Update app.py

app.py

@@ -80,16 +80,30 @@ def calculate_selected_score(df, selected_columns):
 
 def get_final_score(df, selected_columns):
     normalize_df = get_normalized_df(df)
-    final_score = normalize_df.drop('name', axis=1).sum(axis=1)
+    #final_score = normalize_df.drop('name', axis=1).sum(axis=1)
+    for name in normalize_df.drop('name', axis=1):
+        normalize_df[name] = normalize_df[name]*DIM_WEIGHT[name]
+    quality_score = normalize_df[QUALITY_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in QUALITY_LIST])
+    semantic_score = normalize_df[SEMANTIC_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in SEMANTIC_LIST ])
+    final_score =  quality_score+ semantic_score
     if 'Overall Score' in df:
         df['Overall Score'] = final_score
     else:
         df.insert(1, 'Overall Score', final_score)
+    final_score =  quality_score+ semantic_score
+    if 'Semantic Score' in df:
+        df['Semantic Score'] = semantic_score
+    else:
+        df.insert(2, 'Semantic Score', semantic_score)
+    if 'Quality Score' in df:
+        df['Quality Score'] = quality_score
+    else:
+        df.insert(3, 'Quality Score', quality_score)
     selected_score = calculate_selected_score(normalize_df, selected_columns)
-    if 'Total Score' in df:
-        df['Total Score'] = selected_score
+    if 'Selected Score' in df:
+        df['Selected Score'] = selected_score
     else:
-        df.insert(1, 'Total Score', selected_score)
+        df.insert(1, 'Selected Score', selected_score)
     return df
 
 def get_baseline_df():