path = reports_dir / filename
    
    # Security check
    if ".." in filename or "/" in filename or "\\" in filename:
        raise HTTPException(status_code=400, detail="Invalid filename")
    
    if not file_path.exists():
        raise HTTPException(status_code=404, detail=f"Report không tồn tại: {filename}")
    
    with open(file_path, 'r', encoding='utf-8') as f:
        html_content = f.read()
    
    return HTMLResponse(content=html_content)


@app.get("/api/reports/download/{filename}")
async def download_report(filename: str):
    """Download báo cáo HTML"""
    reports_dir = Path("reports")
    file_path = reports_dir / filename
    
    # Security check
    if ".." in filename or "/" in filename or "\\" in filename:
        raise HTTPException(status_code=400, detail="Invalid filename")
    
    if not file_path.exists():
        raise HTTPException(status_code=404, detail=f"Report không tồn tại: {filename}")
    
    return FileResponse(
        path=str(file_path),
        filename=filename,
        media_type="text/html",
        headers={
            "Content-Disposition": f"attachment; filename={filename}"
        }
    )


@app.delete("/api/reports/delete/{filename}")
async def delete_report(filename: str):
    """Xóa một báo cáo"""
    reports_dir = Path("reports")
    file_path = reports_dir / filename
    
    # Security check
    if ".." in filename or "/" in filename or "\\" in filename:
        raise HTTPException(status_code=400, detail="Invalid filename")
    
    if not file_path.exists():
        raise HTTPException(status_code=404, detail=f"Report không tồn tại: {filename}")
    
    try:
        file_path.unlink()
        return {"message": f"Đã xóa báo cáo: {filename}", "success": True}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Không thể xóa: {str(e)}")


@app.post("/api/prediction/start")
async def start_prediction(config: PredictionConfig, background_tasks: BackgroundTasks):
    """Bắt đầu dự đoán"""
    global prediction_status
    
    if prediction_status["is_predicting"]:
        raise HTTPException(status_code=400, detail="Đang có dự đoán khác đang chạy")
    
    # Reset status
    prediction_status = {
        "is_predicting": True,
        "progress": "Đang khởi động...",
        "error": None,
        "result": None,
        "output_file": None,
        "start_time": datetime.now().isoformat(),
        "end_time": None
    }
    
    # Run prediction in background
    background_tasks.add_task(run_prediction, config)
    
    return {"message": "Đã bắt đầu dự đoán", "status": prediction_status}


@app.get("/api/prediction/status")
async def get_prediction_status():
    """Kiểm tra trạng thái dự đoán"""
    return prediction_status


async def run_training(config: TrainingConfig):
    """Chạy training process"""
    global training_status
    
    try:
        training_status["cancel_requested"] = False
        training_status["progress"] = "Đang import thư viện..."
        
        # Import training module
        from train_module import train_model
        
        training_status["progress"] = "Đang load dữ liệu Sentinel-2..."
        
        # Function to check if training should be cancelled
        def should_cancel():
            return training_status.get("cancel_requested", False)
        
        # Run training
        result = train_model(
            bbox=[config.min_lon, config.min_lat, config.max_lon, config.max_lat],
            time_range=f"{config.start_date}/{config.end_date}",
            max_scenes=config.max_scenes,
            cloud_cover=config.cloud_cover,
            resolution=config.resolution,
            training_shapefile=config.training_shapefile,
            model_type=config.model_type,
            n_estimators=config.n_estimators,
            max_depth=config.max_depth,
            learning_rate=config.learning_rate,
            use_gpu=config.use_gpu,
            use_cache=config.use_cache,
            test_size=config.test_size,
            status_callback=lambda msg: update_progress(msg),
            cancel_check=should_cancel
        )
        
        if training_status.get("cancel_requested", False):
            training_status["is_training"] = False
            training_status["progress"] = "Đã hủy training"
            training_status["error"] = "Training cancelled by user"
        else:
            training_status["is_training"] = False
            training_status["progress"] = "Hoàn thành! Đang tạo báo cáo..."
            training_status["result"] = result
            
            # Auto generate report
            if result.get("success", False):
                try:
                    report_path, _ = generate_training_report(result)
                    training_status["result"]["report_path"] = report_path
                    training_status["result"]["report_filename"] = Path(report_path).name
                    training_status["progress"] = "Hoàn thành! Báo cáo đã được tạo."
                    print(f"[REPORT] Generated: {report_path}")
                except Exception as e:
                    print(f"[REPORT ERROR] Failed to generate report: {e}")
                    training_status["progress"] = "Hoàn thành! (Không thể tạo báo cáo)"
        
        training_status["end_time"] = datetime.now().isoformat()
        
    except Exception as e:
        training_status["is_training"] = False
        training_status["error"] = str(e)
        training_status["progress"] = f"Lỗi: {str(e)}"
        training_status["end_time"] = datetime.now().isoformat()
        import traceback
        print(traceback.format_exc())


def update_progress(message: str):
    """Cập nhật progress message"""
    global training_status
    training_status["progress"] = message
    print(f"[PROGRESS] {message}")


def update_prediction_progress(message: str):
    """Cập nhật prediction progress message"""
    global prediction_status
    prediction_status["progress"] = message
    print(f"[PREDICTION PROGRESS] {message}")


async def run_prediction(config: PredictionConfig):
    """Chạy prediction process - Áp dụng phương pháp từ 02.predict_ODC.ipynb"""
    global prediction_status
    
    try:
        prediction_status["progress"] = "Đang import thư viện..."
        
        # Import required libraries
        import xarray as xr
        import numpy as np
        from datetime import datetime as dt
        import rioxarray
        import dask.array as da
        
        # Validate bbox
        if (config.min_lon < -180 or config.max_lon > 180 or 
            config.min_lat < -90 or config.max_lat > 90):
            raise ValueError(f"Bbox không hợp lệ: ({config.min_lon}, {config.min_lat}, {config.max_lon}, {config.max_lat}). "
                           f"Phải trong phạm vi (-180, -90, 180, 90)")
        
        prediction_status["progress"] = "Đang load model..."
        
        # Load model
        model_path = Path("model_train") / config.model_filename
        if not model_path.exists():
            raise FileNotFoundError(f"Model không tồn tại: {config.model_filename}")
        
        model_data = joblib.load(model_path)
        
        # Extract model from dict (models are saved as {'model': xgb_model, 'label_encoder': encoder})
        if isinstance(model_data, dict):
            model = model_data.get('model')
            label_encoder = model_data.get('label_encoder')
        else:
            model = model_data
            label_encoder = None
        
        # Check if it's a CNN model (PyTorch)
        is_cnn_model = hasattr(model, '__class__') and 'CNN' in model.__class__.__name__
        if is_cnn_model:
            prediction_status["progress"] = "Phát hiện PyTorch CNN model..."
            # Import PyTorch if needed
            try:
                import torch
            except ImportError:
                raise ImportError("PyTorch is required for CNN prediction. Install: pip install torch")
        
        prediction_status["progress"] = "Đang kiểm tra cache dữ liệu đầu vào..."
        import hashlib, os
        cache_dir = Path("dataset_cache")
        cache_dir.mkdir(exist_ok=True)
        # Tạo cache key từ bbox, time_range, max_scenes, cloud_cover, resolution
        cache_key = f"pred_{config.min_lon}_{config.min_lat}_{config.max_lon}_{config.max_lat}_{config.start_date}_{config.end_date}_{config.max_scenes}_{config.cloud_cover}_{config.resolution}"
        cache_hash = hashlib.md5(cache_key.encode()).hexdigest()
        cache_file = cache_dir / f"prediction_input_{cache_hash}.joblib"
        
        # Initialize common variables
        bbox = [config.min_lon, config.min_lat, config.max_lon, config.max_lat]
        time_range = f"{config.start_date}/{config.end_date}"

        # Try to load from cache first
        s2_data = None
        use_cache = False
        
        if cache_file.exists():
            prediction_status["progress"] = "Đang load dữ liệu từ cache..."
            try:
                cached = joblib.load(cache_file)
                s2_data_temp = cached["s2_data"]
                
                # Verify that cached data is not lazy (to avoid 403 errors from expired URLs)
                # If s2_data has chunks attribute, it's a dask array (lazy)
                is_lazy = False
                try:
                    is_lazy = any(hasattr(s2_data_temp[var].data, 'chunks') for var in s2_data_temp.data_vars)
                except:
                    pass
                    
                if is_lazy:
                    print(f"[WARNING] Cache contains lazy data with potentially expired URLs. Deleting cache...")
                    cache_file.unlink()
                    raise ValueError("Cache invalid - contains lazy data")
                
                # Cache is valid, use it
                s2_data = s2_data_temp
                s2_items = cached.get("s2_items", [])
                vh_monthly = cached.get("vh_monthly")
                vv_monthly = cached.get("vv_monthly")
                use_radar = cached.get("use_radar", False)
                use_cache = True
                print(f"[INFO] Loaded valid cache from {cache_file.name}")
                
            except Exception as e:
                print(f"[WARNING] Failed to load cache: {e}. Fetching fresh data...")
                s2_data = None
        
        # If cache not available or invalid, fetch from Microsoft
        if s2_data is None:
            prediction_status["progress"] = "Đang kết nối Microsoft Planetary Computer..."
            import pystac_client
            import planetary_computer
            from odc.stac import load
            catalog = pystac_client.Client.open(
                "https://planetarycomputer.microsoft.com/api/stac/v1",
                modifier=planetary_computer.sign_inplace,
            )
            # ============ BƯỚC 1: TẢI DỮ LIỆU SENTINEL-2 ============
            prediction_status["progress"] = "Đang tải dữ liệu Sentinel-2..."
            s2_search = catalog.search(
                collections=["sentinel-2-l2a"],
                bbox=bbox,
                datetime=time_range,
                query={"eo:cloud_cover": {"lt": config.cloud_cover}}
            )
            s2_items = list(s2_search.items())
            if not s2_items:
                raise ValueError("Không tìm thấy dữ liệu Sentinel-2 cho khu vực và thời gian này")
            s2_items = s2_items[:config.max_scenes]
            prediction_status["progress"] = f"Đang xử lý {len(s2_items)} scenes Sentinel-2..."
            s2_data_lazy = load(
                s2_items,
                bbox=bbox,
                chunks={"time": 1, "x": 2048, "y": 2048},
                groupby="solar_day",
                resolution=config.resolution
            )
            # Compute s2_data to load into memory (avoid lazy loading from expired URLs)
            prediction_status["progress"] = "Đang tải dữ liệu Sentinel-2 vào bộ nhớ..."
            s2_data = s2_data_lazy.compute()
            
