This Food Truck application serves as a full-stack project built with React, Node.js, and Elasticsearch. It's designed to showcase real-time data interaction with a map interface, where users can find food trucks in a city, explore their menus, and locate them on the map.

• Docker
• Node.js (v18.17.0)
• NPM

1. Clone the repository:

   git clone https://github.com/yourusername/foodtruck-app.git
   cd foodtruck-app

2. Navigate to the app directory:

   cd food_truck/app

3. Install dependencies:

   npm install

4. Build the project:

   npm run build

5. Start the application locally:

   npm start

   This command will start the backend server on http://localhost:3000 and will also serve the frontend.

1. Build the Docker image:

   docker build -t dva89/foodtruck-app .

2. Run the Docker containers:

   docker-compose up

   This will start both the application and the Elasticsearch service as defined in the docker-compose.yml.

/food-truck-app
|-- /src
|   |-- /client       # Client-specific code (React, CSS)
|   |   |-- components  # React components
|   |   |-- App.js
|   |   |-- index.js
|   |-- /server       # Server-specific code (Express, GraphQL)
|   |   |-- api       # API logic and controllers
|   |   |-- config    # Server config files
|   |   |-- routes    # Defines all routes and their controllers
|   |   |-- services  # Backend service logic, e.g., interacting with Elasticsearch
|-- /public           # Static files and compiled output
|-- /config           # Configuration files like Webpack
|-- /scripts          # Scripts for building and running the project
|-- package.json      # Node project dependencies and scripts
|-- Dockerfile
|-- docker-compose.yml
|-- README.md

• Ensure Docker is correctly installed and configured on your machine before attempting to run the Docker commands.
• The application uses a mocked backend for demonstration purposes; adjust the data endpoints for production use.

This README provides a concise yet comprehensive guide to getting the Food Truck application running on your local development machine and using Docker for deployment.