from typing import Dict, List, Any
from io import BytesIO
import base64
import logging
import uform
from PIL import Image
import numpy as np

class EndpointHandler():
    def __init__(self, path=""):
        self.model, self.processor = uform.get_model('unum-cloud/uform-vl-multilingual-v2')

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        data args:
            image (:obj:`string`)
            candidates (:obj:`list`)
        Return:
            A :obj:`list`: une liste permettant de passer les embedding 
        """
        inputs_request = data.pop("inputs", data)

        # decode base64 image to PIL
        image = Image.open(BytesIO(base64.b64decode(inputs_request['image'])))
        text = inputs_request['text']

        image_data = self.processor.preprocess_image(image)
        text_data = self.processor.preprocess_text(text)

        image_features, image_embedding = self.model.encode_image(image_data)
        text_features, text_embedding = self.model.encode_text(text_data)
        joint_embedding = self.model.encode_multimodal(image=image_data, text=text_data)

        # Convert embeddings to lists of floats
        serializable_results = {
            'joint_embedding': joint_embedding.tolist() if isinstance(joint_embedding, np.ndarray) else joint_embedding,
            'text_embedding': text_embedding.tolist() if isinstance(text_embedding, np.ndarray) else text_embedding,
            'image_embedding': image_embedding.tolist() if isinstance(image_embedding, np.ndarray) else image_embedding
        }

        return serializable_results