            # ============ BƯỚC 4: TẢI DỮ LIỆU SENTINEL-1 (Radar)... ============
            prediction_status["progress"] = "Đang tải dữ liệu Sentinel-1 (Radar)..."
            s1_search = catalog.search(
                collections=["sentinel-1-rtc"],
                bbox=bbox,
                datetime=time_range,
            )
            s1_items = list(s1_search.items())
            if s1_items:
                s1_items = s1_items[:config.max_scenes]
                prediction_status["progress"] = f"Đang xử lý {len(s1_items)} scenes Sentinel-1..."
                s1_data = load(
                    s1_items,
                    bbox=bbox,
                    chunks={"time": 1, "x": 2048, "y": 2048},
                    groupby="sat:absolute_orbit",
                    resolution=config.resolution
                )
                if "vh" in s1_data and "vv" in s1_data:
                    vh = s1_data["vh"].astype('float32')
                    vv = s1_data["vv"].astype('float32')
                    vh_monthly = vh.resample(time="1ME").mean().compute()
                    vv_monthly = vv.resample(time="1ME").mean().compute()
                    use_radar = True
                else:
                    vh_monthly = None
                    vv_monthly = None
                    use_radar = False
            else:
                vh_monthly = None
                vv_monthly = None
                use_radar = False
            # Lưu cache
            joblib.dump({
                "s2_data": s2_data,
                "s2_items": s2_items,
                "vh_monthly": vh_monthly,
                "vv_monthly": vv_monthly,
                "use_radar": use_radar
            }, cache_file)
        
        # ============ BƯỚC 2: TÍNH NDVI VÀ XỬ LÝ MÂY ============
        prediction_status["progress"] = "Đang tính toán NDVI và xử lý mây..."
        
        # Calculate NDVI using Sentinel-2 band names (B08 = NIR, B04 = Red)
        nir = s2_data["B08"].astype('float32')
        red = s2_data["B04"].astype('float32')
        ndvi = (nir - red) / (nir + red + 1e-8)
        
        # Mask clouds using SCL band if available
        if "SCL" in s2_data:
            scl = s2_data["SCL"]
            # SCL values: 4=vegetation, 5=bare soil, 6=water - these are clear
            # 3=cloud shadow, 8=cloud medium, 9=cloud high, 10=cirrus - mask these
            cloud_mask = (scl == 3) | (scl == 8) | (scl == 9) | (scl == 10)
            ndvi = ndvi.where(~cloud_mask)
        
        # ============ BƯỚC 3: ĐIỀN GIÁ TRỊ NAN (FILL NAN) ============
        prediction_status["progress"] = "Đang điền giá trị bị che mây..."
        
        # Fill NaN using forward fill and backward fill
        ndvi_filled = ndvi.ffill(dim='time').bfill(dim='time')
        
        # Resample to monthly average
        prediction_status["progress"] = "Đang tính trung bình NDVI theo tháng..."
        ndvi_monthly = ndvi_filled.resample(time="1ME").mean()
        
        # Compute NDVI (convert from dask to numpy)
        ndvi_monthly = ndvi_monthly.compute()
        
        # ============ BƯỚC 4: TẢI DỮ LIỆU SENTINEL-1 (VH, VV) ============
        # Only load radar if not already in cache
        if not cache_file.exists() or (cache_file.exists() and not use_radar):
            prediction_status["progress"] = "Đang tải dữ liệu Sentinel-1 (Radar)..."
            try:
                # Initialize catalog if not already done
                if not cache_file.exists():
                    pass
                else:
                    import pystac_client
                    import planetary_computer
                    from odc.stac import load
                    catalog = pystac_client.Client.open(
                        "https://planetarycomputer.microsoft.com/api/stac/v1",
                        modifier=planetary_computer.sign_inplace,
                    )
                # Search Sentinel-1 data
                s1_search = catalog.search(
                    collections=["sentinel-1-rtc"],
                    bbox=bbox,
                    datetime=time_range,
                )
                s1_items = list(s1_search.items())
                if s1_items:
                    s1_items = s1_items[:config.max_scenes]
                    prediction_status["progress"] = f"Đang xử lý {len(s1_items)} scenes Sentinel-1..."
                    try:
                        s1_data = load(
                            s1_items,
                            bbox=bbox,
                            chunks={"time": 1, "x": 2048, "y": 2048},
                            groupby="sat:absolute_orbit",
                            resolution=config.resolution
                        )
                        if "vh" in s1_data and "vv" in s1_data:
                            vh = s1_data["vh"].astype('float32')
                            vv = s1_data["vv"].astype('float32')
                            prediction_status["progress"] = "Đang tính trung bình VH/VV theo tháng..."
                            try:
                                vh_monthly = vh.resample(time="1ME").mean().compute()
                                vv_monthly = vv.resample(time="1ME").mean().compute()
                                use_radar = True
                            except Exception as radar_exc:
                                print(f"[RADAR WARNING] Không thể tính radar monthly: {radar_exc}")
                                vh_monthly = None
                                vv_monthly = None
                                use_radar = False
                        else:
                            prediction_status["progress"] = "Không tìm thấy bands VH/VV, tiếp tục với NDVI..."
                            use_radar = False
                    except Exception as radar_exc:
                        print(f"[RADAR WARNING] Không thể tải dữ liệu Sentinel-1: {radar_exc}")
                        vh_monthly = None
                        vv_monthly = None
                        use_radar = False
                else:
                    prediction_status["progress"] = "Không có dữ liệu Sentinel-1, tiếp tục với NDVI..."
                    use_radar = False
            except Exception as radar_exc:
                print(f"[RADAR WARNING] Không thể truy cập Sentinel-1: {radar_exc}")
                prediction_status["progress"] = "Không thể truy cập Sentinel-1, tiếp tục với NDVI..."
                vh_monthly = None
                vv_monthly = None
                use_radar = False
        
        # ============ BƯỚC 5: CHUẨN BỊ FEATURES CHO DỰ ĐOÁN ============
        prediction_status["progress"] = "Đang chuẩn bị features cho dự đoán..."
        
        # Get shape information
        n_times_ndvi = len(ndvi_monthly.time)
        y_size = len(ndvi_monthly.y)
        x_size = len(ndvi_monthly.x)
        n_pixels = y_size * x_size
        
        # Prepare NDVI features (flatten each time step)
        ndvi_features = []
        for t in range(n_times_ndvi):
            ndvi_t = ndvi_monthly.isel(time=t).values.flatten()
            ndvi_features.append(ndvi_t)
        
        # Stack NDVI features
        features = np.column_stack(ndvi_features)
        
        # Add radar features if available
        if use_radar:
            n_times_vh = len(vh_monthly.time)
            n_times_vv = len(vv_monthly.time)
            
            # Add VH features
            for t in range(min(n_times_vh, n_times_ndvi)):
                vh_t = vh_monthly.isel(time=t).values.flatten()
                # Resize if needed
                if len(vh_t) != n_pixels:
                    vh_t = np.resize(vh_t, n_pixels)
                features = np.column_stack([features, vh_t])
            
            # Add VV features
            for t in range(min(n_times_vv, n_times_ndvi)):
                vv_t = vv_monthly.isel(time=t).values.flatten()
                # Resize if needed
                if len(vv_t) != n_pixels:
                    vv_t = np.resize(vv_t, n_pixels)
                features = np.column_stack([features, vv_t])
        
        # Handle NaN values in features✓ CNN PyTorch: Mạnh nhất với ảnh vệ tinh, tự học features, tương thích GPU tốt, cần pip install torch
        features = np.nan_to_num(features, nan=0.0)
        
        # ============ BƯỚC 6: DỰ ĐOÁN ============
        # Check model's expected feature count and adjust
        try:
            # Get expected number of features from model
            if is_cnn_model:
                # For PyTorch CNN, get n_features from model
                expected_features = model.n_features
            elif hasattr(model, 'n_features_in_'):
                expected_features = model.n_features_in_
            elif hasattr(model, 'feature_names_in_'):
                expected_features = len(model.feature_names_in_)
            else:
                # Try to get from booster for XGBoost
                try:
                    expected_features = model.get_booster().num_features()
                except:
                    expected_features = features.shape[1]
            
            prediction_status["progress"] = f"Model cần {expected_features} features, đang có {features.shape[1]} features..."
            
            # Adjust features to match model
            if features.shape[1] > expected_features:
                # Trim to expected number (use only first N features - NDVI only)
                prediction_status["progress"] = f"Cắt bớt features từ {features.shape[1]} xuống {expected_features}..."
                features = features[:, :expected_features]
            elif features.shape[1] < expected_features:
                # Pad with zeros or repeat last features
                prediction_status["progress"] = f"Thêm features từ {features.shape[1]} lên {expected_features}..."
                n_missing = expected_features - features.shape[1]
                # Repeat last feature column to fill
                padding = np.tile(features[:, -1:], (1, n_missing))
                features = np.column_stack([features, padding])
        except Exception as e:
            prediction_status["progress"] = f"Không thể xác định số features của model, tiếp tục với {features.shape[1]} features..."
        
        prediction_status["progress"] = f"Đang dự đoán với {features.shape[1]} features..."
        
        # Make prediction
        if is_cnn_model:
            # PyTorch CNN prediction
            predictions = model.predict(features)
        else:
            predictions = model.predict(features)
        
        # Decode labels if label_encoder exists
        if label_encoder is not None:
            try:
                predictions = label_encoder.inverse_transform(predictions)
            except:
                pass  # Keep numeric predictions if inverse_transform fails
        
        # Reshape to original shape
        pred_shape = (y_size, x_size)
        predictions_2d = predictions.reshape(pred_shape)
        
        # ============ BƯỚC 7: TẠO OUTPUT VÀ LƯU KẾT QUẢ ============
        prediction_status["progress"] = "Đang tạo bản đồ phân loại..."
        
        # Create output xarray
        prediction_da = xr.DataArray(
            predictions_2d,
            coords={
                "y": ndvi_monthly.y,
                "x": ndvi_monthly.x
            },
            dims=["y", "x"],
            name="classification"
        )
        
        # Save output
        output_dir = Path("predictions")
        output_dir.mkdir(exist_ok=True)
        
        timestamp = dt.now().strftime("%Y%m%d_%H%M%S")
        output_file = output_dir / f"prediction_{timestamp}.tif"
        
        prediction_status["progress"] = "Đang lưu kết quả GeoTIFF..."
        
