Web-App Template

A webapp template that I use for everything - a python/flask backend with a node based HTML/CSS/JS front end

General use instructions

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

What things you need to install the software and how to install them.

npm
Python 3
docker

Installing

A step by step series of examples that tell you how to get a development environment running.

• Clone the repository

git clone https://yourrepositorylink.com
cd your-project-name

• Install JavaScript dependencies

npm install

• Install Python dependencies

First, ensure you have a virtual environment created and activated:

pipenv shell

• Then install the dependencies:

pipenv install Flask

• Build the project

Before serving the project with Flask, you need to build the static files with Parcel:

npm run build

• This command compiles your assets into the dist directory. Serving with Flask

To serve your project using Flask, run:

python app.py

Your project should now be accessible at http://localhost:5000

Development

For development, you can use Parcel's development server for hot reloading of changes:

npm start

And in another terminal, run your Flask application for backend functionality:

python app.py

Clearing the dist directory before builds

To ensure the dist directory is cleared before each build, the prebuild script in package.json is configured to run a clean operation:

"scripts": {
  "clean": "rimraf ./dist/*",
  "prebuild": "npm run clean",
  ...
}

Contributing

Please be nice and polite while submiting PRs - use github to submit the PRs and open issues as requiered.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

This was mostly done for a huge number of students and personal projects that I make with the Node - Flask - Mongo (Or whatever DB of your choice) Python allows for some really funky ML pipelines that would be impossible in other environments.