Add new CNN class for MNIST handling and update model usage in API

[sweep-nightly]

Description

This PR adds a new CNN class in src/cnn.py that handles the MNIST dataset. The CNN class is responsible for loading and preprocessing the data, defining the CNN architecture, and training the model. The existing code in src/main.py for loading and preprocessing the MNIST dataset and defining the PyTorch model has been removed and replaced with the new CNN class. Additionally, the usage of the model in src/api.py has been updated to use the new CNN model instead of the previous Net model.

Summary of Changes

• Created a new file src/cnn.py to contain the new CNN class.
• Imported necessary libraries in src/cnn.py for building the CNN model.
• Defined a new class CNN in src/cnn.py that inherits from torch.nn.Module.
• Implemented the __init__ method in CNN class to define the layers of the CNN.
• Implemented the forward method in CNN class to perform the forward pass of the CNN.
• Defined a load_data function in src/cnn.py to load and preprocess the MNIST dataset.
• Defined a train function in src/cnn.py to train the CNN model on the MNIST dataset.
• Added a main function in src/cnn.py to create an instance of the CNN class, load the data, and train the model.
• Updated src/main.py to import the CNN class from src/cnn.py.
• Removed the code in src/main.py for loading and preprocessing the MNIST dataset and defining the PyTorch model.
• Added code in src/main.py to create an instance of the CNN class and call the train method.
• Updated src/api.py to import the CNN class from src/cnn.py.
• Replaced the usage of the previous Net model with the new CNN model in src/api.py.
• Updated the path to the state dict file in src/api.py to match the location of the saved CNN model.

Please review and merge this PR to incorporate the changes.

Fixes #9.

---

🎉 Latest improvements to Sweep:

• Sweep can now passively improve your repository! Check out Rules to learn more.

---

💡 To get Sweep to edit this pull request, you can:

• Comment below, and Sweep can edit the entire PR
• Comment on a file, Sweep will only modify the commented file
• Edit the original issue to get Sweep to recreate the PR from scratch

[sweep-nightly]

Rollback Files For Sweep

• Rollback changes to src/main.py
• Rollback changes to src/main.py
• Rollback changes to src/api.py
• Rollback changes to src/cnn.py
• Rollback changes to src/main.py

[sweep-nightly]

Apply Sweep Rules to your PR?

• Apply: All docstrings and comments should be up to date.
• Apply: Code should be properly formatted and indented.
• Apply: Variable and function names should be descriptive and follow a consistent naming convention.
• Apply: Imports should be organized and grouped together.
• Apply: There should be no unused imports or variables.
• Apply: Code should be properly commented and include docstrings for functions and classes.