        # Set CRS and save as GeoTIFF
        if hasattr(s2_data, 'rio') and s2_data.rio.crs is not None:
            prediction_da.rio.write_crs(s2_data.rio.crs, inplace=True)
        else:
            prediction_da.rio.write_crs("EPSG:4326", inplace=True)
        
        prediction_da.rio.to_raster(str(output_file), driver="GTiff")
        
        # Generate PNG preview for web display
        prediction_status["progress"] = "Đang tạo PNG preview..."
        png_file = output_dir / f"prediction_{timestamp}.png"
        try:
            import matplotlib
            matplotlib.use('Agg')  # Non-interactive backend
            import matplotlib.pyplot as plt
            
            # Create a figure with prediction result
            fig, ax = plt.subplots(figsize=(12, 10), dpi=150)
            
            # Plot prediction with colormap
            im = ax.imshow(predictions_2d, cmap='tab20', interpolation='nearest')
            ax.set_title(f'Prediction Result - {timestamp}', fontsize=14, fontweight='bold')
            ax.set_xlabel('X (pixels)', fontsize=10)
            ax.set_ylabel('Y (pixels)', fontsize=10)
            
            # Add colorbar
            cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
            cbar.set_label('Class', rotation=270, labelpad=15)
            
            # Add grid
            ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)
            
            # Save PNG
            plt.tight_layout()
            plt.savefig(str(png_file), dpi=150, bbox_inches='tight')
            plt.close(fig)
            
            print(f"[PNG PREVIEW] Created: {png_file}")
        except Exception as e:
            print(f"[PNG PREVIEW ERROR] Failed to create PNG: {e}")
            png_file = None
        
        # Get unique classes for result
        unique_classes = np.unique(predictions_2d)
        unique_classes = unique_classes[~np.isnan(unique_classes)].tolist()
        
        prediction_status["is_predicting"] = False
        prediction_status["progress"] = "Hoàn thành! Đang tạo báo cáo..."
        prediction_status["output_file"] = str(output_file)
        prediction_status["result"] = {
            "output_file": str(output_file),
            "png_file": str(png_file) if png_file else None,
            "shape": list(pred_shape),
            "unique_classes": unique_classes,
            "bbox": bbox,
            "time_range": time_range,
            "n_features": features.shape[1],
            "n_times_ndvi": n_times_ndvi,
            "used_radar": use_radar,
            "model_used": config.model_filename
        }
        
        # Auto generate prediction report
        try:
            report_path, _ = generate_prediction_report(prediction_status["result"])
            prediction_status["result"]["report_path"] = report_path
            prediction_status["result"]["report_filename"] = Path(report_path).name
            prediction_status["progress"] = "Hoàn thành! Báo cáo đã được tạo."
            print(f"[PREDICTION REPORT] Generated: {report_path}")
        except Exception as e:
            print(f"[PREDICTION REPORT ERROR] Failed to generate report: {e}")
            prediction_status["progress"] = "Hoàn thành! (Không thể tạo báo cáo)"
        
        prediction_status["end_time"] = dt.now().isoformat()
        
    except Exception as e:
        prediction_status["is_predicting"] = False
        prediction_status["error"] = str(e)
        prediction_status["progress"] = f"Lỗi: {str(e)}"
        prediction_status["end_time"] = dt.now().isoformat()
        import traceback
        print(traceback.format_exc())


@app.get("/api/predictions/list")
async def list_predictions():
    """Lấy danh sách các file prediction đã tạo"""
    predictions_dir = Path("predictions")
    predictions_dir.mkdir(exist_ok=True)
    
    predictions = []
    for pred_file in predictions_dir.glob("*.tif"):
        pred_info = {
            "filename": pred_file.name,
            "created": datetime.fromtimestamp(pred_file.stat().st_mtime).isoformat(),
            "size_mb": round(pred_file.stat().st_size / 1024 / 1024, 2),
            "download_url": f"/api/predictions/download/{pred_file.name}",
            "is_batch_job": pred_file.name.startswith("batch_"),
            "batch_metadata": None
        }
        
        # Try to load batch metadata from JSON sidecar if exists
        json_file = pred_file.with_suffix('.json')
        if json_file.exists():
            try:
                import json
                with open(json_file, 'r') as f:
                    metadata = json.load(f)
                    pred_info["batch_metadata"] = {
                        "batch_job_id": metadata.get("batch_job_id"),
                        "batch_name": metadata.get("batch_name"),
                        "batch_timestamp": metadata.get("batch_timestamp")
                    }
            except Exception as e:
                print(f"[METADATA ERROR] Failed to load {json_file}: {e}")
        
        # Check PNG preview
        png_file = pred_file.with_suffix('.png')
        pred_info["has_preview"] = png_file.exists()
        if png_file.exists():
            pred_info["preview_url"] = f"/api/predictions/preview/{png_file.name}"
        
        predictions.append(pred_info)
    
    # Sort by creation time (newest first)
    predictions.sort(key=lambda x: x["created"], reverse=True)
    return {"predictions": predictions}


@app.get("/api/predictions/download/{filename}")
async def download_prediction(filename: str):
    """Download file prediction GeoTIFF"""
    predictions_dir = Path("predictions")
    file_path = predictions_dir / filename
    
    # Security check: ensure filename doesn't contain path traversal
    if ".." in filename or "/" in filename or "\\" in filename:
        raise HTTPException(status_code=400, detail="Invalid filename")
    
    if not file_path.exists():
        raise HTTPException(status_code=404, detail=f"File không tồn tại: {filename}")
    
    return FileResponse(
        path=str(file_path),
        filename=filename,
        media_type="image/tiff",
        headers={
            "Content-Disposition": f"attachment; filename={filename}"
        }
    )


@app.get("/api/predictions/preview/{filename}")
async def preview_prediction_png(filename: str):
    """Preview PNG image of prediction"""
    predictions_dir = Path("predictions")
    file_path = predictions_dir / filename
    
    # Security check
    if ".." in filename or "/" in filename or "\\" in filename:
        raise HTTPException(status_code=400, detail="Invalid filename")
    
    if not file_path.exists():
        raise HTTPException(status_code=404, detail=f"PNG preview không tồn tại: {filename}")
    
    return FileResponse(
        path=str(file_path),
        media_type="image/png"
    )


@app.get("/api/predictions/preview/{filename}")
async def preview_prediction_png(filename: str):
    """Preview PNG image of prediction"""
    predictions_dir = Path("predictions")
    file_path = predictions_dir / filename
    
    # Security check
    if ".." in filename or "/" in filename or "\\" in filename:
        raise HTTPException(status_code=400, detail="Invalid filename")
    
    if not file_path.exists():
        raise HTTPException(status_code=404, detail=f"PNG preview không tồn tại: {filename}")
    
    return FileResponse(
        path=str(file_path),
        media_type="image/png"
    )


# ============ DASHBOARD & VISUALIZATION API ============

@app.get("/api/dashboard/accuracy-trends")
async def get_accuracy_trends():
    """Lấy dữ liệu accuracy trends của các models theo thời gian"""
    model_dir = Path("model_train")
    if not model_dir.exists():
        return {"trends": [], "models": []}
    
    trends_data = []
    for info_file in sorted(model_dir.glob("*.json")):
        try:
            with open(info_file) as f:
                info = json.load(f)
            
            # Extract relevant data
            if "training_date" in info and "metrics" in info:
                trends_data.append({
                    "date": info["training_date"],
                    "model_name": info.get("model_type", "unknown"),
                    "accuracy": info["metrics"].get("accuracy", 0),
                    "f1_score": info["metrics"].get("macro avg", {}).get("f1-score", 0),
                    "precision": info["metrics"].get("macro avg", {}).get("precision", 0),
                    "recall": info["metrics"].get("macro avg", {}).get("recall", 0),
                    "filename": info_file.stem + ".joblib"
                })
        except Exception as e:
            print(f"Error loading {info_file}: {e}")
            continue
    
    # Sort by date
    trends_data.sort(key=lambda x: x["date"])
    
    return {
        "trends": trends_data,
        "models": list(set(d["model_name"] for d in trends_data))
    }


@app.get("/api/dashboard/statistics")
async def get_statistics():
    """Lấy thống kê tổng quan: số models, predictions, reports"""
    model_dir = Path("model_train")
    predictions_dir = Path("predictions")
    reports_dir = Path("reports")
    
    # Count items
    n_models = len(list(model_dir.glob("*.joblib"))) if model_dir.exists() else 0
    n_predictions = len(list(predictions_dir.glob("*.tif"))) if predictions_dir.exists() else 0
    n_reports = len(list(reports_dir.glob("*.html"))) if reports_dir.exists() else 0
    
    # Get latest model info
    latest_model = None
    if model_dir.exists():
        model_files = sorted(model_dir.glob("*.json"), key=lambda x: x.stat().st_mtime, reverse=True)
        if model_files:
            try:
                with open(model_files[0]) as f:
                    latest_model = json.load(f)
            except:
                pass
    
    # Get latest prediction
    latest_prediction = None
    if predictions_dir.exists():
        pred_files = sorted(predictions_dir.glob("*.tif"), key=lambda x: x.stat().st_mtime, reverse=True)
        if pred_files:
            latest_prediction = {
                "filename": pred_files[0].name,
                "created": datetime.fromtimestamp(pred_files[0].stat().st_mtime).isoformat(),
                "size_mb": round(pred_files[0].stat().st_size / 1024 / 1024, 2)
            }
    
    return {
        "models": {
            "total": n_models,
            "latest": latest_model
        },
        "predictions": {
            "total": n_predictions,
            "latest": latest_prediction
        },
        "reports": {
            "total": n_reports
        },
        "training_status": training_status,
        "prediction_status": prediction_status
    }


@app.get("/api/dashboard/class-distribution/{model_filename}")
async def get_class_distribution(model_filename: str):
    """Lấy phân bố các lớp từ model info"""
    # Convert model filename to info filename
    # e.g., model_cnn_20251221_163841.joblib -> model_cnn_20251221_163841_info.json
    base_name = model_filename.replace(".joblib", "")
    info_file = Path("model_train") / f"{base_name}_info.json"
    
    if not info_file.exists():
        raise HTTPException(status_code=404, detail="Model info không tồn tại")
    
    with open(info_file) as f:
        info = json.load(f)
    
