diff --git a/src/cnn.py b/src/cnn.py
new file mode 100644
index 0000000..6819040
--- /dev/null
+++ b/src/cnn.py
@@ -0,0 +1,43 @@
+import torch.nn as nn
+import torch.optim as optim
+
+
+class CNN(nn.Module):
+    """
+    Convolutional Neural Network (CNN) class.
+    """
+
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
+        self.relu1 = nn.ReLU()
+        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.relu2 = nn.ReLU()
+        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.fc1 = nn.Linear(7 * 7 * 64, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.pool1(self.relu1(self.conv1(x)))
+        x = self.pool2(self.relu2(self.conv2(x)))
+        x = x.view(-1, 7 * 7 * 64)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        return x
+
+
+def train_cnn(model, dataloader, epochs):
+    """
+    Function to train the CNN model.
+    """
+    optimizer = optim.SGD(model.parameters(), lr=0.01)
+    criterion = nn.CrossEntropyLoss()
+
+    for _epoch in range(epochs):
+        for images, labels in dataloader:
+            optimizer.zero_grad()
+            output = model(images)
+            loss = criterion(output, labels)
+            loss.backward()
+            optimizer.step()
diff --git a/src/main.py b/src/main.py
index 243a31e..7bc2620 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,48 +1,29 @@
-from PIL import Image
+import numpy as np
 import torch
-import torch.nn as nn
-import torch.optim as optim
-from torchvision import datasets, transforms
 from torch.utils.data import DataLoader
-import numpy as np
+from torchvision import datasets, transforms
+
+from src.cnn import (  # Import CNN class and train_cnn function from cnn.py
+    CNN,
+    train_cnn,
+)
 
 # Step 1: Load MNIST Data and Preprocess
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5,), (0.5,))
-])
+transform = transforms.Compose(
+    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)
 
-trainset = datasets.MNIST('.', download=True, train=True, transform=transform)
+trainset = datasets.MNIST(".", download=True, train=True, transform=transform)
 trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
 
 # Step 2: Define the PyTorch Model
-class Net(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.fc1 = nn.Linear(28 * 28, 128)
-        self.fc2 = nn.Linear(128, 64)
-        self.fc3 = nn.Linear(64, 10)
-    
-    def forward(self, x):
-        x = x.view(-1, 28 * 28)
-        x = nn.functional.relu(self.fc1(x))
-        x = nn.functional.relu(self.fc2(x))
-        x = self.fc3(x)
-        return nn.functional.log_softmax(x, dim=1)
+# Create an instance of the CNN class
+model = CNN()  # Create an instance of the CNN class
 
 # Step 3: Train the Model
-model = Net()
-optimizer = optim.SGD(model.parameters(), lr=0.01)
-criterion = nn.NLLLoss()
-
-# Training loop
-epochs = 3
-for epoch in range(epochs):
-    for images, labels in trainloader:
-        optimizer.zero_grad()
-        output = model(images)
-        loss = criterion(output, labels)
-        loss.backward()
-        optimizer.step()
+# Call the train_cnn function, passing the CNN instance, the DataLoader instance, and a number of epochs
+train_cnn(
+    model, trainloader, 10
+)  # Train the CNN model with the trainloader data for 10 epochs
 
-torch.save(model.state_dict(), "mnist_model.pth")
\ No newline at end of file
+torch.save(model.state_dict(), "mnist_model.pth")  # Save the trained model