x

app.py

@@ -69,17 +69,25 @@ def predict_masks(image, points):
         return image  # Return the original image if no points are selected
     PREDICTOR = SAM2ImagePredictor.from_pretrained(SAM_MODEL, device=DEVICE)
 
-    image_np = np.array(image)
-    points_list = [[point["x"], point["y"]] for point in points]
+    # Debugging: Print the structure of points
+    print(f"Points structure: {points}")
+
+    # Ensure points is a list of lists with at least two elements
+    if isinstance(points, list) and all(isinstance(point, list) and len(point) >= 2 for point in points):
+        points_list = [[point[0], point[1]] for point in points]
+    else:
+        return image  # Return the original image if points structure is unexpected
+
     input_labels = [1] * len(points_list)
 
     with torch.inference_mode():
-        PREDICTOR.set_image(image_np)
+        PREDICTOR.set_image(np.array(image))
         masks, _, _ = PREDICTOR.predict(
             point_coords=points_list, point_labels=input_labels, multimask_output=False
         )
 
     # Prepare the overlay image
+    image_np = np.array(image)
     red_mask = np.zeros_like(image_np)
     if masks and len(masks) > 0:
         red_mask[:, :, 0] = masks[0].astype(np.uint8) * 255  # Apply the red channel
@@ -90,7 +98,6 @@ def predict_masks(image, points):
     else:
         return image_np
 
-
 def update_mask(prompts):
     """Update the mask based on the prompts."""
     image = prompts["image"]