Update app.py

app.py

@@ -45,28 +45,28 @@ class GeneticAlgorithm:
             self.population = [self.population[i] for i in np.argsort(fitness)[-len(self.population)//2:]]
 
     def crossover(self):
-         offspring = []
-    for _ in range(len(self.population)//2):
-        parent1, parent2 = random.sample(self.population, 2)
-        child = Net()
-        child.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-        
-        # Get the weights of the parent networks
-        parent1_weights = parent1.get_weights()
-        parent2_weights = parent2.get_weights()
-        
-        # Average the weights of the two parents
-        child_weights = []
-        for w1, w2 in zip(parent1_weights, parent2_weights):
-            child_weights.append((w1 + w2) / 2)
-        
-        # Create the child instance with the correct weights
-        child = Net()
-        child.set_weights(child_weights)
-        child.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-        
-        offspring.append(child)
-    self.population += offspring
+        offspring = []
+        for _ in range(len(self.population)//2):
+            parent1, parent2 = random.sample(self.population, 2)
+            child = Net()
+            child.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+            
+            # Get the weights of the parent networks
+            parent1_weights = parent1.get_weights()
+            parent2_weights = parent2.get_weights()
+            
+            # Average the weights of the two parents
+            child_weights = []
+            for w1, w2 in zip(parent1_weights, parent2_weights):
+                child_weights.append((w1 + w2) / 2)
+            
+            # Create the child instance with the correct weights
+            child = Net()
+            child.set_weights(child_weights)
+            child.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+            
+            offspring.append(child)
+        self.population += offspring
 
     def mutation(self):
         for net in self.population: