multi output

app.py

@@ -80,13 +80,12 @@ with block:
         cv2.circle(prompt_image, (x, y), 5, color, -1)
         point_coords.append([x,y])
         point_labels.append(point_label_radio)
-        generated_mask, _, _ = sam.cond_pred(pts=np.array(point_coords), lbls=np.array(point_labels))
-        pred_masks = [(generated_mask, text)]
+        sam_masks = sam.cond_pred(pts=np.array(point_coords), lbls=np.array(point_labels))
         return  [ prompt_image,
-                  (input_image, [(generated_mask, "Mask")]),
+                  (input_image, sam_masks),
                   point_coords,
                   point_labels,
-                  pred_masks ]
+                  sam_masks ]
         
     prompt_image.select(on_prompt_image_select,
                        [input_image, prompt_image, point_coords, point_labels, point_label_radio, text, pred_masks],

inference.py

@@ -151,7 +151,10 @@ class SegmentPredictor:
             point_labels=lbls,
             multimask_output=True
             )
-        return masks
+        sam_masks = []
+        for i,mask in enumerate(masks):
+            sam_masks.append((mask["segmentation"], str(i)))
+        return sam_masks
 
 
     def segment_everything(self, image):