model.py

from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation
from PIL import Image
import torch
import numpy as np

# Load model
processor = AutoImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
model = AutoModelForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")

def predict_defect(image: Image.Image):
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits
    segmentation = torch.argmax(logits.squeeze(), dim=0).detach().cpu().numpy()
    
    # Convert to RGB overlay
    overlay = np.zeros((segmentation.shape[0], segmentation.shape[1], 3), dtype=np.uint8)
    overlay[segmentation == 12] = [255, 0, 0]  # example label index for defects (adjust accordingly)
    return Image.fromarray(overlay)