Update models/detection/detector.py

models/detection/detector.py

@@ -28,12 +28,13 @@ class ObjectDetector:
         )
 
     def load_model(self):
-        if self.model is None:
-            from ultralytics import YOLO  # Defer import
-            self.model = YOLO(self.weights_path)
-            if self.device == "cuda":
-                self.model.to("cuda")
-        return self  # So you can chain
+    if self.model is None:
+        from ultralytics import YOLO  # Deferring import to avoid early CUDA trigger
+        self.model = YOLO(self.weights_path)
+        if self.device == "cuda" and torch.cuda.is_available():
+            self.model.to("cuda")  
+    return self
+
 
     def predict(self, image: Image.Image, conf_threshold=0.25):
         self.load_model()