some code cleaning

src/tile.py

@@ -52,7 +52,6 @@ class Tile(pygame.sprite.Sprite):
 
             self.prediction = self.predict(model, image_transforms, self.image_path, classes)
             
-
         else:
             if random.randint(1, 10) % 3 == 0:
                 self.set_type('water')
@@ -79,9 +78,7 @@ class Tile(pygame.sprite.Sprite):
         files = [f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]
         random_file = random.choice(files)
 
-        #image_path = os.path.join(folder_path, random_file)
         image_path = folder_path + "/" + random_file
-        #print(image_path)
         return image_path
 
     def set_type(self, type):
@@ -110,7 +107,6 @@ class Tile(pygame.sprite.Sprite):
         output = model(image)
         _, predicted = torch.max(output.data, 1)
 
-        #print("Rozpoznano: ", classes[predicted.item()])
         result = classes[predicted.item()]
         if result == "ziemniak":
             result = 'marchew'

src/veggies_recognition/predict.py

@@ -1,36 +0,0 @@
-# import torch
-# import torchvision.transforms as transforms
-# from PIL import Image
-
-# classes = [
-#         "bób", "brokuł", "brukselka", "burak", "cebula",
-#         "cukinia", "dynia", "fasola", "groch", "jarmuż",
-#         "kalafior", "kalarepa", "kapusta", "marchew", 
-#         "ogórek", "papryka", "pietruszka", "pomidor",
-#         "por", "rzepa", "rzodkiewka", "sałata", "seler",
-#         "szpinak", "ziemniak"]
-
-# model = torch.load("best_model.pth")
-
-# mean = [0.5322, 0.5120, 0.3696]
-# std = [0.2487, 0.2436, 0.2531]
-
-# image_transforms = transforms.Compose([
-#     transforms.Resize((224, 224)),
-#     transforms.ToTensor(),
-#     transforms.Normalize(torch.Tensor(mean),torch.Tensor(std))
-# ])
-
-# def predict(model, image_transforms, image_path, classes):
-#     model = model.eval()
-#     image = Image.open(image_path)
-#     print(image_path)
-#     image = image_transforms(image).float()
-#     image = image.unsqueeze(0)
-
-#     output = model(image)
-#     _, predicted = torch.max(output.data, 1)
-
-#     print(classes[predicted.item()])
-
-# predict(model, image_transforms, "veggies/marchew_118.jpg", classes)