Update app.py

app.py

@@ -103,10 +103,22 @@ def get_image_embedding(image_path):
         with torch.no_grad():
             image_embedding = model.get_image_features(**inputs).numpy().flatten()
         
-        # Check if the embedding dimension is already 384
-        if len(image_embedding) != 384:
-            # Ensure PCA transformation gets the correct shape
+         # Print the actual embedding dimension
+        print(f"Image embedding shape: {image_embedding.shape}")
+
+        # CASE 1: Embedding is already 384-dimensional ✅
+        if len(image_embedding) == 384:
+            return image_embedding.tolist()
+
+        # CASE 2: Embedding is larger than 384 (e.g., 512) → Apply PCA ✅
+        elif len(image_embedding) > 384:
+            pca = PCA(n_components=384)
             image_embedding = pca.fit_transform(image_embedding.reshape(1, -1)).flatten()
+
+        # CASE 3: Embedding is smaller than 384 → Apply Padding ❌
+        else:
+            padding = np.zeros(384 - len(image_embedding))  # Create padding vector
+            image_embedding = np.concatenate((image_embedding, padding))  # Append padding
         
         return image_embedding.tolist()