fixed return types

vision_agent/tools/tools.py

@@ -185,6 +185,7 @@ def owl_v2_image(
  "function_name": "owl_v2",
  }
  data: Dict[str, Any] = send_inference_request(request_data, "owlv2", v2=True)
+ print(data)
  return_data = []
  if data is not None:
  for elt in data:
@@ -246,21 +247,23 @@ def owl_v2_video(
  data: Dict[str, Any] = send_inference_request(
  payload, "text-to-object-detection", files=files, v2=True
  )
- return_data = []
+ print(data)
+ bboxes_formatted = []
  if data is not None:
  for frame_data in data:
- return_data_frame = []
+ bboxes_formated_frame = []
  for elt in frame_data:
- return_data_frame.append(
+ bboxes_formated_frame.append(
  ODResponseData(
- label=elt["label"],
- bbox=normalize_bbox(elt["bbox"], image_size),
- score=round(elt["score"], 2),
+ label=elt["label"], # type: ignore
+ bbox=normalize_bbox(elt["bounding_box"], image_size), # type: ignore
+ score=round(elt["score"], 2), # type: ignore
  )
  )
- return_data.append(return_data_frame)
+ bboxes_formatted.append(bboxes_formated_frame)
 
- return return_data
+ filtered_bboxes = [filter_bboxes_by_threshold(elt, box_threshold) for elt in bboxes_formatted]
+ return [[bbox.model_dump() for bbox in frame] for frame in filtered_bboxes]
 
 
 def grounding_sam(