Add Gradio app.py and update requirements.txt

app.py

@@ -0,0 +1,89 @@
+import os
+import torch
+import numpy as np
+from PIL import Image
+import torchvision.transforms as transforms
+import gradio as gr
+
+from models.networks import define_G
+
+# --- 1. Model Configuration ---
+class Args:
+    n_class = 2
+    net_G = 'base_transformer_pos_s4_dd8_dedim8'
+    gpu_ids = []  # [] for CPU inference
+    img_size = 256
+
+args = Args()
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# --- 2. Load Model & Weights ---
+print(f"Loading model on {device}...")
+model = define_G(args=args, gpu_ids=args.gpu_ids)
+
+checkpoint_path = os.path.join("checkpoints", "BIT_LEVIR", "best_ckpt.pt")
+if not os.path.exists(checkpoint_path):
+    raise FileNotFoundError(f"Checkpoint not found at: {checkpoint_path}")
+
+checkpoint = torch.load(checkpoint_path, map_location=device)
+model.load_state_dict(checkpoint["model_G_state_dict"])
+model.to(device)
+model.eval()
+
+# --- 3. Image Preprocessing ---
+transform = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+])
+
+# --- 4. Prediction Function ---
+def predict_change(img_a_pil, img_b_pil):
+    if img_a_pil is None or img_b_pil is None:
+        return None, None
+
+    # Convert to RGB
+    img_a_rgb = img_a_pil.convert("RGB")
+    img_b_rgb = img_b_pil.convert("RGB")
+
+    # Transform to tensor
+    tensor_a = transform(img_a_rgb).unsqueeze(0).to(device)
+    tensor_b = transform(img_b_rgb).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        output = model(tensor_a, tensor_b)
+        pred = torch.argmax(output, dim=1).squeeze(0).cpu().numpy()
+
+    # Binary mask: 0 (No change, Black), 255 (Change, White)
+    mask = (pred * 255).astype(np.uint8)
+    mask_pil = Image.fromarray(mask, mode="L")
+
+    # Create a red change overlay on Image B for clear visualization
+    img_b_resized = img_b_rgb.resize((256, 256))
+    img_b_np = np.array(img_b_resized)
+    overlay_np = img_b_np.copy()
+    overlay_np[mask == 255] = [255, 50, 50]  # Highlight changes in Red
+    blended = Image.blend(img_b_resized, Image.fromarray(overlay_np), alpha=0.5)
+
+    return mask_pil, blended
+
+# --- 5. Gradio Web UI ---
+demo = gr.Interface(
+    fn=predict_change,
+    inputs=[
+        gr.Image(type="pil", label="Image A (Before)"),
+        gr.Image(type="pil", label="Image B (After)")
+    ],
+    outputs=[
+        gr.Image(type="pil", label="Change Detection Mask"),
+        gr.Image(type="pil", label="Overlay on Image B (Red = Change)")
+    ],
+    title="🛰️ Remote Sensing Change Detection (BIT_CD)",
+    description="Upload two satellite/aerial images (Before & After) to detect structural and landscape changes.",
+    examples=[
+        ["samples/A/test_2_0000_0000.png", "samples/B/test_2_0000_0000.png"]
+    ] if os.path.exists("samples/A/test_2_0000_0000.png") else None
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -7,4 +7,5 @@ scikit-learn==0.24.2
 matplotlib==3.3.4
 tensorboardX
 tifffile
+gradio