From 6eb6a09b38963aaee67dcba98bb2a55196f36c69 Mon Sep 17 00:00:00 2001
From: "sweep-nightly[bot]"
 <131841235+sweep-nightly[bot]@users.noreply.github.com>
Date: Tue, 24 Oct 2023 04:15:13 +0000
Subject: [PATCH 1/2] feat: Updated src/main.py

---
 src/main.py | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/src/main.py b/src/main.py
index 243a31e..13759b1 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,22 +1,42 @@
+"""
+This module is used to load and preprocess the MNIST dataset and define a PyTorch model.
+"""
+
+# PIL is used for opening, manipulating, and saving many different image file formats
 from PIL import Image
+# torch is the main PyTorch library
 import torch
+# torch.nn provides classes for building neural networks
 import torch.nn as nn
+# torch.optim provides classes for implementing various optimization algorithms
 import torch.optim as optim
+# torchvision.datasets provides classes for loading and manipulating datasets
 from torchvision import datasets, transforms
+# torchvision.transforms provides classes for transforming images
+# torch.utils.data provides utilities for loading and manipulating data
 from torch.utils.data import DataLoader
+# numpy is used for numerical operations
 import numpy as np
 
 # Step 1: Load MNIST Data and Preprocess
+# 'transform' is a sequence of transformations applied to the images in the dataset
 transform = transforms.Compose([
     transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))
 ])
 
+# 'trainset' represents the MNIST dataset
 trainset = datasets.MNIST('.', download=True, train=True, transform=transform)
+# 'trainloader' is a data loader for the MNIST dataset
 trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
 
 # Step 2: Define the PyTorch Model
 class Net(nn.Module):
+    """
+    This class defines a simple feed-forward neural network for classifying MNIST images.
+    The network has three fully connected layers and uses ReLU activation functions.
+    The forward method takes an input tensor, reshapes it, and passes it through the network.
+    """
     def __init__(self):
         super().__init__()
         self.fc1 = nn.Linear(28 * 28, 128)

From 520f1b6e1b424afa21dc18c119d6d7000a141c2b Mon Sep 17 00:00:00 2001
From: "sweep-nightly[bot]"
 <131841235+sweep-nightly[bot]@users.noreply.github.com>
Date: Tue, 24 Oct 2023 04:25:16 +0000
Subject: [PATCH 2/2] feat: Updated src/api.py

---
 src/api.py | 19 ++++++++++++++++---
 1 file changed, 16 insertions(+), 3 deletions(-)

diff --git a/src/api.py b/src/api.py
index 36c257a..c4a4b2d 100644
--- a/src/api.py
+++ b/src/api.py
@@ -1,24 +1,37 @@
+"""
+This module creates a FastAPI application for making predictions using the PyTorch model defined in main.py.
+"""
+# FastAPI is used for creating the API
 from fastapi import FastAPI, UploadFile, File
+# PIL is used for opening, manipulating, and saving many different image file formats
 from PIL import Image
+# torch is the main PyTorch library
 import torch
+# torchvision.transforms provides classes for transforming images
 from torchvision import transforms
-from main import Net  # Importing Net class from main.py
+# Net is the PyTorch model defined in main.py
+from main import Net
 
-# Load the model
+# 'model' represents the PyTorch model
 model = Net()
 model.load_state_dict(torch.load("mnist_model.pth"))
 model.eval()
 
-# Transform used for preprocessing the image
+# 'transform' is a sequence of transformations applied to the images
 transform = transforms.Compose([
     transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))
 ])
 
+# 'app' represents the FastAPI application
 app = FastAPI()
 
 @app.post("/predict/")
 async def predict(file: UploadFile = File(...)):
+    """
+    This function takes an image file as input, preprocesses it, passes it through the model, and returns the model's prediction.
+    The input is an image file and the return value is a dictionary with the key 'prediction' and the model's prediction as the value.
+    """
     image = Image.open(file.file).convert("L")
     image = transform(image)
     image = image.unsqueeze(0)  # Add batch dimension