from ultralytics import YOLO
from datetime import datetime
import os
import shutil

def train_yolo_model(data_path, model='yolov9s.pt', epochs=10, batch_size=8, imgsz=320):
    """
    Train YOLOv9s model on Raspberry Pi 5 with light settings and CSV export.
    """
    model = YOLO(model)

    results = model.train(
        data=data_path,
        epochs=epochs,
        batch=batch_size,
        imgsz=imgsz,
        device='cpu',
        workers=2,
        cache=True,     # PS: como True consome RAM, mas acelera. Desligar se travar
        amp=False,
        patience=5,
        save_period=5,
        plots=False,
        verbose=True
    )

    save_results_csv()
    return results

def save_results_csv():
    now = datetime.now().strftime("%Y%m%d_%H%M%S")
    source = "runs/detect/train/results.csv"
    target_dir = "training_logs"
    target = f"{target_dir}/yolov9s_{now}.csv"
    os.makedirs(target_dir, exist_ok=True)
    
    if os.path.exists(source):
        shutil.copy(source, target)
        print(f"✅ Training metrics saved to: {target}")
    else:
        print("⚠️ results.csv not found. Was training successful?")

if __name__ == "__main__":
    data_path = "fasdd.yaml"
    train_yolo_model(
        data_path=data_path,
        model='yolov9s.pt',
        epochs=10,
        batch_size=8, # Adjust if it crashes
        imgsz=320    # Test 416 later
    )