diff --git a/NeuralNetwork/prediction.py b/NeuralNetwork/prediction.py
new file mode 100644
index 0000000..b2a63a4
--- /dev/null
+++ b/NeuralNetwork/prediction.py
@@ -0,0 +1,147 @@
+import torch
+import torch.nn as nn
+from torchvision.transforms import transforms
+import numpy as np
+from torch.autograd import Variable
+from torchvision.models import squeezenet1_1
+import torch.functional as F
+from io import open
+import os
+from PIL import Image
+import pathlib
+import glob
+from tkinter import Tk, Label
+from PIL import Image, ImageTk
+
+absolute_path = os.path.abspath('NeuralNetwork/src/train_images')
+train_path = absolute_path
+absolute_path = os.path.abspath('Images/Items_test')
+pred_path = absolute_path
+
+root=pathlib.Path(train_path)
+classes=sorted([j.name.split('/')[-1] for j in root.iterdir()])
+
+
+class DataModel(nn.Module):
+    def __init__(self, num_classes):
+        super(DataModel, self).__init__()
+        #input (batch=256, nr of channels rgb=3 , size=244x244)
+
+        # convolution
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
+        #shape (256, 12, 224x224)
+
+        # batch normalization
+        self.bn1 = nn.BatchNorm2d(num_features=12)
+        #shape (256, 12, 224x224)
+        self.reul1 = nn.ReLU()
+
+        self.pool=nn.MaxPool2d(kernel_size=2, stride=2)
+        # reduce image size by factor 2
+        # pooling window moves by 2 pixels at a time instead of 1
+        # shape (256, 12, 112x112)
+
+
+
+        self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
+        self.bn2 = nn.BatchNorm2d(num_features=24)
+        self.reul2 = nn.ReLU()
+        # shape (256, 24, 112x112)
+
+        self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1)
+        #shape (256, 48, 112x112)
+        self.bn3 = nn.BatchNorm2d(num_features=48)
+        #shape (256, 48, 112x112)
+        self.reul3 = nn.ReLU()
+
+        # connected layer
+        self.fc = nn.Linear(in_features=48*112*112, out_features=num_classes)
+
+    def forward(self, input):
+            output = self.conv1(input)
+            output = self.bn1(output)
+            output = self.reul1(output)
+
+            output = self.pool(output)
+            output = self.conv2(output)
+            output  = self.bn2(output)
+            output = self.reul2(output)
+            
+            output = self.conv3(output)
+            output = self.bn3(output)
+            output = self.reul3(output)
+
+            # output shape matrix (256, 48, 112x112)
+            #print(output.shape)
+            #print(self.fc.weight.shape)
+
+            output = output.view(-1, 48*112*112)
+            output = self.fc(output)
+            
+            return output
+
+script_dir = os.path.dirname(os.path.abspath(__file__))
+file_path = os.path.join(script_dir, 'best_model.pth')
+checkpoint=torch.load(file_path)
+model = DataModel(num_classes=2)
+model.load_state_dict(checkpoint)
+model.eval()
+
+transformer = transforms.Compose([
+    transforms.Resize((224, 224)),  # Resize images to (224, 224)
+    transforms.ToTensor(),  # Convert images to tensors, 0-255 to 0-1
+    # transforms.RandomHorizontalFlip(),  # 0.5 chance to flip the image
+    transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5]) 
+])
+
+def prediction(img_path,transformer):
+    
+    image=Image.open(img_path)
+    
+    image_tensor=transformer(image).float()
+    
+    image_tensor=image_tensor.unsqueeze_(0)
+    
+    if torch.cuda.is_available():
+        image_tensor.cuda()
+        
+    input=Variable(image_tensor)
+    
+    output=model(input)
+    
+    index=output.data.numpy().argmax()
+    
+    pred=classes[index]
+    
+    return pred
+
+def prediction_keys():
+
+    #funkcja zwracajaca sciezki do kazdego pliku w folderze w postaci listy
+
+    images_path=glob.glob(pred_path+'/*.jpg')
+
+    pred_list=[]
+
+    for i in images_path:
+        pred_list.append(i)
+
+    return pred_list
+
+def predict_one(path):
+
+    #wyswietlanie obrazka po kazdym podniesieniu itemu
+    root = Tk()
+    root.title("Okno z obrazkiem")
+
+    image = Image.open(path)
+    photo = ImageTk.PhotoImage(image)
+    label = Label(root, image=photo)
+    label.pack()
+
+    root.mainloop()
+
+    #uruchamia sie funkcja spr czy obrazek to paczka czy list
+    pred_print = prediction(path,transformer)
+    print('Zdjecie jest: '+pred_print)
+    return pred_print
\ No newline at end of file
diff --git a/images/Items_test/test1.jpg b/images/Items_test/test1.jpg
new file mode 100644
index 0000000..633b417
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diff --git a/images/Items_test/test2.jpg b/images/Items_test/test2.jpg
new file mode 100644
index 0000000..4fe00d6
Binary files /dev/null and b/images/Items_test/test2.jpg differ
diff --git a/images/Items_test/test3.jpg b/images/Items_test/test3.jpg
new file mode 100644
index 0000000..2e4d2e5
Binary files /dev/null and b/images/Items_test/test3.jpg differ
diff --git a/images/Items_test/test4.jpg b/images/Items_test/test4.jpg
new file mode 100644
index 0000000..63c28eb
Binary files /dev/null and b/images/Items_test/test4.jpg differ
diff --git a/letter.py b/letter.py
index 98c1deb..b5b24da 100644
--- a/letter.py
+++ b/letter.py
@@ -3,9 +3,9 @@ import pygame
 letter_pic = pygame.image.load("images/letter.png")
 
