handler.py

from typing import  Dict, List, Any
import base64
import io
import torch
import numpy as np
import torch.nn.functional as F
from serkan import SimpleUpscaleModel
import os
from PIL import Image

def decode_image(base64_str: str) -> np.ndarray:
        """Decode base64 string to an image (numpy array)"""
        image_data = base64.b64decode(base64_str)
        image = Image.open(io.BytesIO(image_data))
        return np.array(image)

class EndpointHandler():
    def __init__(self, path="."):
        # load the optimized model
        self.model = SimpleUpscaleModel()
        model_path = os.path.join(path, "model_weights.pth")
        self.model.load_state_dict(torch.load(model_path))


    

    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"]
        img = decode_image(img)
        img = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).float()
        # Load the image
        upscaled = self.model(img)
        upscaled = upscaled.squeeze(0).permute(1,2,0)
        upscaled = upscaled.numpy()
        upscaled = np.clip(upscaled, 0, 255).astype(np.uint8)

        
        pil = Image.fromarray(upscaled)
        # Save the image to a buffer
        buffered = io.BytesIO()
        pil.save(buffered, format="PNG")
        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")

        # Return a dictionary with the base64 image and additional data
        return {
            "image": img_str
        }

        # postprocess the prediction
        return "OKAY"