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

class NLPModel(BaseModel):
    """AutoDL model for natural language processing."""
    
    def __init__(self, task="sentiment", **kwargs):
        super().__init__(name=f"NLPModel_{task}")
        self.task = task
        self.device = DEVICE
        logger.info(f"Initialized NLPModel for {task} on {self.device}")

    def fit(self, data, text_col="text", target="label", **kwargs):
        """Train on a CSV with text and label columns."""
        logger.info(f"Starting NLP training on task {self.task}...")
        # Placeholder for Transformers/Torch training logic
        logger.warning("NLPModel implementation is currently a placeholder.")
        return self

    def predict(self, text, **kwargs):
        """Predict on a string or list of strings."""
        return "Not implemented"

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

