update scripts

app.py

@@ -38,7 +38,7 @@ def load_data(data_path):
     df = pd.read_csv(data_path, skiprows=1, header=0).dropna()
 
     columns = ['Model', 'type', 'open?', 'shot', 'en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']
-    columns_sorted = ['Model', 'type', 'open?', 'shot', 'avg', 'avg_sea', 'en', 'zh', 'id', 'th', 'vi']
+    
 
     # Splitting into three separate DataFrames based on the groups M3Exam and MMLU and average
     df_m3exam = df.iloc[:, :11]  # M3Exam columns
@@ -51,9 +51,10 @@ def load_data(data_path):
     for df_tmp in [df_m3exam, df_mmlu, df_avg]:
         df_tmp[['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']] *= 100
         df_tmp[['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']] = df_tmp[['en', 'zh', 'id', 'th', 'vi', 'avg', 'avg_sea']].round(2)
-
+        df_tmp['rank'] = df_tmp['avg'].rank(ascending=False).astype(int)
     # change the order of the columns to ['Model', 'type', 'open?', 'shot', 'avg', 'avg_sea', 'en', 'zh', 'id', 'th', 'vi'] 
     # and sort the columns by 'avg' in descending order
+    columns_sorted = ['rank','Model', 'type', 'open?', 'shot', 'avg', 'avg_sea', 'en', 'zh', 'id', 'th', 'vi']
     df_m3exam = df_m3exam[columns_sorted].sort_values(by='avg', ascending=False)
     df_mmlu = df_mmlu[columns_sorted].sort_values(by='avg', ascending=False)
     df_avg = df_avg[columns_sorted].sort_values(by='avg', ascending=False)