restored with prints

app.py

@@ -211,8 +211,10 @@ def get_sorted_cosine_similarity(embeddings_metadata):
         # TODO: Compute cosine similarity between input sentence and categories
         # TODO: Update category embeddings if category not found  
         ##########################################
-        #cat = categories[index]
-        cat_embed = category_embeddings[index]
+        cat = categories[index]
+        print('categories', categories)
+        print('cat_embeddings', category_embeddings)
+        cat_embed = category_embeddings[cat]
         # Calc cosine sim
         cat_scores.append((index, np.dot(input_embedding,cat_embed)))
         # Store doc_id and score as a tuple