 class Letter(pygame.sprite.Sprite):
-    def __init__(self, id):
+    def __init__(self, img_path):
         super().__init__()
-        self.id = id
+        self.img_path = img_path
         self.image = pygame.transform.scale(letter_pic, (40, 40))
         self.rect = self.image.get_rect()
         self.x = 430
diff --git a/main.py b/main.py
index b78c014..eede3df 100644
--- a/main.py
+++ b/main.py
@@ -9,6 +9,7 @@ import wyszukiwanie
 import ekran
 from grid import GridCellType, SearchGrid
 import plansza
+import NeuralNetwork.prediction as prediction
 
 
 from plansza import a_pix, b_pix
@@ -17,10 +18,11 @@ pygame.init()
 
 def main():
     wozek = Wozek()
-    p1 = Paczka('duzy', 12, 'narzedzia', False, True, False, any, any, any, any, any)
-    p2 = Paczka('maly', 1, 'ogród', False, True, False, any, any, any, any, any)
-    l1 = Letter(1)
-    l2 = Letter(2)
+    pred_list = prediction.prediction_keys()
+    p1 = Paczka('duzy', 12, 'narzedzia', False, True, False, any, any, any, any, any, pred_list[3])
+    p2 = Paczka('maly', 1, 'ogród', False, True, False, any, any, any, any, any, pred_list[1])
+    l1 = Letter(pred_list[0])
+    l2 = Letter(pred_list[2])
     ekran.dodaj_na_rampe(p2, l1, p1, l2)
     grid_points = SearchGrid()
 
@@ -54,7 +56,7 @@ def main():
                                 wozek.dynamic_wozek_picture()
                                                       
                                 przenoszony_item = wozek.storage[-1]
-                                if isinstance(przenoszony_item,Paczka):
+                                if (prediction.predict_one(przenoszony_item.img_path)=='package'):
                                 ## wozek jedzie odlozyc paczke na regal
                                     przenoszona_paczka = przenoszony_item
 
diff --git a/paczka.py b/paczka.py
index aea865f..51e0595 100644
--- a/paczka.py
+++ b/paczka.py
@@ -4,7 +4,7 @@ import ekran
 
 
 class Paczka(pygame.sprite.Sprite):
-    def __init__(self, rozmiar, waga, kategoria, priorytet, ksztalt, kruchosc, nadawca, adres, imie, nazwisko, telefon):
+    def __init__(self, rozmiar, waga, kategoria, priorytet, ksztalt, kruchosc, nadawca, adres, imie, nazwisko, telefon, img_path):
         super().__init__()
         self.rozmiar = rozmiar
         self.image = pygame.image.load("images/paczka.png")
@@ -31,6 +31,7 @@ class Paczka(pygame.sprite.Sprite):
         self.priorytet = priorytet
         self.ksztalt = ksztalt
         self.kruchosc = kruchosc
+        self.img_path = img_path
         self.x = 430
         self.y = 400
         self.label = Etykieta(nadawca, adres, imie, nazwisko, telefon, priorytet)