no-log mode

agent.py

@@ -16,12 +16,12 @@ engine = Engine(SCREEN_SIZE, SQUARE_SIZE, kitchen, waiter, ACTION_DURATION)
 layout = LayoutController(engine, store).create_and_subscribe(COUNT_OF_OBJECTS)
 
 
-engine.loop()
+# engine.loop()
 
-'''
+# '''
 def example_stop(action_clock: int) -> bool:
     return action_clock < 1000
 
 
 print(engine.train_loop(example_stop))
-'''
+# '''

src/Engine.py

@@ -83,12 +83,14 @@ class Engine:
         real_action_duration = self.action_duration
         self.action_duration = 0
         self.is_simulation = True
+        self.predictor_c.is_simulation = True
 
         while stop_condition(self.action_clock):
             self.action()
 
         self.action_duration = real_action_duration
         self.is_simulation = False
+        self.predictor_c.is_simulation = False
 
         return self.serviced_tables
 
@@ -157,6 +159,7 @@ class Engine:
         self.goal = None
 
     def unattainable_goal(self):
+        if not self.is_simulation:
             print(colored("Object unattainable", "red"))
         self.objects.remove(self.goal.parent)
         self.revoke_goal()

src/controller/NeuralNetworkController.py

@@ -28,6 +28,8 @@ class NeuralNetworkController:
         # STEP 5. Set up path to the folder containing test images
         self.test_images_folder = 'dataset/testset'
 
+        self.is_simulation = False
+
     def predict(self, image_path):
         # STEP 1. Load and preprocess the test image
         test_image = keras.preprocessing.image.load_img(
@@ -44,6 +46,7 @@ class NeuralNetworkController:
         predicted_class_index = np.argmax(predictions[0])
         predicted_class = self.class_names[predicted_class_index]
 
+        if not self.is_simulation:
             print(colored("Predicted class: ", "yellow")+f"{predicted_class}")
         return predicted_class
 

src/controller/StateController.py

@@ -31,6 +31,7 @@ class StateController:
             self.path.append(self.path[-1].parent)
             total_cost += self.path[-1].cost
 
+        if not engine.is_simulation:
             print(colored(f"Total path cost: ", "green")+f"{total_cost}")
         return self.path
 
@@ -41,6 +42,7 @@ class StateController:
         # STEP 1. Store goal position
         self.goal = engine.goal.position
 
+        if not engine.is_simulation:
             print(colored(f"Search path to ", "yellow")+f"{self.goal}")
 
         # STEP 2. Reset structures
@@ -73,6 +75,7 @@ class StateController:
         # STEP 9. Reset structures
         self.reset()
 
+        if not engine.is_simulation:
             print(colored("Not found", "red"))
 
         return False