app.py

import gradio as gr
import torch
from torchvision import transforms
from PIL import Image
import numpy as np
from unet_model import UNet
from huggingface_hub import hf_hub_download

# Load trained model weights from Hugging Face Hub
weights_path = hf_hub_download(
    repo_id="faranbutt789/my-dataset",  # ensure this matches your repo
    filename="unet_weights_v2.pth"                # make sure this file exists in repo
)

# Initialize and load model
model = UNet()
model.load_state_dict(torch.load(weights_path, map_location="cpu"))
model.eval()

# Preprocessing: same as training
IMG_HEIGHT, IMG_WIDTH = 128, 128
transform = transforms.Compose([
    transforms.Resize((IMG_HEIGHT, IMG_WIDTH)),
    transforms.ToTensor()
])

def predict(image):
    orig_w, orig_h = image.size   # original size of uploaded image
    img = transform(image).unsqueeze(0)  # (1,3,128,128)
    with torch.no_grad():
        pred = model(img)

    mask = pred.squeeze(0).squeeze(0).cpu().numpy()
    mask = (mask * 255).astype(np.uint8)  # grayscale mask

    # Resize back to original size
    mask_img = Image.fromarray(mask).resize((orig_w, orig_h), Image.NEAREST)
    return mask_img


# Gradio interface
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.Image(type="pil"),
    title="UNet Crack Segmentation",
    description="Upload a concrete surface image to get predicted crack mask"
)

if __name__ == "__main__":
    demo.launch()