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### function process image
def process_image(image):
    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,
        returns an Numpy array
    '''

    # TODO: Process a PIL image for use in a PyTorch model
    # tensor.numpy().transpose(1, 2, 0)

    preprocess = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ])
    image = preprocess(image)
    return image

### predict product recommendation
def predict2(uploaded_file_path, model, topk=5):
    ''' Predict the class (or classes) of an image using a trained deep learning model.
    '''

    # TODO: Implement the code to predict the class from an image file
    img = Image.open(uploaded_file_path)
    img.convert("RGB")


    img = process_image(img)

    # Convert 2D image to 1D vector
    img = np.expand_dims(img, 0)

    img = torch.from_numpy(img)

    model.eval()
    inputs = Variable(img).to(device)
    logits = model.forward(inputs)

    ps = F.softmax(logits,dim=1)
    topk = ps.cpu().topk(topk)

    return (e.data.numpy().squeeze().tolist() for e in topk)