Update src/comparison.py

src/comparison.py

@@ -17,6 +17,32 @@ except ImportError:
     print("  Install with: pip install transformers")
     CLIP_AVAILABLE = False
 
+_GLOBAL_CLIP_MODEL = None
+_GLOBAL_CLIP_PROCESSOR = None
+
+
+def get_clip_model():
+    """Get or initialize global CLIP model"""
+    global _GLOBAL_CLIP_MODEL, _GLOBAL_CLIP_PROCESSOR
+
+    if _GLOBAL_CLIP_MODEL is None and CLIP_AVAILABLE:
+        try:
+            model_name = "openai/clip-vit-base-patch32"
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            _GLOBAL_CLIP_MODEL = CLIPModel.from_pretrained(model_name).to(device)
+            _GLOBAL_CLIP_PROCESSOR = CLIPProcessor.from_pretrained(model_name)
+            _GLOBAL_CLIP_MODEL.eval()
+
+            for param in _GLOBAL_CLIP_MODEL.parameters():
+                param.requires_grad = False
+
+            print(f"✓ CLIP model loaded for ReID: {model_name}")
+        except Exception as e:
+            print(f"⚠ CLIP loading failed: {e}. Using fallback features.")
+            _GLOBAL_CLIP_MODEL = None
+            _GLOBAL_CLIP_PROCESSOR = None
+
+    return _GLOBAL_CLIP_MODEL, _GLOBAL_CLIP_PROCESSOR
 
 class DamageComparator:
     """Enhanced damage comparator with view-invariant re-identification"""
@@ -32,32 +58,14 @@ class DamageComparator:
         # Device selection
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-        # Initialize CLIP if available
-        self.clip_model = None
-        self.clip_processor = None
-        if CLIP_AVAILABLE:
-            self._init_clip_model()
+        # Get global CLIP model instead of creating new one
+        self.clip_model, self.clip_processor = get_clip_model()
 
         # ReID thresholds
-        self.reid_similarity_threshold = 0.6  # For matching same damage across views
-        self.feature_cache = {}  # Cache extracted features
-
-    def _init_clip_model(self):
-        """Initialize CLIP model for view-invariant features"""
-        try:
-            model_name = "openai/clip-vit-base-patch32"  # Smaller model for speed
-            self.clip_model = CLIPModel.from_pretrained(model_name).to(self.device)
-            self.clip_processor = CLIPProcessor.from_pretrained(model_name)
-            self.clip_model.eval()
+        self.reid_similarity_threshold = 0.6
+        self.feature_cache = {}
 
-            # Freeze parameters
-            for param in self.clip_model.parameters():
-                param.requires_grad = False
 
-            print(f"✓ CLIP model loaded for ReID: {model_name}")
-        except Exception as e:
-            print(f"⚠ CLIP loading failed: {e}. Using fallback features.")
-            self.clip_model = None
 
     def calculate_iou(self, box1: List[int], box2: List[int]) -> float:
         """Calculate Intersection over Union between two boxes"""