# handler.py
from typing import Any, Dict, List
from transformers import pipeline

class EndpointHandler:
    def __init__(self, path: str = ""):
        """
        Load your model and create a Hugging Face pipeline.
        'path' is the local folder or repo name containing your model.
        """
        self.classifier = pipeline("text-classification", model=path)

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Called on each inference request.
        Expects a dict with an "inputs" key (string or list of strings).
        Returns the pipeline output as a list of dicts.
        """
        # Extract inputs; if they passed raw string, handle that too
        inputs = data.get("inputs", data)
        # Run inference
        return self.classifier(inputs)