fix attempt

app.py

@@ -125,18 +125,17 @@ def averaged_glove_embeddings_gdrive(sentence, word_index_dict, embeddings, mode
     ##################################
     ##### TODO: Add code here ########
     ##################################
-    for word in sentence:
-        #print(sentence)
-        words = [word.strip('.,?!').lower() for word in sentence.split()]
-        total = 0
-        for w in words:
-            if w in embeddings:
-                embed += embeddings[w]
-                total +=1
-        if total != 0:
-            embed = embed/total
+   
+    words = [word.strip('.,?!').lower() for word in sentence.split()]
+    total = 0
+    for w in words:
+        if w in word_index_dict:
+            embedding += embeddings[word_index_dict[w]]
+            total +=1
+    if total != 0:
+        embedding = embedding/total
     
-    return embed
+    return embedding
 
 
 def get_category_embeddings(embeddings_metadata):
@@ -204,34 +203,31 @@ def get_sorted_cosine_similarity(embeddings_metadata):
             input_embedding = get_sentence_transformer_embeddings(st.session_state.text_search, model_name=model_name)
         else:
             input_embedding = get_sentence_transformer_embeddings(st.session_state.text_search)
-        for index in range(len(categories)):
-            
-            ##########################################
-            # TODO: Compute cosine similarity between input sentence and categories
-            # TODO: Update category embeddings if category not found  
-            ##########################################
-            cat_scores = []
-            cat_idx = 0
-            for cat_embed in category_embeddings:
-                # Calc cosine sim
-                cat_scores.append((cat_idx, np.dot(input,cat_embed)))
-                # Store doc_id and score as a tuple
-                cat_idx +=1
-                
-           
-            sorted_list = sorted(cat_scores, key=lambda x: x[1])
+    
+    cat_scores = []
+    for index in range(len(categories)):
+        ##########################################
+        # 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]
+        # Calc cosine sim
+        cat_scores.append((cat, np.dot(input_embedding,cat_embed)))
+        # Store doc_id and score as a tuple
             
-            sorted_cats = [element[0] for element in sorted_list]
-           
-            #flip sorting order
-            sorted_cats = sorted_cats[::-1]
-            # Add list to Map
-            result = sorted_cats[0]
-            selected_cat = categories[result]
-            print(selected_cat)
-
+        
+    sorted_list = sorted(cat_scores, key=lambda x: x[1])
+    
+    sorted_cats = [element[0] for element in sorted_list]
+    
+    #flip sorting order
+    sorted_cats = sorted_cats[::-1]
+    # Add list to Map
+    for cat_pair in sorted_cats:
+        cosine_sim[cat_pair[0]] = cat_pair[1]
 
-    return selected_cat
+    return cosine_sim
 
 
 def plot_piechart(sorted_cosine_scores_items):