Update recommendation.py

recommendation.py

@@ -36,7 +36,7 @@ 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()
-  image_embeds = image_embeds / np.linalg.norm(image_embeds, axis=1, keepdims=True)
+  # image_embeds = image_embeds / np.linalg.norm(image_embeds, axis=1, keepdims=True)
   final_embeds = np.concatenate((image_embeds,title_embeds), axis=1)
 
   # candidates = index.topk(final_embeds,k=k)