MNIST Digit Classifier-CNN 🧑‍💻✨

Welcome to the MNIST Digit Classifier! 🎉 This project combines machine learning and web development to create a web application that classifies handwritten digits from the MNIST dataset. Whether you're a data science enthusiast, a machine learning newbie, or just curious, you can test out this cool web app to see how well a neural network can recognize digits.

🧩 Project Overview

🧠 Machine Learning Model

At the heart of this application is a Convolutional Neural Network (CNN) designed to recognize handwritten digits. Here's a quick rundown of the model:

• Architecture: SimpleCNN
• Layers:
  • 2 Convolutional Layers
  • 2 ReLU Activations
  • 2 MaxPooling Layers
  • 1 Fully Connected Layer
  • Output: 10 classes (digits 0-9)
• Dataset: MNIST
• Frameworks: PyTorch

🌐 Web Application

The web app provides a user-friendly interface for uploading images and receiving predictions. It’s built with Flask and features:

• Frontend: HTML, CSS, and JavaScript
• Backend: Python Flask, integrated with the PyTorch model

📜 How It Works

1. Upload an Image: Select a file containing a handwritten digit.
2. Model Prediction: The image is processed, transformed into a tensor, and passed through the CNN model.
3. Result Display: The app returns the predicted digit based on the model’s output.

📂 Project Structure

Here's an overview of the project files and their roles:

• app.py: The Flask application code that handles image uploads and communicates with the ML model.
• model.py: Defines the SimpleCNN model architecture used for digit classification.
• static/: Contains CSS and JavaScript files for the web app.
• templates/: HTML files for the web app's frontend.
• .vercelignore: Specifies files and directories to ignore during deployment.
• requirements.txt: Lists the Python packages required for the app.
• Procfile: Instructions for the web server to run the Flask app.

📦 Installation

To get this project up and running on your local machine:

1. Clone the repository:

   git clone https://github.com/your-username/MNIST-Digit-Classifier.git
   cd MNIST-Digit-Classifier

2. Create a virtual environment and activate it:

   python -m venv venv
   venv\Scripts\activate  # For Windows
   source venv/bin/activate  # For macOS/Linux

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Flask app:

   python app.py

5. Open your browser and go to: http://localhost:5000

🎯 Contributing

Want to contribute? Great! Open an issue or create a pull request. Contributions, feedback, and suggestions are always welcome! 🙌

📝 License

This project is licensed under the MIT License. See the LICENSE file for details.

📷 Screenshots

Here are some screenshots of the application in action: