diff --git a/src/api.py b/src/api.py
index 36c257a..25eb967 100644
--- a/src/api.py
+++ b/src/api.py
@@ -1,22 +1,23 @@
-from fastapi import FastAPI, UploadFile, File
-from PIL import Image
 import torch
+from fastapi import FastAPI, File, UploadFile
+from PIL import Image
 from torchvision import transforms
-from main import Net  # Importing Net class from main.py
+
+from cnn import CNN  # Importing CNN class from cnn.py
 
 # Load the model
-model = Net()
+model = CNN()
 model.load_state_dict(torch.load("mnist_model.pth"))
 model.eval()
 
 # Transform used for preprocessing the image
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5,), (0.5,))
-])
+transform = transforms.Compose(
+    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)
 
 app = FastAPI()
 
+
 @app.post("/predict/")
 async def predict(file: UploadFile = File(...)):
     image = Image.open(file.file).convert("L")
diff --git a/src/cnn.py b/src/cnn.py
new file mode 100644
index 0000000..1dddaa0
--- /dev/null
+++ b/src/cnn.py
@@ -0,0 +1,21 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.fc1 = nn.Linear(64 * 4 * 4, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 64 * 4 * 4)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
diff --git a/src/main.py b/src/main.py
index 243a31e..774cc5d 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,20 +1,22 @@
-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 PIL import Image
 from torch.utils.data import DataLoader
-import numpy as np
+from torchvision import datasets, transforms
+
+from cnn import 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):
@@ -22,7 +24,7 @@ def __init__(self):
         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))
@@ -30,8 +32,9 @@ def forward(self, x):
         x = self.fc3(x)
         return nn.functional.log_softmax(x, dim=1)
 
+
 # Step 3: Train the Model
-model = Net()
+model = CNN()
 optimizer = optim.SGD(model.parameters(), lr=0.01)
 criterion = nn.NLLLoss()
 
@@ -45,4 +48,4 @@ def forward(self, x):
         loss.backward()
         optimizer.step()
 
-torch.save(model.state_dict(), "mnist_model.pth")
\ No newline at end of file
+torch.save(model.state_dict(), "mnist_model.pth")