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| from PIL import Image | |
| import torch | |
| from typing import IO | |
| from model_loader import models | |
| class ImagePreprocessor: | |
| def __init__(self): | |
| self.preprocess = models.clip_preprocess | |
| self.device = models.device | |
| def process(self, image_file: IO) -> torch.Tensor: | |
| """ | |
| Opens an image file, preprocesses it, and returns it as a tensor. | |
| Args: | |
| image_file (IO): The image file object (e.g., from a file upload). | |
| Returns: | |
| torch.Tensor: The preprocessed image as a tensor, ready for the model. | |
| """ | |
| try: | |
| # Open the image from the file-like object | |
| image = Image.open(image_file).convert("RGB") | |
| except Exception as e: | |
| print(f"Error opening image: {e}") | |
| # You might want to raise a custom exception here | |
| raise ValueError("Invalid or corrupted image file.") | |
| # Apply the CLIP preprocessing transformations and move to the correct device | |
| image_tensor = self.preprocess(image).unsqueeze(0).to(self.device) | |
| return image_tensor | |
| preprocessor = ImagePreprocessor() | |