Update src/similarity_search.py

src/similarity_search.py

@@ -1,22 +1,24 @@
-import faiss
-import numpy as np
-import pickle
-import os
-
-class SimilaritySearchEngine:
-    def __init__(self, embeddings_path='data/embeddings.pkl'):
-        # Load precomputed embeddings
-        with open(embeddings_path, 'rb') as f:
-            data = pickle.load(f)
-            self.embeddings = data['embeddings']
-            self.image_paths = data['image_paths']
-
-        # Create FAISS index
-        dimension = len(self.embeddings[0])
-        self.index = faiss.IndexFlatL2(dimension)
-        self.index.add(np.array(self.embeddings))
-
-    def search_similar_images(self, query_embedding, top_k=5):
-        # Perform similarity search
-        distances, indices = self.index.search(np.array([query_embedding]), top_k)
+import faiss
+import numpy as np
+import pickle
+import os
+
+class SimilaritySearchEngine:
+    def __init__(self):
+        base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+        embeddings_path = os.path.join(base_dir, 'data', 'embeddings.pkl')
+        
+        with open(embeddings_path, 'rb') as f:
+            data = pickle.load(f)
+            self.embeddings = data['embeddings']
+            # Convert Windows paths to Linux paths
+            self.image_paths = [os.path.normpath(path).replace('\\', '/') 
+                              for path in data['image_paths']]
+
+        dimension = len(self.embeddings[0])
+        self.index = faiss.IndexFlatL2(dimension)
+        self.index.add(np.array(self.embeddings))
+
+    def search_similar_images(self, query_embedding, top_k=5):
+        distances, indices = self.index.search(np.array([query_embedding]), top_k)
         return [self.image_paths[idx] for idx in indices[0]], distances[0]