    # Extract class distribution from classification report
    class_dist = {}
    if "classification_report" in info:
        for class_name, metrics in info["classification_report"].items():
            if isinstance(metrics, dict) and "support" in metrics:
                class_dist[class_name] = int(metrics["support"])
    
    return {
        "model": model_filename,
        "class_distribution": class_dist,
        "total_samples": sum(class_dist.values()) if class_dist else 0
    }


# ============ BATCH PROCESSING API ============

class BatchPredictionItem(BaseModel):
    """Một item trong batch prediction"""
    name: str
    min_lon: float
    min_lat: float
    max_lon: float
    max_lat: float
    start_date: str = "2023-03-01"
    end_date: str = "2023-05-31"
    max_scenes: int = 12
    cloud_cover: int = 30
    resolution: int = 20


class BatchPredictionConfig(BaseModel):
    """Cấu hình cho batch prediction"""
    model_filename: str
    items: List[BatchPredictionItem]
    auto_retry: bool = True
    max_retries: int = 3


@app.post("/api/batch/start")
async def start_batch_prediction(config: BatchPredictionConfig, background_tasks: BackgroundTasks):
    """Bắt đầu batch prediction"""
    global batch_queue, batch_results
    
    # Create batch jobs
    batch_id = datetime.now().strftime("%Y%m%d_%H%M%S")
    
    for idx, item in enumerate(config.items):
        job = {
            "batch_id": batch_id,
            "job_id": f"{batch_id}_{idx}",
            "name": item.name,
            "status": "queued",
            "progress": 0,
            "error": None,
            "result": None,
            "retries": 0,
            "max_retries": config.max_retries if config.auto_retry else 0,
            "config": {
                "model_filename": config.model_filename,
                "min_lon": item.min_lon,
                "min_lat": item.min_lat,
                "max_lon": item.max_lon,
                "max_lat": item.max_lat,
                "start_date": item.start_date,
                "end_date": item.end_date,
                "max_scenes": item.max_scenes,
                "cloud_cover": item.cloud_cover,
                "resolution": item.resolution
            },
            "created_at": datetime.now().isoformat()
        }
        batch_queue.append(job)
    
    # Start processing in background
    background_tasks.add_task(process_batch_queue)
    
    return {
        "message": f"Đã tạo {len(config.items)} batch jobs",
        "batch_id": batch_id,
        "total_jobs": len(config.items)
    }


@app.get("/api/batch/status")
async def get_batch_status():
    """Lấy trạng thái của batch queue"""
    global batch_queue, batch_results
    
    queued = [j for j in batch_queue if j["status"] == "queued"]
    running = [j for j in batch_queue if j["status"] == "running"]
    completed = [j for j in batch_results if j["status"] == "completed"]
    failed = [j for j in batch_results if j["status"] == "failed"]
    
    return {
        "queue": {
            "queued": len(queued),
            "running": len(running),
            "completed": len(completed),
            "failed": len(failed),
            "total": len(batch_queue) + len(batch_results)
        },
        "jobs": {
            "queued": queued[:5],  # Show first 5
            "running": running,
            "recent_completed": completed[:10],  # Show last 10
            "recent_failed": failed[:10]
        }
    }


@app.get("/api/batch/results/{batch_id}")
async def get_batch_results(batch_id: str):
    """Lấy kết quả của một batch"""
    global batch_results
    
    results = [j for j in batch_results if j["batch_id"] == batch_id]
    
    if not results:
        # Check if still in queue
        queued = [j for j in batch_queue if j["batch_id"] == batch_id]
        if queued:
            return {
                "batch_id": batch_id,
                "status": "processing",
                "jobs": queued
            }
        else:
            raise HTTPException(status_code=404, detail="Batch không tồn tại")
    
    return {
        "batch_id": batch_id,
        "status": "completed",
        "jobs": results,
        "summary": {
            "total": len(results),
            "successful": len([j for j in results if j["status"] == "completed"]),
            "failed": len([j for j in results if j["status"] == "failed"])
        }
    }


@app.post("/api/batch/cancel/{batch_id}")
async def cancel_batch(batch_id: str):
    """Hủy một batch đang chạy"""
    global batch_queue
    
    # Remove from queue
    removed = 0
    batch_queue_copy = batch_queue.copy()
    for job in batch_queue_copy:
        if job["batch_id"] == batch_id and job["status"] == "queued":
            batch_queue.remove(job)
            removed += 1
    
    return {
        "message": f"Đã hủy {removed} jobs",
        "batch_id": batch_id
    }


async def process_batch_queue():
    """Process batch prediction queue"""
    global batch_queue, batch_results
    
    import asyncio
    
    while batch_queue:
        # Get next job
        job = None
        for j in batch_queue:
            if j["status"] == "queued":
                job = j
                break
        
        if not job:
            break
        
        # Mark as running
        job["status"] = "running"
        job["progress"] = 0
        job["started_at"] = datetime.now().isoformat()
        
        try:
            # Create PredictionConfig from job config
            pred_config = PredictionConfig(**job["config"])
            
            print(f"[BATCH] Processing job {job['job_id']}: {job['name']}")
            job["progress"] = 5
            
            # Run prediction synchronously (in the same thread to avoid conflicts)
            await asyncio.to_thread(run_batch_prediction, job, pred_config)
            
            # Check if prediction was successful
            if job.get("result") and not job.get("error"):
                job["status"] = "completed"
                job["progress"] = 100
                job["completed_at"] = datetime.now().isoformat()
                print(f"[BATCH] Job {job['job_id']} completed successfully")
            else:
                raise Exception(job.get("error", "Unknown error during prediction"))
            
        except Exception as e:
            job["error"] = str(e)
            
            # Retry logic
            if job["retries"] < job["max_retries"]:
                job["retries"] += 1
                job["status"] = "queued"  # Retry
                job["progress"] = 0
                print(f"[BATCH] Job {job['job_id']} ({job['name']}) failed, retrying ({job['retries']}/{job['max_retries']}): {e}")
                continue
            else:
                job["status"] = "failed"
                job["progress"] = 0
                job["completed_at"] = datetime.now().isoformat()
                print(f"[BATCH] Job {job['job_id']} ({job['name']}) failed permanently: {e}")
        
        # Move to results
        batch_queue.remove(job)
        batch_results.append(job)
        
        # Keep only last 100 results
        if len(batch_results) > 100:
            batch_results = batch_results[-100:]


def run_batch_prediction(job: dict, config: PredictionConfig):
    """Run prediction for a single batch job"""
    try:
        job["progress"] = 10
        
        # Import required libraries
        import xarray as xr
        import numpy as np
        from datetime import datetime as dt
        import rioxarray
        import dask.array as da
        
        job["progress"] = 15
        
        # Load model
        model_path = Path("model_train") / config.model_filename
        if not model_path.exists():
            raise FileNotFoundError(f"Model không tồn tại: {config.model_filename}")
        
        model_data = joblib.load(model_path)
        
        if isinstance(model_data, dict):
            model = model_data.get('model')
            label_encoder = model_data.get('label_encoder')
        else:
            model = model_data
            label_encoder = None
        
        job["progress"] = 20
        
        # Check if CNN model
        is_cnn_model = hasattr(model, '__class__') and 'CNN' in model.__class__.__name__
        
        # Load data from Microsoft Planetary Computer
        import pystac_client
        import planetary_computer
        from odc.stac import load
        
        catalog = pystac_client.Client.open(
            "https://planetarycomputer.microsoft.com/api/stac/v1",
            modifier=planetary_computer.sign_inplace,
        )
        
        bbox = [config.min_lon, config.min_lat, config.max_lon, config.max_lat]
        time_range = f"{config.start_date}/{config.end_date}"
        
        job["progress"] = 25
        
        # Search Sentinel-2
        s2_search = catalog.search(
            collections=["sentinel-2-l2a"],
            bbox=bbox,
            datetime=time_range,
            query={"eo:cloud_cover": {"lt": config.cloud_cover}}
        )
        
        s2_items = list(s2_search.items())
        if not s2_items:
            raise ValueError("Không tìm thấy dữ liệu Sentinel-2")
        
        s2_items = s2_items[:config.max_scenes]
        
        job["progress"] = 35
        
        # Load Sentinel-2 data
        s2_data = load(
            s2_items,
            bbox=bbox,
            chunks={"time": 1, "x": 2048, "y": 2048},
            groupby="solar_day",
            resolution=config.resolution
        )
        
        job["progress"] = 50
        
        # Calculate NDVI
        nir = s2_data["B08"].astype('float32')
        red = s2_data["B04"].astype('float32')
        ndvi = (nir - red) / (nir + red + 1e-8)
        
        # Mask clouds if SCL available
        if "SCL" in s2_data:
            scl = s2_data["SCL"]
            cloud_mask = (scl == 3) | (scl == 8) | (scl == 9) | (scl == 10)
            ndvi = ndvi.where(~cloud_mask)
        
        # Fill NaN and resample
        ndvi_filled = ndvi.ffill(dim='time').bfill(dim='time')
        ndvi_monthly = ndvi_filled.resample(time="1ME").mean().compute()
        
        job["progress"] = 70
        
        # Prepare features
        n_times_ndvi = len(ndvi_monthly.time)
        y_size = len(ndvi_monthly.y)
        x_size = len(ndvi_monthly.x)
        n_pixels = y_size * x_size
        
        ndvi_features = []
        for t in range(n_times_ndvi):
            ndvi_t = ndvi_monthly.isel(time=t).values.flatten()
            ndvi_features.append(ndvi_t)
        
        features = np.column_stack(ndvi_features)
        features = np.nan_to_num(features, nan=0.0)
        
        job["progress"] = 80
        
        # Adjust features to match model expectations
        try:
            if is_cnn_model:
                expected_features = model.n_features
            elif hasattr(model, 'n_features_in_'):
                expected_features = model.n_features_in_
            else:
                try:
                    expected_features = model.get_booster().num_features()
                except:
                    expected_features = features.shape[1]
            
            if features.shape[1] > expected_features:
                features = features[:, :expected_features]
            elif features.shape[1] < expected_features:
                n_missing = expected_features - features.shape[1]
                padding = np.tile(features[:, -1:], (1, n_missing))
                features = np.column_stack([features, padding])
        except:
            pass
        
