Update app.py

app.py

@@ -442,7 +442,7 @@ def get_final_score(df, selected_columns):
         for name in normalize_df.drop('Model Name (clickable)', axis=1).drop("Sampled by", axis=1).drop('Mail', axis=1).drop('Date',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 ])
+    semantic_score = normalize_df[SEMANTIC_LIST].sum(axis=1)/sum([DIM_WEIGHT[i] for i in SEMANTIC_LIST])
     final_score =  (quality_score * QUALITY_WEIGHT + semantic_score * SEMANTIC_WEIGHT) / (QUALITY_WEIGHT + SEMANTIC_WEIGHT)
     if 'Total Score' in df:
         df['Total Score'] = final_score
@@ -658,9 +658,9 @@ def enable_all():
 def on_filter_model_size_method_change(selected_columns, vbench_team_sample, vbench_team_eval=False):
     updated_data = get_all_df(selected_columns, CSV_DIR)
     if vbench_team_sample:
-        updated_data = updated_data[updated_data["Sampled by"] == 'VBench Team'] 
+        updated_data = updated_data[updated_data["Sampled by"].str.contains('VBench Team')]
     if vbench_team_eval:
-        updated_data = updated_data[updated_data['Evaluated by'] == 'VBench Team']
+        updated_data =  updated_data[updated_data["Evaluated by"].str.contains('VBench Team')]
     #print(updated_data)
     # columns:
     selected_columns = [item for item in TASK_INFO if item in selected_columns]