Update app.py

app.py

@@ -35,15 +35,18 @@ class GeneticAlgorithm:
         self.population = [Net() for _ in range(population_size)]
 
     def selection(self):
-        X_train, X_test, y_train, y_test = generate_dataset(self.task_id)
-        fitness = []
-        for net in self.population:
-            net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-            net.fit(X_train, y_train, epochs=10, verbose=0)
-            loss, accuracy = net.evaluate(X_test, y_test, verbose=0)
-            fitness.append(accuracy)
+    X_train, X_test, y_train, y_test = generate_dataset(self.task_id)
+    fitness = []
+    for net in self.population:
+        net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+        net.fit(X_train, y_train, epochs=10, verbose=0)
+        loss, accuracy = net.evaluate(X_test, y_test, verbose=0)
+        fitness.append(accuracy)
+    if len(fitness) > 0:
         self.population = [self.population[i] for i in np.argsort(fitness)[-self.population_size//2:]]
-
+    else:
+        print("Fitness list is empty. Cannot select individuals.")
+   
     def crossover(self):
         offspring = []
         X = np.random.rand(1, 10)  # dummy input to build the layers