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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]
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