        # Predict
        if is_cnn_model:
            predictions = model.predict(features)
        else:
            predictions = model.predict(features)
        
        # Decode labels
        if label_encoder is not None:
            try:
                predictions = label_encoder.inverse_transform(predictions)
            except:
                pass
        
        job["progress"] = 90
        
        # Reshape and create output
        pred_shape = (y_size, x_size)
        predictions_2d = predictions.reshape(pred_shape)
        
        prediction_da = xr.DataArray(
            predictions_2d,
            coords={"y": ndvi_monthly.y, "x": ndvi_monthly.x},
            dims=["y", "x"],
            name="classification"
        )
        
        # Save output
        output_dir = Path("predictions")
        output_dir.mkdir(exist_ok=True)
        
        output_file = output_dir / f"batch_{job['job_id']}_{job['name'].replace(' ', '_')}.tif"
        
        if hasattr(s2_data, 'rio') and s2_data.rio.crs is not None:
            prediction_da.rio.write_crs(s2_data.rio.crs, inplace=True)
        else:
            prediction_da.rio.write_crs("EPSG:4326", inplace=True)
        
        prediction_da.rio.to_raster(str(output_file), driver="GTiff")
        
        # Generate PNG preview
        png_file = output_dir / f"batch_{job['job_id']}_{job['name'].replace(' ', '_')}.png"
        try:
            import matplotlib
            matplotlib.use('Agg')
            import matplotlib.pyplot as plt
            
            fig, ax = plt.subplots(figsize=(12, 10), dpi=150)
            im = ax.imshow(predictions_2d, cmap='tab20', interpolation='nearest')
            ax.set_title(f"{job['name']} - Batch {job['job_id']}", fontsize=14, fontweight='bold')
            ax.set_xlabel('X (pixels)', fontsize=10)
            ax.set_ylabel('Y (pixels)', fontsize=10)
            
            cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
            cbar.set_label('Class', rotation=270, labelpad=15)
            ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5)
            
            plt.tight_layout()
            plt.savefig(str(png_file), dpi=150, bbox_inches='tight')
            plt.close(fig)
        except Exception as e:
            print(f"[BATCH PNG ERROR] {e}")
            png_file = None
        
        # Get unique classes
        unique_classes = np.unique(predictions_2d)
        unique_classes = unique_classes[~np.isnan(unique_classes)].tolist()
        
        # Store result in job with batch metadata
        job["result"] = {
            "output_file": str(output_file),
            "png_file": str(png_file) if png_file else None,
            "shape": list(pred_shape),
            "unique_classes": unique_classes,
            "bbox": bbox,
            "time_range": time_range,
            "n_features": features.shape[1],
            "n_times_ndvi": n_times_ndvi,
            "model_used": config.model_filename,
            "batch_job_id": job["job_id"],
            "batch_name": job["name"],
            "batch_timestamp": datetime.now().isoformat()
        }
        
        # Save batch metadata to JSON sidecar file for persistence
        metadata_file = output_file.with_suffix('.json')
        try:
            import json
            with open(metadata_file, 'w') as f:
                json.dump(job["result"], f, indent=2, default=str)
            print(f"[BATCH METADATA] Saved to {metadata_file}")
        except Exception as e:
            print(f"[BATCH METADATA ERROR] Failed to save metadata: {e}")
        
        # Auto generate prediction report for batch job
        try:
            from report_generator import generate_prediction_report
            report_path, _ = generate_prediction_report(job["result"])
            job["result"]["report_path"] = report_path
            job["result"]["report_filename"] = Path(report_path).name
            print(f"[BATCH REPORT] Generated prediction report: {report_path}")
        except Exception as e:
            print(f"[BATCH REPORT ERROR] Failed to generate report: {e}")
        
        job["progress"] = 100
        
    except Exception as e:
        job["error"] = str(e)
        import traceback
        print(f"[BATCH ERROR] Job {job['job_id']}: {traceback.format_exc()}")


# ============ CHANGE DETECTION API ============

def rasterize_ground_truth(shapefile_path, out_shape, bbox, class_column="class"):
    """Rasterize ground truth shapefile to match prediction raster shape."""
    try:
        import geopandas as gpd
        from rasterio import features as rio_features
        
        gdf = gpd.read_file(shapefile_path)
        minx, miny, maxx, maxy = bbox
        
        # Crop to bbox
        gdf = gdf.cx[minx:maxx, miny:maxy]
        
        if class_column not in gdf.columns:
            raise ValueError(f"Shapefile missing '{class_column}' column. Available: {list(gdf.columns)}")
        
        # Create transform for rasterization
        transform = from_bounds(minx, miny, maxx, maxy, out_shape[1], out_shape[0])
        
        # Prepare geometries and values for rasterization
        shapes = zip(gdf.geometry, gdf[class_column])
        
        # Rasterize
        gt_raster = rio_features.rasterize(
            shapes,
            out_shape=out_shape,
            fill=-1,
            transform=transform,
            dtype="int16"
        )
        
        return gt_raster
    except Exception as e:
        print(f"[RASTERIZE ERROR] {e}")
        raise


@app.post("/api/change-detection/predict")
async def change_detection_predict_workflow(
    model_filename: str,
    min_lon: float,
    min_lat: float,
    max_lon: float,
    max_lat: float,
    start_date: str,
    end_date: str,
    max_scenes: int = 12,
    cloud_cover: int = 30,
    resolution: int = 20
):
    """
    Complete workflow: Predict + Compare with Ground Truth
    1. Load Sentinel-2 data for bbox and date range
    2. Run prediction using trained model
    3. Rasterize ground truth from training shapefile
    4. Compare and generate change detection results
    """
    try:
        # --- STEP 1: LOAD MODEL ---
        model_path = Path("model_train") / model_filename
        if not model_path.exists():
            raise HTTPException(status_code=404, detail=f"Model not found: {model_filename}")
        
        model_data = joblib.load(model_path)
        
        if isinstance(model_data, dict):
            model = model_data.get('model')
            label_encoder = model_data.get('label_encoder')
        else:
            model = model_data
            label_encoder = None
        
        print(f"[CHANGE DETECTION] Loaded model: {model_filename}")
        
        # --- STEP 2: LOAD SENTINEL-2 DATA ---
        bbox = [min_lon, min_lat, max_lon, max_lat]
        time_range = f"{start_date}/{end_date}"
        
        catalog = Client.open(
            "https://planetarycomputer.microsoft.com/api/stac/v1",
            modifier=planetary_computer.sign_inplace
        )
        
        search = catalog.search(
            collections=["sentinel-2-l2a"],
            bbox=bbox,
            datetime=time_range,
            query={"eo:cloud_cover": {"lt": cloud_cover}}
        )
        
        items = list(search.items())[:max_scenes]
        print(f"[CHANGE DETECTION] Found {len(items)} Sentinel-2 scenes")
        
        if len(items) == 0:
            raise HTTPException(status_code=404, detail="No Sentinel-2 data found for the given area and date range")
        
        # Load data
        signed_items = [planetary_computer.sign(item) for item in items]
        data = odc.stac.load(
            signed_items,
            bbox=bbox,
            bands=["B02", "B03", "B04", "B08"],
            resolution=resolution,
            chunks={"x": 2048, "y": 2048}
        ).compute()
        
        # --- STEP 3: CALCULATE NDVI ---
        print("[CHANGE DETECTION] Calculating NDVI...")
        nir = data["B08"].astype('float32')
        red = data["B04"].astype('float32')
        ndvi = (nir - red) / (nir + red + 1e-8)
        
        # Handle clouds if SCL available
        if "SCL" in data:
            scl = data["SCL"]
            cloud_mask = (scl == 3) | (scl == 8) | (scl == 9) | (scl == 10)
            ndvi = ndvi.where(~cloud_mask)
        
        # --- STEP 4: PREPARE FEATURES ---
        ndvi_filled = ndvi.ffill(dim='time').bfill(dim='time')
        ndvi_mean = np.nanmean(ndvi_filled.values, axis=0)
        
        height, width = ndvi_mean.shape
        n_pixels = height * width
        
        # Prepare features
        features = ndvi_mean.flatten().reshape(-1, 1)
        valid_mask = ~np.isnan(features[:, 0])
        features_clean = features[valid_mask]
        
        # --- STEP 5: PREDICT ---
        print("[CHANGE DETECTION] Running prediction...")
        predictions = model.predict(features_clean)
        
        # Decode labels if needed
        if label_encoder is not None:
            try:
                predictions = label_encoder.inverse_transform(predictions)
            except:
                pass
        
        # Reshape to raster
        prediction_raster = np.full(n_pixels, -1, dtype=np.int16)
        prediction_raster[valid_mask] = predictions.astype(np.int16)
        prediction_raster = prediction_raster.reshape(height, width)
        
        # --- STEP 6: COMPARE WITH GROUND TRUTH ---
        print("[CHANGE DETECTION] Comparing with ground truth...")
        gt_shapefile = "train/ST_training data_updated_1130points_new.shp"
        gt_raster = rasterize_ground_truth(gt_shapefile, (height, width), bbox, class_column="class")
        
        # Calculate changes
        mask_valid = (gt_raster >= 0) & (prediction_raster >= 0)
        changes = gt_raster[mask_valid] != prediction_raster[mask_valid]
        n_total = np.count_nonzero(mask_valid)
        n_changed = np.count_nonzero(changes)
        
        # Create change matrix
        from collections import Counter
        change_pairs = list(zip(gt_raster[mask_valid][changes], prediction_raster[mask_valid][changes]))
        change_counter = Counter(change_pairs)
        change_matrix = {f"{int(gt)}->{int(pred)}": int(cnt) for (gt, pred), cnt in change_counter.items()}
        
        # Create change map
        change_map = np.full((height, width), -1, dtype=np.int8)
        change_map[mask_valid] = changes.astype(np.int8)
        
        # --- STEP 7: SAVE RESULTS ---
        output_dir = Path("predictions")
        output_dir.mkdir(exist_ok=True)
        
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        change_file = output_dir / f"change_map_{timestamp}.tif"
        
        transform = from_bounds(min_lon, min_lat, max_lon, max_lat, width, height)
        
        with rasterio.open(
            change_file, 'w',
            driver='GTiff',
            height=height,
            width=width,
            count=1,
            dtype=change_map.dtype,
            crs='EPSG:4326',
            transform=transform
        ) as dst:
            dst.write(change_map, 1)
        
        print(f"[CHANGE DETECTION] Saved change map to {change_file}")
        
