import os
import torch
from pathlib import Path
from huggingface_hub import hf_hub_download, HfApi
from dotenv import load_dotenv
import yaml

load_dotenv()

class ModelLoader:
    def __init__(self):
        self.hf_repo = os.getenv("HF_MODEL_REPO", "junaid17/damagelens-models")
        self.hf_token = os.getenv("HF_TOKEN", None)
        self.cache_dir = Path(os.getenv("MODEL_CACHE_DIR", "./model_cache"))
        self.cache_dir.mkdir(parents=True, exist_ok=True)
        
        # Load model config
        with open("model_config.yaml", "r") as f:
            self.config = yaml.safe_load(f)
    
    def download_model(self, model_type: str) -> str:
        """
        Download a model from HuggingFace Hub and return local path.
        
        Args:
            model_type: 'resnet', 'fusion', or 'yolo'
        
        Returns:
            Path to cached model file
        """
        if model_type not in self.config["models"]:
            raise ValueError(f"Unknown model type: {model_type}")
        
        model_info = self.config["models"][model_type]
        filename = model_info["filename"]
        
        # Check if already cached locally
        local_path = self.cache_dir / filename
        if local_path.exists():
            print(f"✓ Using cached {model_type} model: {local_path}")
            return str(local_path)
        
        # Download from HuggingFace Hub
        print(f"⏳ Downloading {model_type} model from {self.hf_repo}...")
        try:
            downloaded_path = hf_hub_download(
                repo_id=self.hf_repo,
                filename=filename,
                cache_dir=str(self.cache_dir),
                token=self.hf_token,
                resume_download=True
            )
            print(f"✓ Downloaded {model_type} model: {downloaded_path}")
            return downloaded_path
        
        except Exception as e:
            print(f"❌ Failed to download {model_type} model: {str(e)}")
            raise RuntimeError(f"Could not load {model_type} model from HuggingFace Hub: {str(e)}")
    
    def load_checkpoint(self, model_type: str, device: str = None) -> dict:
        """
        Download and load a model checkpoint.
        
        Args:
            model_type: 'resnet', 'fusion', or 'yolo'
            device: torch device (auto-detect if None)
        
        Returns:
            Model state dict
        """
        if device is None:
            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
        model_path = self.download_model(model_type)
        checkpoint = torch.load(model_path, map_location=device)
        return checkpoint
    
    def get_model_path(self, model_type: str) -> str:
        """Get local path to model (downloads if needed)."""
        return self.download_model(model_type)


def initialize_models(class_map: dict):
    """
    Initialize all models from HuggingFace Hub.
    Returns predictor instances ready for inference.
    """
    from scripts.prediction_helper import ResnetCarDamagePredictor, FusionCarDamagePredictor
    
    loader = ModelLoader()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    print("\n" + "="*50)
    print("🚀 Initializing Models from HuggingFace Hub")
    print("="*50)
    
    # Load ResNet model
    resnet_path = loader.get_model_path("resnet")
    resnet_model = ResnetCarDamagePredictor(resnet_path, class_map)
    print("✓ ResNet model loaded successfully")
    
    # Load Fusion model
    fusion_path = loader.get_model_path("fusion")
    fusion_model = FusionCarDamagePredictor(fusion_path, class_map)
    print("✓ Fusion model loaded successfully")
    
    print("="*50 + "\n")
    
    return resnet_model, fusion_model, loader


if __name__ == "__main__":
    # Test the loader
    loader = ModelLoader()
    print(f"Repository: {loader.hf_repo}")
    print(f"Cache dir: {loader.cache_dir}")
    print(f"Models config: {loader.config['models'].keys()}")