Update app.py

app.py

@@ -80,17 +80,16 @@ def calculate_selected_score(df, selected_columns):
     selected_SEMANTIC = [i for i in selected_columns if i in SEMANTIC_LIST]
     selected_quality_score = df[selected_QUALITY].sum(axis=1)/sum([DIM_WEIGHT[i] for i in selected_QUALITY])
     selected_semantic_score = df[selected_SEMANTIC].sum(axis=1)/sum([DIM_WEIGHT[i] for i in selected_SEMANTIC ])
+    if selected_quality_score.isna().any().any() and selected_semantic_score.isna().any().any():
+        selected_score =  (selected_quality_score * QUALITY_WEIGHT + selected_semantic_score * SEMANTIC_WEIGHT) / (QUALITY_WEIGHT + SEMANTIC_WEIGHT)
+        return selected_score.fillna(0.0)
     if selected_quality_score.isna().any().any():
         return selected_semantic_score
     if selected_semantic_score.isna().any().any():
         return selected_quality_score
     # print(selected_semantic_score,selected_quality_score )
     selected_score =  (selected_quality_score * QUALITY_WEIGHT + selected_semantic_score * SEMANTIC_WEIGHT) / (QUALITY_WEIGHT + SEMANTIC_WEIGHT)
-    
-    if selected_score.isna().any().any():
-        selected_score = [0.0 for _ in range(len(selected_score))]
-    print( selected_score.isna().any().any(),selected_score)
-    return selected_score
+    return selected_score.fillna(0.0)
 
 def get_final_score(df, selected_columns):
     normalize_df = get_normalized_df(df)