EUMETNET ML Applications Gallery (Climate ML Showcase)

A gallery of machine learning applications in weather and climate from across the EUMETNET community.

This project is a Single-Page Application (SPA) built with React, Vite, TanStack Router, and Tailwind CSS.

Local Development

Prerequisites

You will need Node.js installed on your system. The project uses standard npm scripts, but can also be run using Bun (which is used in the CI/CD pipeline).

Installation

Clone the repository and install the dependencies:

git clone https://github.com/eumetnet-e-ai/eumetnet-e-ai.github.io.git
cd eumetnet-e-ai.github.io

# Using npm
npm install

# Or using bun
bun install

Running the Development Server

To start the local development server with Hot Module Replacement (HMR):

# Using npm
npm run dev

# Or using bun
bun run dev

The application will be available at http://localhost:5173 (or the port specified in your terminal).

Available Scripts

• npm run dev - Starts the Vite development server.
• npm run build - Builds the application for production (outputs to dist/client/).
• npm run preview - Locally preview the production build.
• npm run lint - Runs ESLint to check for code quality issues.
• npm run format - Runs Prettier to format the codebase.

Deployment

The application is automatically deployed to GitHub Pages whenever changes are pushed to the main branch. The deployment process is handled by GitHub Actions (.github/workflows/deploy-pages.yml).

Because it is deployed to a subdirectory (/climate-ml-showcase/), the Vite configuration and TanStack Router are specifically set up to use the BASE_URL environment variable during the build step.