        # --- RETURN RESULTS ---
        return {
            "success": True,
            "n_scenes": len(items),
            "ndvi_stats": {
                "mean": float(np.nanmean(ndvi_mean)),
                "min": float(np.nanmin(ndvi_mean)),
                "max": float(np.nanmax(ndvi_mean)),
                "std": float(np.nanstd(ndvi_mean))
            },
            "class_distribution": {
                int(cls): int(count) 
                for cls, count in zip(*np.unique(predictions, return_counts=True))
            },
            "change_detection": {
                "n_total_pixels": int(n_total),
                "n_changed_pixels": int(n_changed),
                "change_rate": float(n_changed) / n_total if n_total > 0 else 0.0,
                "change_matrix": change_matrix,
                "message": f"Detected {n_changed} changes out of {n_total} valid pixels ({(n_changed/n_total*100):.1f}%)" if n_total > 0 else "No valid pixels for comparison"
            },
            "change_map_file": str(change_file),
            "timestamp": timestamp
        }
        
    except HTTPException:
        raise
    except Exception as e:
        print(f"[CHANGE DETECTION ERROR] {e}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"Change detection workflow failed: {str(e)}")


@app.post("/api/change-detection/workflow")
async def change_detection_workflow(request: ChangeDetectionWorkflowRequest):
    """Workflow: compare prediction with ground truth training data."""
    from collections import Counter
    
    try:
        prediction_result = request.prediction_result
        bbox = request.bbox
        
        if not prediction_result or "output_files" not in prediction_result:
            raise ValueError("Invalid prediction result")
        
        # Get classification raster from prediction
        class_file = None
        for f in prediction_result.get("output_files", []):
            if f.get("type") == "classification":
                class_file = f.get("path")
                break
        
        if not class_file:
            raise ValueError("No classification raster in prediction result")
        
        # Load prediction raster
        with rasterio.open(class_file) as pred_ds:
            pred_arr = pred_ds.read(1)
            pred_crs = pred_ds.crs
            pred_transform = pred_ds.transform
        
        # Rasterize ground truth training data
        gt_shapefile = "train/ST_training data_updated_1130points_new.shp"
        gt_raster = rasterize_ground_truth(gt_shapefile, pred_arr.shape, bbox, class_column="class")
        
        # Calculate change detection
        mask_valid = (gt_raster >= 0) & (pred_arr >= 0) & ~np.isnan(gt_raster) & ~np.isnan(pred_arr)
        changes = gt_raster[mask_valid] != pred_arr[mask_valid]
        n_total = np.count_nonzero(mask_valid)
        n_changed = np.count_nonzero(changes)
        
        # Create change pairs matrix
        change_pairs = list(zip(gt_raster[mask_valid][changes], pred_arr[mask_valid][changes]))
        change_counter = Counter(change_pairs)
        change_matrix = {f"{int(gt)}->{int(pred)}": int(cnt) for (gt, pred), cnt in change_counter.items()}
        
        # Create change map
        change_map = np.full(pred_arr.shape, -1, dtype=np.int8)
        change_map[mask_valid] = changes.astype(np.int8)
        
        # Change rate
        change_rate = float(n_changed) / n_total if n_total > 0 else 0.0
        
        # Save change map
        change_dir = Path("predictions")
        change_dir.mkdir(exist_ok=True)
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        change_file = change_dir / f"change_map_{timestamp}.tif"
        
        with rasterio.open(
            change_file, 'w',
            driver='GTiff',
            height=change_map.shape[0],
            width=change_map.shape[1],
            count=1,
            dtype=change_map.dtype,
            crs=pred_crs,
            transform=pred_transform
        ) as dst:
            dst.write(change_map, 1)
        
        return {
            "success": True,
            "change_detection": {
                "n_total_pixels": int(n_total),
                "n_changed_pixels": int(n_changed),
                "change_rate": change_rate,
                "change_matrix": change_matrix,
                "message": f"Detected {n_changed} changes out of {n_total} valid pixels ({change_rate*100:.1f}%)"
            },
            "change_map_file": str(change_file),
            "timestamp": timestamp
        }
    
    except Exception as e:
        print(f"[CHANGE DETECTION WORKFLOW ERROR] {str(e)}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"Change detection workflow failed: {str(e)}")


@app.post("/api/change-detection/compare-periods")
async def compare_periods(request: ComparePeriodsPredictionConfig, background_tasks: BackgroundTasks):
    """Compare land use classification between two time periods."""
    from collections import Counter
    
    try:
        # Extract parameters
        model_filename = request.model_filename
        bbox = [request.min_lon, request.min_lat, request.max_lon, request.max_lat]
        
        current_start = request.current_period["start_date"]
        current_end = request.current_period["end_date"]
        pred_start = request.prediction_period["start_date"]
        pred_end = request.prediction_period["end_date"]
        
        max_scenes = request.max_scenes
        cloud_cover = request.cloud_cover
        resolution = request.resolution
        
        print(f"[COMPARE PERIODS] Current: {current_start} to {current_end} | Prediction: {pred_start} to {pred_end}")
        
        # Step 1: Predict on current period
        print(f"[COMPARE PERIODS] Step 1: Predicting current period...")
        current_result = await predict_with_ndvi(PredictionWithNDVIConfig(
            model_filename=model_filename,
            min_lon=request.min_lon,
            min_lat=request.min_lat,
            max_lon=request.max_lon,
            max_lat=request.max_lat,
            start_date=current_start,
            end_date=current_end,
            max_scenes=max_scenes,
            cloud_cover=cloud_cover,
            resolution=resolution,
            export_classification=True,
            export_ndvi=False
        ), background_tasks)
        
        # Get current classification raster
        current_class_file = None
        for f in current_result.get("output_files", []):
            if f.get("type") == "classification":
                current_class_file = f.get("path")
                break
        
        if not current_class_file:
            raise ValueError("No classification raster for current period")
        
        # Step 2: Predict on prediction period
        print(f"[COMPARE PERIODS] Step 2: Predicting future period...")
        pred_result = await predict_with_ndvi(PredictionWithNDVIConfig(
            model_filename=model_filename,
            min_lon=request.min_lon,
            min_lat=request.min_lat,
            max_lon=request.max_lon,
            max_lat=request.max_lat,
            start_date=pred_start,
            end_date=pred_end,
            max_scenes=max_scenes,
            cloud_cover=cloud_cover,
            resolution=resolution,
            export_classification=True,
            export_ndvi=False
        ), background_tasks)
        
        # Get prediction classification raster
        pred_class_file = None
        for f in pred_result.get("output_files", []):
            if f.get("type") == "classification":
                pred_class_file = f.get("path")
                break
        
        if not pred_class_file:
            raise ValueError("No classification raster for prediction period")
        
        # Step 3: Load both rasters
        print(f"[COMPARE PERIODS] Step 3: Comparing classifications...")
        with rasterio.open(current_class_file) as src:
            current_arr = src.read(1)
            crs = src.crs
            transform = src.transform
        
        with rasterio.open(pred_class_file) as src:
            pred_arr = src.read(1)
        
        # Ensure same shape
        if current_arr.shape != pred_arr.shape:
            raise ValueError(f"Shape mismatch: current {current_arr.shape} vs prediction {pred_arr.shape}")
        
        # Calculate changes
        mask_valid = ~np.isnan(current_arr) & ~np.isnan(pred_arr)
        changes = current_arr[mask_valid] != pred_arr[mask_valid]
        n_total = np.count_nonzero(mask_valid)
        n_changed = np.count_nonzero(changes)
        
        # Create change pairs
        change_pairs = list(zip(current_arr[mask_valid][changes], pred_arr[mask_valid][changes]))
        change_counter = Counter(change_pairs)
        change_matrix = {f"{int(curr)}->{int(pred)}": int(cnt) 
                        for (curr, pred), cnt in change_counter.items()}
        
        # Change rate
        change_rate = float(n_changed) / n_total if n_total > 0 else 0.0
        
        # Save change map
        change_dir = Path("predictions")
        change_dir.mkdir(exist_ok=True)
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        change_file = change_dir / f"change_map_{timestamp}.tif"
        
        change_map = np.zeros(current_arr.shape, dtype=np.int8)
        change_map[mask_valid] = changes.astype(np.int8)
        
        with rasterio.open(
            change_file, 'w',
            driver='GTiff',
            height=change_map.shape[0],
            width=change_map.shape[1],
            count=1,
            dtype=change_map.dtype,
            crs=crs,
            transform=transform
        ) as dst:
            dst.write(change_map, 1)
        
        # Extract class distributions
        current_classes = np.unique(current_arr[~np.isnan(current_arr)]).astype(int)
        current_dist = {int(c): int(np.count_nonzero(current_arr == c)) for c in current_classes}
        
        pred_classes = np.unique(pred_arr[~np.isnan(pred_arr)]).astype(int)
        pred_dist = {int(c): int(np.count_nonzero(pred_arr == c)) for c in pred_classes}
        
        return {
            "success": True,
            "current_classification": {
                "n_scenes": current_result.get("n_scenes"),
                "resolution": current_result.get("resolution"),
                "class_distribution": current_dist
            },
            "prediction_classification": {
                "n_scenes": pred_result.get("n_scenes"),
                "resolution": pred_result.get("resolution"),
                "class_distribution": pred_dist
            },
            "change_detection": {
                "n_total_pixels": int(n_total),
                "n_changed_pixels": int(n_changed),
                "change_rate": change_rate,
                "change_matrix": change_matrix,
                "message": f"Detected {n_changed} changes out of {n_total} valid pixels ({change_rate*100:.1f}%)"
            },
            "change_map_file": str(change_file),
            "timestamp": timestamp
        }
    
    except Exception as e:
        print(f"[COMPARE PERIODS ERROR] {str(e)}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"Period comparison failed: {str(e)}")


@app.post("/api/change-detection")
async def change_detection_api(
    prediction_file: UploadFile = File(...),
    gt_file: UploadFile = File(...)
):
    """Detect changes between two raster files (prediction and ground truth)."""
    import tempfile
    from collections import Counter
    
    try:
        # Save uploaded files temporarily
        pred_tmp = tempfile.NamedTemporaryFile(suffix='.tif', delete=False)
        gt_tmp = tempfile.NamedTemporaryFile(suffix='.tif', delete=False)
        
        try:
            # Write uploaded files to temp
            pred_content = await prediction_file.read()
            gt_content = await gt_file.read()
            
            pred_tmp.write(pred_content)
            gt_tmp.write(gt_content)
            pred_tmp.close()
            gt_tmp.close()
            
