Update app.py

app.py

@@ -94,18 +94,17 @@ if st.button("Run evolution"):
         st.write(f"Generation {generation+1} complete")
 
     # Evaluate the final population
-    final_accuracy = []
-    for task_id, ga in enumerate(gas):
-        X_train, X_test, y_train, y_test = generate_dataset(task_id)
-        accuracy = []
-        for net in ga.population:
-            net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-            net.build(input_shape=(None, 10))  # Compile the model before training
-            net.fit(X_train, y_train, epochs=10, verbose=0)
-            loss, acc = net.evaluate(X_test, y_test, verbose=0)
+final_accuracy = []
+for task_id, ga in enumerate(gas):
+    X_train, X_test, y_train, y_test = generate_dataset(task_id)
+    accuracy = []
+    for net in ga.population:
+        net.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+        net.fit(X_train, y_train, epochs=10, verbose=0)
+        loss, acc = net.evaluate(X_test, y_test, verbose=0)
         accuracy.append(acc)
-    final_accuracy.append(np.mean(accuracy))
-st.write(f"Final accuracy: {np.mean(final_accuracy)}")
+    if len(accuracy) > 0:
+        final_accuracy.append(np.mean(accuracy))
 
         
 
@@ -134,4 +133,7 @@ for task_id, ga in enumerate(gas):
         loss, acc = net.evaluate(X_test, y_test, verbose=0)
         accuracy.append(acc)
     final_accuracy_after_trade.append(np.mean(accuracy))
+if len(final_accuracy) > 0:
+st.write(f"Final accuracy: {np.mean(final_accuracy)}")
 st.write(f"Final accuracy after trading: {np.mean(final_accuracy_after_trade)}")
+