feat: Updated src/main.py

src/main.py

@@ -1,10 +1,32 @@
-from PIL import Image
+from PIL import Ima
+
+
+class Trainer:
+    def __init__(self, model_class, model_params, optimizer_class, optimizer_params, criterion):
+        self.model = model_class(*model_params)
+        self.optimizer = optimizer_class(self.model.parameters(), **optimizer_params)
+        self.criterion = criterion
+
+    def train(self, trainloader, epochs):
+        for epoch in range(epochs):
+            for images, labels in trainloader:
+                self.optimizer.zero_grad()
+                output = self.model(images)
+                loss = self.criterion(output, labels)
+                loss.backward()
+                self.optimizer.step()
+            print(f"Epoch {epoch+1}/{epochs} completed")
+
+    def save_model(self, path):
+        torch.save(self.model.state_dict(), path)
+
+ge
+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
 
 # Step 1: Load MNIST Data and Preprocess
 transform = transforms.Compose([
@@ -31,18 +53,10 @@ def forward(self, 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()
-
+# Initialize Trainer
+trainer = Trainer(Net, [], optim.SGD, {'lr': 0.01}, 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")
+trainer.train(trainloader, epochs)
+# Save the trained model
+trainer.save_model("mnist_model.pth")