handler.py

from typing import  Dict, List, Any
import torch
import numpy as np
import torch.nn.functional as F
from serkan import SimpleUpscaleModel
class EndpointHandler():
    def __init__(self, path="model_weights.pth"):
        # load the optimized model
        self.model = SimpleUpscaleModel()
        self.model.load_state_dict(torch.load("model_weights.pth"))




    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        """
        Args:
            data (:obj:):
                includes the input data and the parameters for the inference.
        Return:
            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
                - "label": A string representing what the label/class is. There can be multiple labels.
                - "score": A score between 0 and 1 describing how confident the model is for this label/class.
        """
        inputs = data.pop("inputs", data)
        img = inputs["image"]

        # Load the image
        img = np.float32(img)

        upscaled = self.model(img)
        # postprocess the prediction
        return "OKAY"