"""
Complete Workflow Script
Creates dataset, trains model, and uploads to Hugging Face
"""

import subprocess
import sys
from pathlib import Path


def print_banner(text):
    print("\n" + "="*60)
    print(text.center(60))
    print("="*60 + "\n")


def check_dataset():
    """Check if dataset exists"""
    dataset_dir = Path("./dataset")
    
    if not dataset_dir.exists():
        print("❌ Dataset directory not found!")
        return False
    
    # Check for images
    images = list(dataset_dir.glob("*.jpg")) + list(dataset_dir.glob("*.png"))
    
    if len(images) == 0:
        print("⚠ No images found in dataset directory!")
        return False
    
    print(f"✓ Dataset found with {len(images)} images")
    return True


def create_dataset():
    """Create test dataset"""
    print_banner("STEP 1: CREATE DATASET")
    
    if check_dataset():
        print("\nDataset already exists!")
        response = input("Overwrite? (y/n): ").strip().lower()
        if response != 'y':
            print("Skipping dataset creation...")
            return True
    
    print("\nCreating test dataset...")
    
    try:
        from create_test_dataset import create_test_dataset
        create_test_dataset(output_dir="./dataset", num_images=10)
        print("\n✅ Dataset created successfully!")
        return True
    except Exception as e:
        print(f"\n❌ Error creating dataset: {e}")
        import traceback
        traceback.print_exc()
        return False


def train_model():
    """Train the model"""
    print_banner("STEP 2: TRAIN MODEL")
    
    print("\nStarting training...")
    print("This may take several minutes depending on your CPU...\n")
    
    try:
        # Run training
        result = subprocess.run([
            sys.executable, "train.py",
            "--train_data", "./dataset",
            "--output_dir", "./models/bytedream",
            "--device", "cpu"
        ], check=True)
        
        print("\n✅ Training completed successfully!")
        return True
        
    except subprocess.CalledProcessError as e:
        print(f"\n❌ Training failed: {e}")
        return False
    except Exception as e:
        print(f"\n❌ Error: {e}")
        import traceback
        traceback.print_exc()
        return False


def upload_to_hf(token, repo_id):
    """Upload to Hugging Face"""
    print_banner("STEP 3: UPLOAD TO HUGGING FACE")
    
    # Check if model exists
    model_dir = Path("./models/bytedream")
    if not model_dir.exists():
        print("❌ Model directory not found!")
        print("Please train the model first.")
        return False
    
    print(f"\nUploading to {repo_id}...")
    
    try:
        from bytedream.generator import ByteDreamGenerator
        
        # Load generator
        print("\nLoading model...")
        generator = ByteDreamGenerator(
            model_path="./models/bytedream",
            config_path="config.yaml",
            device="cpu",
        )
        
        # Upload to HF
        generator.push_to_hub(
            repo_id=repo_id,
            token=token,
            private=False,
            commit_message="Upload Byte Dream model",
        )
        
        print("\n✅ Upload successful!")
        print(f"\n📦 Your model is available at:")
        print(f"https://huggingface.co/{repo_id}")
        return True
        
    except Exception as e:
        print(f"\n❌ Upload failed: {e}")
        import traceback
        traceback.print_exc()
        return False


def main():
    """Main workflow"""
    print_banner("BYTE DREAM - COMPLETE WORKFLOW")
    
    print("This script will:")
    print("1. Create test dataset")
    print("2. Train the model")
    print("3. Upload to Hugging Face")
    print()
    
    # Step 1: Create dataset
    if not create_dataset():
        print("\n❌ Failed to create dataset. Exiting...")
        return
    
    # Step 2: Train
    print("\nReady to train the model.")
    response = input("Continue to training? (y/n): ").strip().lower()
    if response != 'y':
        print("Training cancelled.")
        return
    
    if not train_model():
        print("\n❌ Training failed. Exiting...")
        return
    
    # Step 3: Upload to HF
    print("\nReady to upload to Hugging Face.")
    response = input("Continue to upload? (y/n): ").strip().lower()
    if response != 'y':
        print("Upload cancelled.")
        print("\nModel saved to: ./models/bytedream")
        print("To upload later: python publish_to_hf.py")
        return
    
    # Get HF credentials
    token = input("\nEnter your Hugging Face token (hf_...): ").strip()
    if not token:
        print("❌ Token required!")
        return
    
    repo_id = input("Enter repository ID (e.g., Enzo8930302/ByteDream): ").strip()
    if not repo_id:
        print("❌ Repository ID required!")
        return
    
    if not upload_to_hf(token, repo_id):
        print("\n❌ Upload failed.")
        return
    
    # Success!
    print_banner("WORKFLOW COMPLETED SUCCESSFULLY!")
    print("✅ Dataset created")
    print("✅ Model trained")
    print(f"✅ Uploaded to Hugging Face: {repo_id}")
    print()


if __name__ == "__main__":
    try:
        main()
    except KeyboardInterrupt:
        print("\n\nWorkflow interrupted!")
        sys.exit(0)