from typing import Dict, List, Any
from transformers import pipeline
import logging

# Configuración básica del logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class EndpointHandler():
    def __init__(self, path=""):
        logger.info(f"Modelo cargado desde el path: {path}")
        # Cargar el pipeline con el modelo especificado
        try:
            self.pipeline = pipeline(model=path, truncation=True)
        except Exception as e:
            logger.exception(f"Error cargando el modelo desde el path {path}: {e}")
            path = "AndresR2909/finetuning-bert-text-classification"
            self.pipeline = pipeline(model=path, truncation=True)


    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
       data args:
            inputs (:obj: `str`)
            date (:obj: `str`)
      Return:
            A :obj:`list` | `dict`: will be serialized and returned
        """
        # get inputs
        input = data.get("inputs",data)
        date = data.get("date", None)

        # Textos para análisis
        texts = [input]
        # Realizar la inferencia
        outputs = self.pipeline(texts)
        
        # Ajustar las etiquetas a 0 y 1
        label_mapping = {"LABEL_0": 0, "LABEL_1": 1}
        label_names = {0: "sin_intencion", 1: "intencion_suicida"}
        
        # Modificar las salidas con el formato deseado
        adjusted_results = [
            {
                "input": text,
                "clasiffication": str(label_mapping[result['label']]),  # "clasiffication" como string
                "label": label_names[label_mapping[result['label']]]    # Mapeo de etiquetas a nombres
            }
            for result, text in zip(outputs, texts)
        ]
        return adjusted_results