changed indexing into categories

app.py

@@ -187,9 +187,9 @@ def get_sorted_cosine_similarity(embeddings_metadata):
         ##########################################
         ## TODO: Get embeddings for categories ###
         ##########################################
-        category_embeddings = []
+        category_embeddings = {}
         for cat in categories:
-            category_embeddings.append(get_glove_embeddings(cat, word_index_dict, embeddings, model_type))
+            category_embeddings[cat] = get_glove_embeddings(cat, word_index_dict, embeddings, model_type)
 
     else:
         model_name = embeddings_metadata["model_name"]
@@ -211,8 +211,8 @@ 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_embed = category_embeddings[index]
         cat = categories[index]
+        cat_embed = category_embeddings[cat]
         # Calc cosine sim
         cat_scores.append((cat, np.dot(input_embedding,cat_embed)))
         # Store doc_id and score as a tuple