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pipeline.py

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+
+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]