Update app.py

app.py

@@ -34,33 +34,24 @@ def soft_alpha(mask_uint8, blur_radius=10):
     return (blurred.astype(np.float32) / 255.0).clip(0.0, 1.0)
 
 def isolate_with_click(image: Image.Image, evt: gr.SelectData):
-    """
-    image: PIL image uploaded
-    evt: click coordinates from Gradio (x,y)
-    """
     img_rgb = np.array(image.convert("RGB"))
     predictor.set_image(img_rgb)
 
-    # SAM expects input points as numpy array [[x,y]]
     input_point = np.array([[evt.index[0], evt.index[1]]])
-    input_label = np.array([1])  # 1 = foreground
+    input_label = np.array([1])  # foreground
 
-    masks, scores, logits = predictor.predict(
+    masks, scores, _ = predictor.predict(
         point_coords=input_point,
         point_labels=input_label,
         multimask_output=True
     )
 
-    # Pick the highest score mask
     best_mask = masks[np.argmax(scores)].astype(np.uint8) * 255
-
-    # Soft alpha
     alpha = soft_alpha(best_mask, blur_radius=BLUR_RADIUS)
 
-    # Crop to bounding box
     ys, xs = np.where(best_mask == 255)
     if len(xs) == 0 or len(ys) == 0:
-        return None
+        return None, None
     x0, x1 = xs.min(), xs.max()
     y0, y1 = ys.min(), ys.max()
     pad = int(max(img_rgb.shape[:2]) * 0.02)
@@ -71,16 +62,27 @@ def isolate_with_click(image: Image.Image, evt: gr.SelectData):
     fg_alpha = alpha[y0:y1+1, x0:x1+1]
 
     rgba = np.dstack((fg_rgb, (fg_alpha * 255).astype(np.uint8)))
-    return Image.fromarray(rgba)
+    cutout = Image.fromarray(rgba)
+
+    # Build overlay preview (purple tint on mask)
+    overlay = img_rgb.copy()
+    tint = np.array([180, 0, 180], dtype=np.uint8)  # purple
+    sel = best_mask == 255
+    overlay[sel] = (0.6 * overlay[sel] + 0.4 * tint).astype(np.uint8)
+    overlay_img = Image.fromarray(overlay)
+
+    return cutout, overlay_img
 
 # --- Gradio UI ---
 with gr.Blocks() as demo:
     gr.Markdown("### SAM Object Isolation\nUpload an image, then click on the object to isolate it.")
     inp = gr.Image(type="pil", label="Upload image", interactive=True)
-    out = gr.Image(type="pil", label="Isolated cutout (RGBA)")
-    inp.select(isolate_with_click, inputs=[inp], outputs=out)
+    out_cutout = gr.Image(type="pil", label="Isolated cutout (RGBA)")
+    out_overlay = gr.Image(type="pil", label="Segmentation overlay preview")
 
-    # Add demo example at the bottom
+    inp.select(isolate_with_click, inputs=[inp], outputs=[out_cutout, out_overlay])
+
+    # Demo example at the bottom
     gr.Examples(
         examples=["demo.png"],   # make sure demo.png is in your repo
         inputs=inp,
@@ -88,3 +90,4 @@ with gr.Blocks() as demo:
     )
 
 demo.launch(share=True)
+