from ..core.base_model import BaseModel
from ..utils.logger import logger
from ..utils.device import DEVICE

class ImageModel(BaseModel):
    """AutoDL model for computer vision tasks."""
    
    def __init__(self, task="classification", **kwargs):
        super().__init__(name=f"ImageModel_{task}")
        self.task = task
        self.device = DEVICE
        logger.info(f"Initialized ImageModel for {task} on {self.device}")

    def fit(self, data_path, **kwargs):
        """Train on a directory of images."""
        logger.info(f"Starting Image DL training on {data_path}...")
        # Placeholder for torchvision/torch training logic
        logger.warning("ImageModel implementation is currently a placeholder.")
        return self

    def predict(self, image_path, **kwargs):
        """Predict on an image file."""
        logger.info(f"Predicting on {image_path}...")
        return "Not implemented"

    def evaluate(self, data, **kwargs):
        pass

