Refactor Training Loop into a Class (✓ Sandbox Passed)

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 main import Trainer  # Importing Net class from main.py
 
 # Load the model
-model = Net()
-model.load_state_dict(torch.load("mnist_model.pth"))
+trainer = Trainer()
+model = trainer.model
 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")

src/main.py

@@ -1,48 +1,31 @@
+import to
+from PIL import Image
+
+
+class Trainer:
+    def __init__(self, net_class, optimizer_class, criterion_class):
+        self.model = net_class()
+        self.optimizer = optimizer_class(self.model.parameters(), lr=0.01)
+        self.criterion = criterion_class()
+
+    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()
+
+    def save_model(self, path="mnist_model.pth"):
+        torch.save(self.model.state_dict(), path)
+
+
+# Initialize and train the model with Trainer
+trainer = Trainer(Net, optim.SGD, nn.NLLLoss)
+trainer.train(trainloader, epochs=3)
+trainer.save_model()
+
+
+import to
 from PIL import Image
-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,))
-])
-
-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)
-
-# 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")