handler.py

import torch
from PIL import Image
import torchvision.transforms as T
from model import get_model
import os

class EndpointHandler():
    def __init__(self, path=""):
        # Load model and weights
        self.model = get_model()
        weights_path = os.path.join(path, "doge_223_sd-04.bin")
        self.model.load_state_dict(torch.load(weights_path, map_location="cpu"))
        self.model.eval()
        
        # Define your specific transforms
        self.transform = T.Compose([
            T.Resize((224, 224)),
            T.ToTensor(),
            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])

    def __call__(self, data):
        inputs = data.pop("inputs", data)
        # Convert bytes to image
        image = Image.open(inputs).convert("RGB")
        tensor = self.transform(image).unsqueeze(0)
        
        with torch.no_grad():
            outputs = self.model(tensor)
            prediction = torch.argmax(outputs, dim=1).item()
            
        return {"label": prediction}