app.py

import gradio as gr
import torch
from torchvision import transforms
from PIL import Image
import numpy as np

# 1. Import your custom class from the local file
from residual_unet import ResidualUNet

# === Model Loading (The only part that changes) ===
weights_path = hf_hub_download(
    repo_id="keysun89/resunet_1",  # ensure this matches your repo
    filename="best_residual_unet_model.pth"              # make sure this file exists in repo
)

# 2. Load the model using the built-in method
# This automatically downloads the weights AND reads config.json
model = ResidualUNet.from_pretrained(repo_id) 
model.eval()

# === Preprocessing (Same as before) ===
IMG_HEIGHT, IMG_WIDTH = 128, 128 
transform = transforms.Compose([
    transforms.Resize((IMG_HEIGHT, IMG_WIDTH)),
    transforms.ToTensor()])

# === Prediction Function (Same as before) ===
def predict(image):
    orig_w, orig_h = image.size
    img = transform(image).unsqueeze(0)
    
    with torch.no_grad():
        pred = model(img)

    mask = pred.squeeze(0).squeeze(0).cpu().numpy()
    mask = (mask * 255).astype(np.uint8)
    mask_img = Image.fromarray(mask).resize((orig_w, orig_h), Image.NEAREST)
    return mask_img

# === Gradio Interface (Same as before) ===
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.Image(type="pil"),
    title="Residual-UNet Segmentation",
    description="Upload an image to get the predicted segmentation mask.")

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