diff --git a/src/api.py b/src/api.py
index 36c257a..fae9a17 100644
--- a/src/api.py
+++ b/src/api.py
@@ -1,26 +1,20 @@
-from fastapi import FastAPI, UploadFile, File
-from PIL import Image
 import torch
-from torchvision import transforms
-from main import Net  # Importing Net class from main.py
+from fastapi import FastAPI, File, UploadFile
+from PIL import Image
 
-# Load the model
-model = Net()
-model.load_state_dict(torch.load("mnist_model.pth"))
-model.eval()
+from main import MNISTTrainer  # Importing MNISTTrainer class from main.py
 
-# Transform used for preprocessing the image
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5,), (0.5,))
-])
+# Create an instance of MNISTTrainer and run the training
+trainer = MNISTTrainer()
+model = trainer.run()
 
 app = FastAPI()
 
+
 @app.post("/predict/")
 async def predict(file: UploadFile = File(...)):
     image = Image.open(file.file).convert("L")
-    image = transform(image)
+    image = trainer.preprocess(image)
     image = image.unsqueeze(0)  # Add batch dimension
     with torch.no_grad():
         output = model(image)
diff --git a/src/main.py b/src/main.py
index 243a31e..3c3b22f 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,48 +1,61 @@
-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
-
-# Step 1: Load MNIST Data and Preprocess
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5,), (0.5,))
-])
+from torchvision import datasets, transforms
 
-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):
+class MNISTTrainer:
     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)
-
-# 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()
-
-torch.save(model.state_dict(), "mnist_model.pth")
\ No newline at end of file
+        self.transform = transforms.Compose(
+            [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+        )
+
+    def load_data(self):
+        trainset = datasets.MNIST(
+            ".", download=True, train=True, transform=self.transform
+        )
+        trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
+        return trainloader
+
+    def define_model(self):
+        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)
+
+        model = Net()
+        return model
+
+    def train_model(self, trainloader, model, epochs, lr):
+        optimizer = optim.SGD(model.parameters(), lr=lr)
+        criterion = nn.NLLLoss()
+        for epoch in range(epochs):
+            for images, labels in trainloader:
+                optimizer.zero_grad()
+                output = model(images)
+                loss = criterion(output, labels)
+                loss.backward()
+                optimizer.step()
+        return model
+
+    def run(self, epochs=3, lr=0.01, save_path="mnist_model.pth"):
+        trainloader = self.load_data()
+        model = self.define_model()
+        model = self.train_model(trainloader, model, epochs, lr)
+        torch.save(model.state_dict(), save_path)
+        return model
+
+
+trainer = MNISTTrainer()
+trainer.run()