            # Read prediction raster
            with rasterio.open(pred_tmp.name) as pred_ds:
                pred_arr = pred_ds.read(1)
                pred_crs = pred_ds.crs
                pred_transform = pred_ds.transform
            
            # Read ground truth raster
            with rasterio.open(gt_tmp.name) as gt_ds:
                gt_arr = gt_ds.read(1)
            
            # Ensure same shape
            if pred_arr.shape != gt_arr.shape:
                raise ValueError(f"Raster shapes don't match: prediction {pred_arr.shape} vs ground truth {gt_arr.shape}")
            
            # Calculate change detection
            mask_valid = (gt_arr >= 0) & (pred_arr >= 0) & ~np.isnan(gt_arr) & ~np.isnan(pred_arr)
            changes = gt_arr[mask_valid] != pred_arr[mask_valid]
            n_total = np.count_nonzero(mask_valid)
            n_changed = np.count_nonzero(changes)
            
            # Create change pairs matrix
            change_pairs = list(zip(gt_arr[mask_valid][changes], pred_arr[mask_valid][changes]))
            change_counter = Counter(change_pairs)
            change_matrix = {f"{int(gt)}->{int(pred)}": int(cnt) for (gt, pred), cnt in change_counter.items()}
            
            # Create change map (0=same, 1=changed, -1=invalid)
            change_map = np.full(pred_arr.shape, -1, dtype=np.int8)
            change_map[mask_valid] = changes.astype(np.int8)
            
            # Change rate
            change_rate = float(n_changed) / n_total if n_total > 0 else 0.0
            
            # Save change map as GeoTIFF
            change_dir = Path("predictions")
            change_dir.mkdir(exist_ok=True)
            
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            change_file = change_dir / f"change_map_{timestamp}.tif"
            
            with rasterio.open(
                change_file, 'w',
                driver='GTiff',
                height=change_map.shape[0],
                width=change_map.shape[1],
                count=1,
                dtype=change_map.dtype,
                crs=pred_crs,
                transform=pred_transform
            ) as dst:
                dst.write(change_map, 1)
            
            # Return results
            return {
                "success": True,
                "change_detection": {
                    "n_total_pixels": int(n_total),
                    "n_changed_pixels": int(n_changed),
                    "change_rate": change_rate,
                    "change_matrix": change_matrix,
                    "message": f"Detected {n_changed} changes out of {n_total} valid pixels ({change_rate*100:.1f}%)"
                },
                "change_map_file": str(change_file),
                "timestamp": timestamp
            }
            
        finally:
            # Cleanup temp files
            try:
                Path(pred_tmp.name).unlink()
                Path(gt_tmp.name).unlink()
            except:
                pass
    
    except Exception as e:
        print(f"[CHANGE DETECTION ERROR] {str(e)}")
        raise HTTPException(status_code=500, detail=f"Change detection failed: {str(e)}")
async def change_detection_api(
    prediction_file: UploadFile = File(...),
    gt_file: UploadFile = File(...)
):
    """Detect changes between prediction raster and ground truth raster."""
    import tempfile
    from collections import Counter
    
    try:
        # Save uploaded files temporarily
        pred_tmp = tempfile.NamedTemporaryFile(suffix='.tif', delete=False)
        gt_tmp = tempfile.NamedTemporaryFile(suffix='.tif', delete=False)
        
        try:
            # Write uploaded files to temp
            pred_content = await prediction_file.read()
            gt_content = await gt_file.read()
            
            pred_tmp.write(pred_content)
            gt_tmp.write(gt_content)
            pred_tmp.close()
            gt_tmp.close()
            
            # Read prediction raster
            import rasterio
            with rasterio.open(pred_tmp.name) as pred_ds:
                pred_arr = pred_ds.read(1)
                pred_crs = pred_ds.crs
                pred_transform = pred_ds.transform
            
            # Read ground truth raster
            with rasterio.open(gt_tmp.name) as gt_ds:
                gt_arr = gt_ds.read(1)
            
            # Ensure same shape
            if pred_arr.shape != gt_arr.shape:
                raise ValueError(f"Raster shapes don't match: prediction {pred_arr.shape} vs ground truth {gt_arr.shape}")
            
            # Calculate change detection
            mask_valid = (gt_arr >= 0) & (pred_arr >= 0) & ~np.isnan(gt_arr) & ~np.isnan(pred_arr)
            changes = gt_arr[mask_valid] != pred_arr[mask_valid]
            n_total = np.count_nonzero(mask_valid)
            n_changed = np.count_nonzero(changes)
            
            # Create change pairs matrix
            change_pairs = list(zip(gt_arr[mask_valid][changes], pred_arr[mask_valid][changes]))
            change_counter = Counter(change_pairs)
            change_matrix = {f"{int(gt)}->{int(pred)}": int(cnt) for (gt, pred), cnt in change_counter.items()}
            
            # Create change map (0=same, 1=changed, -1=invalid)
            change_map = np.full(pred_arr.shape, -1, dtype=np.int8)
            change_map[mask_valid] = changes.astype(np.int8)
            
            # Change rate
            change_rate = float(n_changed) / n_total if n_total > 0 else 0.0
            
            # Save change map as GeoTIFF
            change_dir = Path("predictions")
            change_dir.mkdir(exist_ok=True)
            
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            change_file = change_dir / f"change_map_{timestamp}.tif"
            
            with rasterio.open(
                change_file, 'w',
                driver='GTiff',
                height=change_map.shape[0],
                width=change_map.shape[1],
                count=1,
                dtype=change_map.dtype,
                crs=pred_crs,
                transform=pred_transform
            ) as dst:
                dst.write(change_map, 1)
            
            # Return results
            return {
                "success": True,
                "change_detection": {
                    "n_total_pixels": int(n_total),
                    "n_changed_pixels": int(n_changed),
                    "change_rate": change_rate,
                    "change_matrix": change_matrix,
                    "message": f"Detected {n_changed} changes out of {n_total} valid pixels ({change_rate*100:.1f}%)"
                },
                "change_map_file": str(change_file),
                "timestamp": timestamp
            }
            
        finally:
            # Cleanup temp files
            try:
                Path(pred_tmp.name).unlink()
                Path(gt_tmp.name).unlink()
            except:
                pass
    
    except Exception as e:
        print(f"[CHANGE DETECTION ERROR] {str(e)}")
        raise HTTPException(status_code=500, detail=f"Change detection failed: {str(e)}")


# ============ PREDICTION WITH NDVI API ============

@app.post("/api/predict/with-ndvi")
async def predict_with_ndvi(config: PredictionWithNDVIConfig, background_tasks: BackgroundTasks):
    """Predict land classification và NDVI cho một khu vực"""
    try:
        import numpy as np
        
        # Load model
        model_path = Path(f"model_train/{config.model_filename}")
        if not model_path.exists():
            raise HTTPException(status_code=404, detail=f"Model {config.model_filename} không tồn tại")
        
        model_data = joblib.load(model_path)
        
        # Extract model from dict (models are saved as {'model': xgb_model, 'label_encoder': encoder})
        if isinstance(model_data, dict):
            model = model_data.get('model')
            label_encoder = model_data.get('label_encoder')
        else:
            model = model_data
            label_encoder = None
        
        print(f"[PREDICT+NDVI] Loaded model: {config.model_filename}")
        
        # Check cache first
        cache_dir = Path("dataset_cache")
        cache_dir.mkdir(exist_ok=True)
        cache_key = f"pred_{config.min_lon}_{config.min_lat}_{config.max_lon}_{config.max_lat}_{config.start_date}_{config.end_date}_{config.max_scenes}_{config.cloud_cover}_{config.resolution}"
        cache_hash = hashlib.md5(cache_key.encode()).hexdigest()
        cache_file = cache_dir / f"prediction_input_{cache_hash}.joblib"
        
        bbox = [config.min_lon, config.min_lat, config.max_lon, config.max_lat]
        
        # Load from cache or fetch from Microsoft
        if cache_file.exists():
            print(f"[PREDICT+NDVI] Loading from cache: {cache_file.name}")
            cached = joblib.load(cache_file)
            # Extract s2_data from cache (already computed in cache)
            s2_data = cached["s2_data"]
            # Check if needed bands are available in cache
            available_bands = list(s2_data.data_vars.keys())
            needed_bands = ["B02", "B03", "B04", "B08"]
            if all(band in available_bands for band in needed_bands):
                # Use cached data directly (no need to compute again)
                data = s2_data[needed_bands]
                print(f"[PREDICT+NDVI] Using cached bands: {needed_bands}")
                # Set items to match the number of time slices in the cached data
                items = [None] * s2_data.sizes.get("time", 1)
            else:
                raise HTTPException(status_code=400, 
                    detail=f"Cache thiếu bands cần thiết. Có: {available_bands}, Cần: {needed_bands}")
        else:
            print(f"[PREDICT+NDVI] No cache found, fetching from Microsoft Planetary Computer")
            # Connect to Microsoft Planetary Computer
            catalog = Client.open(
                "https://planetarycomputer.microsoft.com/api/stac/v1",
                modifier=planetary_computer.sign_inplace
            )
            
            time_range = f"{config.start_date}/{config.end_date}"
            
            # Search for Sentinel-2 data
            search = catalog.search(
                collections=["sentinel-2-l2a"],
                bbox=bbox,
                datetime=time_range,
                query={"eo:cloud_cover": {"lt": config.cloud_cover}}
            )
            
            items = list(search.items())[:config.max_scenes]
            print(f"[PREDICT+NDVI] Found {len(items)} Sentinel-2 scenes")
            
            if len(items) == 0:
                raise HTTPException(status_code=404, detail="Không tìm thấy dữ liệu vệ tinh")
            
            # Sign items to refresh SAS tokens (keep as pystac.Item, not dict)
            signed_items = [planetary_computer.sign(item) for item in items]
            
            # Load all bands needed for features
            data = odc.stac.load(
                signed_items,
                bbox=bbox,
                bands=["B02", "B03", "B04", "B08"],  # Blue, Green, Red, NIR
                resolution=config.resolution,
                chunks={"x": 2048, "y": 2048}
            ).compute()
        
        print(f"[PREDICT+NDVI] Loaded data shape: {data.dims}")
        
        # Calculate NDVI and other indices
        blue = data["B02"].values
        green = data["B03"].values
        red = data["B04"].values
        nir = data["B08"].values
        
        # Calculate indices
        # NDVI = (NIR - Red) / (NIR + Red)
        ndvi = (nir - red) / (nir + red + 1e-8)
        
        # NDWI = (Green - NIR) / (Green + NIR)
        ndwi = (green - nir) / (green + nir + 1e-8)
        
        # NDBI = (SWIR - NIR) / (SWIR + NIR) - we use Red as proxy
        ndbi = (red - nir) / (red + nir + 1e-8)
        
        # Prepare features for prediction
        # Assuming model was trained with [NDVI, NDWI, NDBI] features
        height, width = ndvi.shape[1:3]  # Skip time dimension
        n_pixels = height * width
        
        # Average over time dimension
        ndvi_mean = np.nanmean(ndvi, axis=0)
        ndwi_mean = np.nanmean(ndwi, axis=0)
        ndbi_mean = np.nanmean(ndbi, axis=0)
        
        # Reshape for prediction
        features = np.stack([ndvi_mean.flatten(), ndwi_mean.flatten(), ndbi_mean.flatten()], axis=1)
        
        # Handle NaN values
        valid_mask = ~np.isnan(features).any(axis=1)
        features_clean = features[valid_mask]
        
        print(f"[PREDICT+NDVI] Predicting {features_clean.shape[0]} valid pixels...")
        
