update for bboxes

app/main.py

@@ -176,6 +176,8 @@ async def get_multi_label_preds(image: str, q: MaskLabel) -> MaskBoxLabels:
     start = time.perf_counter()
     masks, bboxes, _ = per_sam_model(image_np)
     print(f"inference time {time.perf_counter() - start}")
+    if masks is None:
+        return MaskBoxLabels(masks=[], bboxes=[], labels=[])
     masks_out = []
     for i in range(len(masks)):
         mask_i = Image.fromarray(masks[i])

app/per_sam/model.py

@@ -101,7 +101,7 @@ def fast_inference(
     max_objects: int,
     score_thresh: float,
     nms_iou_thresh: float = 0.2,
-) -> tuple[npt.NDArray, npt.NDArray, npt.NDArray]:
+) -> tuple[npt.NDArray | None, npt.NDArray | None, npt.NDArray | None]:
     weights_np = weights.detach().cpu().numpy()
     pred_masks = []
     pred_scores = []
@@ -190,6 +190,9 @@ def fast_inference(
         pred_masks.append(final_mask)
         pred_scores.append(score)
 
+    if len(pred_masks) == 0:
+        return None, None, None
+
     pred_masks = torch.stack(pred_masks)
     bboxes = batched_mask_to_box(pred_masks)
     keep_by_nms = batched_nms(