# TODO: Implement Fine-Tuning Logic

"""
DeepDream Training / Fine-Tuning Script (Placeholder)

Goal:
Allow users to fine-tune these base models (VGG, GoogLeNet, etc.) on their own datasets
to create custom Dream styles.

Steps to Implement:
1.  Load Dataset: Use `torchvision.datasets.ImageFolder` or custom loader for user images.
2.  Load Model: Use our MLX models (need to add `train()` mode with dropout/grad support if missing,
    or simpler: use PyTorch for training -> export to MLX).
    *Easier path:* Train in PyTorch using standard scripts, then use `export_*.py` to bring it here.
3.  Training Loop: Standard classification training or style transfer fine-tuning.
4.  Export: Save the fine-tuned weights to `.pth`, then run export script.

Usage:
    python train_dream.py --data /path/to/images --epochs 10 --model vgg16
"""

import argparse

def main():
    print("--- DeepDream-MLX Training Stub ---")
    print("Feature coming soon.")
    print("Current Workflow: Train in PyTorch -> Use export_*.py -> Dream in MLX")

if __name__ == "__main__":
    main()