Refactor Training Loop into a Class (✓ Sandbox Passed)

src/api.py

@@ -1,28 +1,29 @@
-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 Trainer class from main.py
 
 # Load the model
-model = Net()
-model.load_state_dict(torch.load("mnist_model.pth"))
-model.eval()
+trainer = Trainer()
+trainer.load_model("mnist_model.pth")  # Assuming load_model method exists
 
 # 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")
     image = transform(image)
     image = image.unsqueeze(0)  # Add batch dimension
     with torch.no_grad():
-        output = model(image)
+        output = trainer.get_model()(image)  # Assuming get_model method exists
         _, predicted = torch.max(output.data, 1)
     return {"prediction": int(predicted[0])}

src/main.py

@@ -1,48 +1,59 @@
-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
 
 # 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):
         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)
 
+
+class Trainer:
+    def __init__(self, learning_rate, model_path):
+        self.model = Net()
+        self.optimizer = optim.SGD(self.model.parameters(), lr=learning_rate)
+        self.criterion = nn.NLLLoss()
+        self.model_path = model_path
+
+    def train(self, 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):
+        torch.save(self.model.state_dict(), self.model_path)
+
+
 # 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")
+
+# Now let's create a Trainer instance and train and save the model
+trainer = Trainer(learning_rate=0.01, model_path="mnist_model.pth")
+trainer.train(epochs=3)
+trainer.save_model()