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]