diff --git a/App.py b/App.py
index b437928..3e0da06 100644
--- a/App.py
+++ b/App.py
@@ -124,13 +124,15 @@ def init_demo(): #Demo purpose
                 if (newModel):
                     print_to_console("uczenie sieci neuronowej")
                     model = neuralnetwork.trainNewModel()
-                    neuralnetwork.saveModel(model)
+                    neuralnetwork.saveModel(model, 'model2.pth')
+                    print_to_console("sieć nuronowa nauczona")
                     print('model został wygenerowany')
                 else:
                     model = neuralnetwork.loadModel('model.pth')
                     print_to_console("model został załądowny")
                 testset = neuralnetwork.getDataset(False)
                 print(neuralnetwork.accuracy(model, testset))
+                traktor.snake_move_predict_plant(pole, model)
             start_flag=False
         # demo_move()
         old_info=get_info(old_info)
diff --git a/Image.py b/Image.py
index 732d622..a2adfe5 100644
--- a/Image.py
+++ b/Image.py
@@ -64,4 +64,6 @@ def getRandomImageFromDataBase():
     random_image = random.choice(files)
     imgPath = os.path.join(folderPath, random_image)
 
-    return pygame.image.load(imgPath), label, imgPath
+    image = pygame.image.load(imgPath)
+    image=pygame.transform.scale(image,(dCon.CUBE_SIZE,dCon.CUBE_SIZE))
+    return image, label, imgPath
diff --git a/Slot.py b/Slot.py
index 6f8e30c..69a971a 100644
--- a/Slot.py
+++ b/Slot.py
@@ -46,6 +46,7 @@ class Slot:
             self.plant=Roslina.Roslina(plant_name)
         else:
             self.plant_image, self.label, self.imagePath = self.random_plant_dataset()
+            # print(self.plant_image)
             self.plant=Roslina.Roslina(self.label)
         self.set_image()
 
diff --git a/Tractor.py b/Tractor.py
index 71a0ee9..4a5d10e 100644
--- a/Tractor.py
+++ b/Tractor.py
@@ -9,6 +9,7 @@ import Osprzet
 import Node
 import Condition
 import Drzewo
+import neuralnetwork as nn
 
 condition=Condition.Condition()
 drzewo=Drzewo.Drzewo()
@@ -191,6 +192,23 @@ class Tractor:
                 self.turn_left()
         print("podlanych slotów: ", str(counter))
 
+    def snake_move_predict_plant(self, pole, model):
+        initPos = (self.slot.x_axis, self.slot.y_axis)
+        for i in range(initPos[1], dCon.NUM_Y):
+            for j in range(initPos[0], dCon.NUM_X):
+                if self.slot.imagePath != None:
+                    predictedLabel = nn.predictLabel(self.slot.imagePath, model)
+                    print(str("Coords: ({:02d}, {:02d})").format(self.slot.x_axis, self.slot.y_axis), "real: ", self.slot.label, "predicted: ", predictedLabel, "correct" if (self.slot.label == predictedLabel) else "incorrect")
+                self.move_forward(pole, False)
+            if i % 2 == 0 and i != dCon.NUM_Y - 1:
+                self.turn_right()
+                self.move_forward(pole, False)
+                self.turn_right()
+            elif i != dCon.NUM_Y - 1:
+                self.turn_left()
+                self.move_forward(pole, False)
+                self.turn_left()
+
     def snake_move(self,pole,x,y):
         next_slot_coordinates=(x,y)
         if(self.do_move_if_valid(pole,next_slot_coordinates)):
diff --git a/model.pth b/model.pth
index 90ece27..c177c79 100644
Binary files a/model.pth and b/model.pth differ
diff --git a/neuralnetwork.py b/neuralnetwork.py
index 352f54a..aa21ffa 100644
--- a/neuralnetwork.py
+++ b/neuralnetwork.py
@@ -66,8 +66,8 @@ def getModel():
         ).to(device)
     return model
     
-def saveModel(model):
-    torch.save(model.state_dict(), 'model.pth')
+def saveModel(model, path):
+    torch.save(model.state_dict(), path)
 
 def loadModel(path):
     model = getModel()
@@ -91,12 +91,6 @@ def predictLabel(imagePath, model):
     predicted_class = torch.argmax(output).item()
     return labels[predicted_class]
 
-def predictLabel(image, model):
-    image = preprocess_image(image)
-    with torch.no_grad():
-        model.eval()  # Ustawienie modelu w tryb ewaluacji
-        output = model(image)
-
     # Znalezienie indeksu klasy o największej wartości prawdopodobieństwa
     predicted_class = torch.argmax(output).item()
     return labels[predicted_class]