Features

• Beautiful modern UI design (using Tailwind CSS)
• Dark mode support
• Responsive design (mobile-friendly)
• Pen size adjustment
• Touch screen support
• Visual feedback with animations
• Probability distribution bar chart

Installation

Prerequisites

• Crystal 1.15.1 or higher
• ONNXRuntime 1.21.0

Usage

Using Docker Compose

docker compose up

Or to run in detached mode:

docker compose up -d

Access the application in your browser at http://localhost:3000.

Development

Setting Up Local Development Environment

# For Linux (x64)
wget https://github.com/microsoft/onnxruntime/releases/download/v1.21.0/onnxruntime-linux-x64-1.21.0.tgz
tar -xzf onnxruntime-linux-x64-1.21.0.tgz
export ONNXRUNTIMEDIR=$(pwd)/onnxruntime-linux-x64-1.21.0

# Download model
mkdir -p spec/fixtures
curl -L -o spec/fixtures/mnist.onnx https://github.com/microsoft/onnxruntime/raw/main/onnxruntime/test/testdata/mnist.onnx

git clone https://github.com/kojix2/mnist.cr.git
cd mnist.cr
shards install

crystal run src/mnist.cr

Contributing

1. Fork it (https://github.com/kojix2/mnist.cr/fork)
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request

Credits

• MNIST Dataset - Handwritten digit dataset
• ONNXRuntime - Machine learning model inference engine
• Kemal - Web framework for Crystal
• Tailwind CSS - Utility-first CSS framework

License

The code was generated entirely by VSCode+CLINE+claude-3.7-sonnet. MIT