hadnler.py

from PIL import Image
import io
import torch
import torch.nn as nn
import torchvision.transforms as transforms

class EndpointHandler:
    def __init__(self, model_path: str):
        # ładujemy model
        from your_model_file import Net  # import architektury
        self.model = Net()
        self.model.load_state_dict(torch.load(model_path, map_location="cpu"))
        self.model.eval()

        self.transform = transforms.Compose([
            transforms.Grayscale(),
            transforms.Resize((28,28)),
            transforms.ToTensor(),
        ])

    def __call__(self, data: dict) -> dict:
        # data["inputs"] może być bajtami obrazu
        image_bytes = data["inputs"]
        image = Image.open(io.BytesIO(image_bytes)).convert("L")
        tensor = self.transform(image).unsqueeze(0)
        with torch.no_grad():
            logits = self.model(tensor)
        scores = torch.softmax(logits, dim=1)[0].tolist()
        # zwróć listę etykiety-score
        return {"scores": scores}