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app.py

@@ -38,6 +38,18 @@ except ImportError:
     print("Warning: RF-DETR not found. Please ensure it's properly installed.")
     RFDETRMedium = None
 
+# Try to import spaces for Hugging Face Spaces GPU support
+try:
+    import spaces
+    HF_SPACES_GPU = True
+except ImportError:
+    # Create a dummy decorator if not running on Spaces
+    class spaces:
+        @staticmethod
+        def GPU(func):
+            return func
+    HF_SPACES_GPU = False
+
 # ============================================================================
 # Configuration for Hugging Face Spaces
 # ============================================================================
@@ -423,6 +435,7 @@ def create_detection_interface():
         "#66ff66", "#99ff00",
     ])
 
+    @spaces.GPU
     def annotate_image(image: Image.Image, threshold: float, model_size: str = "4B") -> Tuple[Image.Image, str]:
         """Process an image and return annotated version with description."""
 
@@ -589,12 +602,18 @@ def main():
     # Ensure results directory exists
     os.makedirs(app_state.config.get('results_dir'), exist_ok=True)
 
-    # Preload all models into VRAM
-    try:
-        app_state.preload_all_models()
-    except Exception as e:
-        print(f"⚠️ Warning: Failed to preload models: {e}")
-        print("Models will be loaded on first use instead")
+    # Preload models if NOT running on HF Spaces with GPU
+    # On HF Spaces, models will be loaded on first inference call (triggered by @spaces.GPU)
+    if not HF_SPACES_GPU:
+        print("Running locally - preloading models into VRAM...")
+        try:
+            app_state.preload_all_models()
+        except Exception as e:
+            print(f"⚠️ Warning: Failed to preload models: {e}")
+            print("Models will be loaded on first use instead")
+    else:
+        print("Running on HF Spaces - models will load on first inference (via @spaces.GPU)")
+        print("This is the recommended approach for Spaces GPU management.")
 
     # Create and launch the interface
     demo = create_detection_interface()

requirements.txt

@@ -2,6 +2,7 @@ torch
 transformers
 huggingface_hub
 gradio
+spaces
 pillow
 opencv-python-headless
 rfdetr