Add Comments and Docstrings to main.py and api.py

src/api.py

@@ -1,8 +1,13 @@
-from fastapi import FastAPI, UploadFile, File
-from PIL import Image
+"""
+This module provides an API endpoint for predicting the digit in an uploaded image using a pre-trained PyTorch model.
+
+The API endpoint '/predict/' accepts POST requests with an image file, preprocesses the image, and returns the predicted digit.
+"""
 import torch
-from torchvision import transforms
+from fastapi import FastAPI, File, UploadFile
 from main import Net  # Importing Net class from main.py
+from PIL import Image
+from torchvision import transforms
 
 # Load the model
 model = Net()
@@ -26,3 +31,20 @@ async def predict(file: UploadFile = File(...)):
         output = model(image)
         _, predicted = torch.max(output.data, 1)
     return {"prediction": int(predicted[0])}
+        output = model(image)
+        _, predicted = torch.max(output.data, 1)
+    return {"prediction": int(predicted[0])}
+    return {"prediction": int(predicted[0])}
+    Parameters:
+    - file (UploadFile): The image file to predict.
+
+    Returns:
+    - dict: A dictionary with the key 'prediction' and the predicted digit as the value.
+    """
+    image = Image.open(file.file).convert("L")
+    image = transform(image)
+    image = image.unsqueeze(0)  # Add batch dimension
+    with torch.no_grad():
+        output = model(image)
+        _, predicted = torch.max(output.data, 1)
+    return {"prediction": int(predicted[0])}

src/main.py

@@ -1,10 +1,10 @@
-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([
@@ -17,6 +17,19 @@
 
 # Step 2: Define the PyTorch Model
 class Net(nn.Module):
+    """
+    This is a simple feed-forward neural network model.
+
+    Methods:
+    - __init__: Initializes the model layers.
+    - forward: Defines the forward pass of the model.
+
+    Parameters for forward:
+    - x (torch.Tensor): The input tensor.
+
+    Returns from forward:
+    - torch.Tensor: The output tensor after passing through the model.
+    """
     def __init__(self):
         super().__init__()
         self.fc1 = nn.Linear(28 * 28, 128)