        # Predict
        predictions = model.predict(features_clean)
        
        # Reshape back to raster
        prediction_raster = np.full(n_pixels, -1, dtype=np.int16)
        prediction_raster[valid_mask] = predictions
        prediction_raster = prediction_raster.reshape(height, width)

        # --- CHANGE DETECTION ---
        change_summary = None
        change_map = None
        try:
            # Use training shapefile as ground truth
            gt_shapefile = "train/ST_training data_updated_1130points_new.shp"
            gt_raster = rasterize_ground_truth(gt_shapefile, (height, width), bbox, class_column="class")
            # Compare prediction and ground truth
            mask_valid = (gt_raster >= 0) & (prediction_raster >= 0)
            changes = gt_raster[mask_valid] != prediction_raster[mask_valid]
            n_total = np.count_nonzero(mask_valid)
            n_changed = np.count_nonzero(changes)
            # Per-class change matrix
            from collections import Counter
            change_pairs = list(zip(gt_raster[mask_valid][changes], prediction_raster[mask_valid][changes]))
            change_counter = Counter(change_pairs)
            change_matrix = {f"{int(gt)}->{int(pred)}": int(cnt) for (gt, pred), cnt in change_counter.items()}
            change_summary = {
                "n_total": int(n_total),
                "n_changed": int(n_changed),
                "change_rate": float(n_changed) / n_total if n_total > 0 else 0.0,
                "change_matrix": change_matrix
            }
            # Optionally, create a change map (1=changed, 0=same, -1=invalid)
            change_map = np.full((height, width), -1, dtype=np.int8)
            change_map[mask_valid] = changes.astype(np.int8)
            # Save change map as GeoTIFF
            change_file = output_dir / f"change_map_{timestamp}.tif"
            with rasterio.open(
                change_file, 'w',
                driver='GTiff',
                height=height,
                width=width,
                count=1,
                dtype=change_map.dtype,
                crs='EPSG:4326',
                transform=from_bounds(bbox[0], bbox[1], bbox[2], bbox[3], width, height)
            ) as dst:
                dst.write(change_map, 1)
            output_files.append({"type": "change_map", "path": str(change_file)})
            print(f"[CHANGE DETECTION] Saved change map to {change_file}")
        except Exception as change_exc:
            print(f"[CHANGE DETECTION] Warning: {change_exc}")
        
        # Prepare outputs
        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
        output_dir = Path("predictions")
        output_dir.mkdir(exist_ok=True)
        
        output_files = []
        
        # Export NDVI if requested
        if config.export_ndvi:
            ndvi_file = output_dir / f"ndvi_{timestamp}.tif"
            transform = from_bounds(bbox[0], bbox[1], bbox[2], bbox[3], width, height)
            
            with rasterio.open(
                ndvi_file, 'w',
                driver='GTiff',
                height=height,
                width=width,
                count=1,
                dtype=ndvi_mean.dtype,
                crs='EPSG:4326',
                transform=transform
            ) as dst:
                dst.write(ndvi_mean, 1)
            
            output_files.append({"type": "ndvi", "path": str(ndvi_file)})
            print(f"[PREDICT+NDVI] Saved NDVI to {ndvi_file}")
        
        # Export classification if requested
        if config.export_classification:
            class_file = output_dir / f"classification_{timestamp}.tif"
            transform = from_bounds(bbox[0], bbox[1], bbox[2], bbox[3], width, height)
            
            with rasterio.open(
                class_file, 'w',
                driver='GTiff',
                height=height,
                width=width,
                count=1,
                dtype=prediction_raster.dtype,
                crs='EPSG:4326',
                transform=transform
            ) as dst:
                dst.write(prediction_raster, 1)
            
            output_files.append({"type": "classification", "path": str(class_file)})
            print(f"[PREDICT+NDVI] Saved classification to {class_file}")
        
        # Calculate statistics
        ndvi_stats = {
            "mean": float(np.nanmean(ndvi_mean)),
            "min": float(np.nanmin(ndvi_mean)),
            "max": float(np.nanmax(ndvi_mean)),
            "std": float(np.nanstd(ndvi_mean))
        }
        
        # Count classes
        unique_classes, counts = np.unique(predictions, return_counts=True)
        class_distribution = {
            int(cls): int(count) for cls, count in zip(unique_classes, counts)
        }
        
        return {
            "success": True,
            "message": "Prediction with NDVI completed",
            "output_files": output_files,
            "ndvi_stats": ndvi_stats,
            "class_distribution": class_distribution,
            "n_scenes": len(items),
            "resolution": config.resolution,
            "bbox": bbox,
            "change_detection": change_summary
        }
        
    except Exception as e:
        print(f"[PREDICT+NDVI ERROR] {str(e)}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=str(e))


# ============ NDVI TIME SERIES API ============

@app.post("/api/ndvi/timeseries")
async def calculate_ndvi_timeseries(config: NDVIConfig):
    """Tính NDVI time series cho một khu vực"""
    try:
        import numpy as np
        import xarray as xr
        from pystac_client import Client
        import planetary_computer
        import odc.stac
        
        print(f"[NDVI] Starting calculation for bbox: {config.bbox}, time: {config.start_date} to {config.end_date}")
        
        # Connect to Microsoft Planetary Computer STAC API
        catalog = Client.open(
            "https://planetarycomputer.microsoft.com/api/stac/v1",
            modifier=planetary_computer.sign_inplace
        )
        
        bbox = config.bbox
        time_range = f"{config.start_date}/{config.end_date}"
        
        # Search for Sentinel-2 data
        search = catalog.search(
            collections=["sentinel-2-l2a"],
            bbox=bbox,
            datetime=time_range,
            query={"eo:cloud_cover": {"lt": config.max_cloud_cover}}
        )
        
        items = list(search.items())
        print(f"[NDVI] Found {len(items)} Sentinel-2 scenes")
        
        if len(items) == 0:
            raise HTTPException(status_code=404, detail="Không tìm thấy dữ liệu Sentinel-2 cho khu vực và thời gian này")
        
        # Load data for each time step
        ndvi_timeseries = []
        
        for item in items:
            try:
                # Load NIR (B08) and Red (B04) bands
                data = odc.stac.load(
                    [item],
                    bbox=bbox,
                    bands=["B04", "B08"],  # Red and NIR
                    resolution=config.resolution,
                    chunks={"x": 2048, "y": 2048}
                ).compute()
                
                if data is None or len(data.keys()) == 0:
                    continue
                
                # Calculate NDVI = (NIR - Red) / (NIR + Red)
                nir = data["B08"].values
                red = data["B04"].values
                
                # Avoid division by zero
                denominator = nir + red
                denominator = np.where(denominator == 0, np.nan, denominator)
                
                ndvi = (nir - red) / denominator
                
                # Calculate mean NDVI (ignore NaN values)
                mean_ndvi = float(np.nanmean(ndvi))
                
                # Get date from item
                date_str = item.datetime.strftime("%Y-%m-%d")
                
                ndvi_timeseries.append({
                    "date": date_str,
                    "ndvi": mean_ndvi
                })
                
                print(f"[NDVI] {date_str}: NDVI = {mean_ndvi:.3f}")
                
            except Exception as e:
                print(f"[NDVI WARNING] Failed to process item {item.id}: {e}")
                continue
        
        if len(ndvi_timeseries) == 0:
            raise HTTPException(status_code=500, detail="Không thể tính NDVI cho bất kỳ ảnh nào")
        
        # Sort by date
        ndvi_timeseries.sort(key=lambda x: x["date"])
        
        # Calculate statistics
        ndvi_values = [item["ndvi"] for item in ndvi_timeseries]
        mean_ndvi = float(np.mean(ndvi_values))
        min_ndvi = float(np.min(ndvi_values))
        max_ndvi = float(np.max(ndvi_values))
        
        result = {
            "timeseries": ndvi_timeseries,
            "n_images": len(ndvi_timeseries),
            "mean_ndvi": mean_ndvi,
            "min_ndvi": min_ndvi,
            "max_ndvi": max_ndvi,
            "bbox": bbox,
            "time_range": time_range
        }
        
        print(f"[NDVI] Calculation complete. Mean NDVI: {mean_ndvi:.3f}, Images: {len(ndvi_timeseries)}")
        
        return result
        
    except HTTPException:
        raise
    except Exception as e:
        print(f"[NDVI ERROR] {e}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"Lỗi khi tính NDVI: {str(e)}")


if __name__ == "__main__":
    print("=" * 70)
    print("🚀 LAND CLASSIFICATION TRAINING API SERVER")
    print("=" * 70)
    print("\n📍 Endpoints:")
    print("   - Web Interface: http://localhost:8000")
    print("   - API Docs: http://localhost:8000/docs")
    print("   - Start Training: POST http://localhost:8000/api/training/start")
    print("   - Check Status: GET http://localhost:8000/api/training/status")
    print("\n" + "=" * 70)
    
    uvicorn.run(app, host="0.0.0.0", port=8000, log_level="info")