| import torch | |
| import torch.nn as nn | |
| from PIL import Image | |
| import torchvision.transforms as transforms | |
| from typing import List | |
| class GreggRecognitionPipeline: | |
| def __init__(self, model_path="pytorch_model.bin"): | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| self.transform = transforms.Compose([ | |
| transforms.Resize((256, 256)), | |
| transforms.Grayscale(num_output_channels=1), | |
| transforms.ToTensor(), | |
| ]) | |
| # Load model here - implement based on your model structure | |
| def __call__(self, images): | |
| """Process images and return text predictions""" | |
| if not isinstance(images, list): | |
| images = [images] | |
| results = [] | |
| for image in images: | |
| if isinstance(image, str): | |
| image = Image.open(image) | |
| # Preprocess image | |
| image_tensor = self.transform(image).unsqueeze(0).to(self.device) | |
| # Generate text (implement based on your model) | |
| with torch.no_grad(): | |
| # This is a placeholder - replace with your actual inference | |
| predicted_text = "sample_text" | |
| results.append({"generated_text": predicted_text}) | |
| return results if len(results) > 1 else results[0] | |