Update app.py

app.py

@@ -411,27 +411,27 @@ class Engine(object):
         return self.load_image()
 
     def get_model_average(self, which, all=False, final=False):
-        aux, i = [], self.index+int(not final)
+        aux, i = [], self.index
         if which == 'user':
             avg_score = sum(self.stats['scores']) / len(self.stats['scores']) if self.stats['scores'] else 0
             avg_distance = sum(self.stats['distances']) / len(self.stats['distances']) if self.stats['distances'] else 0
-            avg_country_accuracy = (0 if self.df['country_val'].iloc[:i].sum() == 0 else sum(self.stats['country'])/self.df['country_val'].iloc[:i].sum())*100
+            avg_country_accuracy = (0 if self.df['country_val'].iloc[:i+1].sum() == 0 else sum(self.stats['country'])/self.df['country_val'].iloc[:i+1].sum())*100
             if all:
-                avg_city_accuracy = (0 if self.df['city_val'].iloc[:i].sum() == 0 else sum(self.stats['city'])/self.df['city_val'].iloc[:i].sum())*100
-                avg_area_accuracy = (0 if self.df['area_val'].iloc[:i].sum() == 0 else sum(self.stats['area'])/self.df['area_val'].iloc[:i].sum())*100
-                avg_region_accuracy = (0 if self.df['region_val'].iloc[:i].sum() == 0 else sum(self.stats['region'])/self.df['region_val'].iloc[:i].sum())*100
+                avg_city_accuracy = (0 if self.df['city_val'].iloc[:i+1].sum() == 0 else sum(self.stats['city'])/self.df['city_val'].iloc[:i+1].sum())*100
+                avg_area_accuracy = (0 if self.df['area_val'].iloc[:i+1].sum() == 0 else sum(self.stats['area'])/self.df['area_val'].iloc[:i+1].sum())*100
+                avg_region_accuracy = (0 if self.df['region_val'].iloc[:i+1].sum() == 0 else sum(self.stats['region'])/self.df['region_val'].iloc[:i+1].sum())*100
                 aux = [avg_city_accuracy, avg_area_accuracy, avg_region_accuracy]
             which = 'You'
         elif which == 'base':
-            avg_score = np.mean(self.df[['score_base']].iloc[:i])
-            avg_distance = np.mean(self.df[['distance_base']].iloc[:i])
+            avg_score = np.mean(self.df[['score_base']].iloc[:i+1])
+            avg_distance = np.mean(self.df[['distance_base']].iloc[:i+1])
             avg_country_accuracy = self.df['accuracy_country_base'].iloc[i]
             if all:
                 aux = [self.df['accuracy_city_base'].iloc[i], self.df['accuracy_area_base'].iloc[i], self.df['accuracy_region_base'].iloc[i]]
             which = 'Baseline-AI'
         elif which == 'best':
-            avg_score = np.mean(self.df[['score']].iloc[:i])
-            avg_distance = np.mean(self.df[['distance']].iloc[:i])
+            avg_score = np.mean(self.df[['score']].iloc[:i+1])
+            avg_distance = np.mean(self.df[['distance']].iloc[:i+1])
             avg_country_accuracy = self.df['accuracy_country'].iloc[i]
             if all:
                 aux = [self.df['accuracy_city_base'].iloc[i], self.df['accuracy_area_base'].iloc[i], self.df['accuracy_region_base'].iloc[i]]