script.py

# function to turn photos to tensor
def img2tensor(x):
    transform = transforms.Compose(
            [transforms.ToPILImage(),
            transforms.Grayscale(num_output_channels=1),
            transforms.ToTensor(),
            transforms.Normalize((0.5), (0.5))])
    return transform(x)

# the model for predicting
model = FERModel(1, 7)
softmax = torch.nn.Softmax(dim=1)
model.load_state_dict(torch.load('FER2013-Resnet9.pth', map_location=get_default_device()))

def predict(x):
    out = model(img2tensor(img)[None])
    scaled = softmax(out)
    prob = torch.max(scaled).item()
    label = classes[torch.argmax(scaled).item()]
    return {'label': label, 'probability': prob}