Update recommendation.py

recommendation.py

@@ -24,7 +24,7 @@ def row_wise_normalize_and_concatenate(array1, array2):
     normalized_array1 = array1 / np.linalg.norm(array1, axis=1, keepdims=True)
     normalized_array2 = array2 / np.linalg.norm(array2, axis=1, keepdims=True)
 
-    concatenated_array = np.concatenate((normalized_array1, normalized_array2), axis=0)
+    concatenated_array = np.concatenate((normalized_array1, normalized_array2), axis=1)
 
     return concatenated_array
 
@@ -37,7 +37,7 @@ def get_recommendations(image, title, k):
   title_embeds = model.encode([title], normalize_embeddings=True)
   image = transforms.ToTensor()(image.convert("L"))
   image_embeds =  encoder(image).detach().numpy()
-  final_embeds = np.concatenate((image_embeds,title_embeds), axis=0)
+  final_embeds = np.concatenate((image_embeds,title_embeds), axis=1)
 
   candidates = image_embedding_index.topk(final_embeds,k=k)
   # title_candidates = text_embedding_index.topk(title_embeds, k=k)