Update recommendation.py

recommendation.py

@@ -21,14 +21,14 @@ embeddings = torch.load("data/embeddings.bin")
 text_embedding_index = Indexer(embeddings)
 image_embedding_index = Indexer(latent_data)
 
-def get_recommendations(image, title):
+def get_recommendations(image, title, k):
 #   title = [dataset[product_id]['title']]
   title_embeds = model.encode([title], normalize_embeddings=True)
   image = transforms.ToTensor()(image.convert("L"))
   image_embeds =  encoder(image).detach().numpy()
 
-  image_candidates = image_embedding_index.topk(image_embeds)
-  title_candidates = text_embedding_index.topk(title_embeds)
+  image_candidates = image_embedding_index.topk(image_embeds,k=k)
+  title_candidates = text_embedding_index.topk(title_embeds, k=k)
   final_candidates = []
   final_candidates.append(list(image_candidates[0]))
   final_candidates.append(list(title_candidates[0]))