Add CNN class for MNIST and integrate it into main.py and api.py

src/api.py

@@ -1,8 +1,8 @@
-from fastapi import FastAPI, UploadFile, File
-from PIL import Image
 import torch
+from cnn import CNN  # Importing CNN class from cnn.py
+from fastapi import FastAPI, File, UploadFile
+from PIL import Image
 from torchvision import transforms
-from main import Net  # Importing Net class from main.py
 
 # Load the model
 model = Net()

src/main.py

@@ -1,10 +1,11 @@
-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 cnn import CNN
+from PIL import Image
 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([
@@ -31,7 +32,7 @@ def forward(self, 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()