testing returning the bbox payload

app.py

@@ -17,13 +17,28 @@ subprocess.run(
 
 from ultralytics import YOLO
 
+def results_to_detections(results):
+    detections = []
+    for result in results:
+        boxes = result.boxes.data  # [num_det, 6] (x1, y1, x2, y2, conf, cls)
+        for box in boxes:
+            x1, y1, x2, y2, conf, cls = box.tolist()
+            detections.append({
+                "class_id": int(cls),
+                "class_name": result.names[int(cls)],
+                "confidence": float(conf),
+                "bbox": [x1, y1, x2, y2]  # XYXY format
+            })
+    return detections
+
 @spaces.GPU
 def yolov12_inference(image, video, model_id, image_size, conf_threshold):
     model = YOLO(model_id)
     if image:
         results = model.predict(source=image, imgsz=image_size, conf=conf_threshold)
         annotated_image = results[0].plot()
-        return annotated_image[:, :, ::-1], None
+        dets = results_to_detections(results)
+        return annotated_image[:, :, ::-1], None, dets
     else:
         video_path = tempfile.mktemp(suffix=".webm")
         with open(video_path, "wb") as f:
@@ -50,7 +65,7 @@ def yolov12_inference(image, video, model_id, image_size, conf_threshold):
         cap.release()
         out.release()
 
-        return None, output_video_path
+        return None, output_video_path, None
 
 
 def yolov12_inference_for_examples(image, model_path, image_size, conf_threshold):