from typing import Dict, List, Any
import importlib.util

class EndpointHandler:
    def __init__(self, path=""):
        """
        The __init__ method is called when starting the Endpoint.
        We perform the imports and model loading here to match your logic.
        """
        # 1. Check if spaCy is installed (Your specific error handling)
        if importlib.util.find_spec("spacy") is None:
            raise RuntimeError(
                "SpaCy is required but not installed. Install it with:\n"
                ".\\.venv\\Scripts\\python -m pip install spacy\n"
                "Then download the model:\n"
                ".\\.venv\\Scripts\\python -m spacy download en_core_web_sm"
            )

        import spacy

        # 2. Load the model (Your specific error handling)
        try:
            # We load the model directly by name since it's installed via requirements.txt
            self.nlp = spacy.load("en_core_web_sm")
        except Exception as e:
            raise RuntimeError(
                "SpaCy model 'en_core_web_sm' is required but not available. "
                "Install it with:\n"
                ".\\.venv\\Scripts\\python -m spacy download en_core_web_sm"
            ) from e

    def __call__(self, data: Dict[str, Any]) -> List[str]:
        """
        The __call__ method is called on every request.
        """
        # 1. Extract inputs
        # The payload usually comes as {"inputs": "some text"}
        raw_text = data.pop("inputs", data)

        # Handle edge case where inputs might be a list
        if isinstance(raw_text, list):
            raw_text = raw_text[0]

        # 2. Run your processing logic
        doc = self.nlp(raw_text)
        
        # 3. Apply your specific list comprehension
        raw_sentences = [s.text.strip() for s in doc.sents if s.text.strip()]

        return raw_sentences