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| import torch | |
| def predict_class(image_tensor, model): | |
| """ | |
| Predicts the class of the image using the model. | |
| Args: | |
| image_tensor (torch.Tensor): Transformed image. | |
| model (torch.nn.Module): The loaded PyTorch model. | |
| Returns: | |
| str: Predicted class (eliptical or spiral). | |
| float: Confidence of the prediction. | |
| """ | |
| with torch.no_grad(): | |
| device = torch.device("cpu") | |
| output = model(image_tensor.to(device)) | |
| probabilities = torch.softmax(output, dim=1) | |
| predicted_class_index = torch.argmax(probabilities, dim=1).item() | |
| confidence = torch.max(probabilities).item() | |
| classes = ['eliptical', 'spiral'] | |
| predicted_class = classes[predicted_class_index] | |
| return predicted_class, confidence |