Football Mixer

A project made at InHolland University of Applied Sciences for the Big Data & AI minor.

The 'app' folder contains a Python project that uses the Flask framework to create a REST API, which interacts with a ML model that analyses microphone data input to determine the sounds of a football match.

The 'client' folder contains a Quasar project that uses Vue.js to interact with the REST API.

Requirements for Client

Node.js
Yarn

Requirements for App

Python 3.9

Getting Client to work

Go to the 'client' folder and run the following commands:

yarn install

Start the app in development mode (hot-code reloading, error reporting, etc.)

yarn run dev

Build the app for production

yarn run build

Getting App to work

Initial setup

For the initial setup, you need a virtual environment. To create one, run the following commands:

python3 -m venv .venv

I HIGHLY recommend modifying the .venv/bin/activate file to include the following line at the end:

export FLASK_APP=manage.py

This will automatically set the FLASK_APP environment variable every time you activate the virtual environment, so you don't have to do it manually every time.

To activate the virtual environment, run the following command:

source .venv/bin/activate

Install dependencies

To install the dependencies, run the following command:

python3.9 -m pip install -r requirements.txt

Start the app

To start the app, run the following command:

python3.9 -m flask run

For hot-reloading, you can use the following command:

python3.9 -m flask --debug run

Run backend with Docker

go in the app folder:

cd app

build the image:

docker compose build

run the docker container with this (with watch for hot-reloading):

docker compose up --watch

Usage with Postman

To use the API with Postman, you can import the following collection:

You can also simply connect to http://localhost:5000/ and use the Swagger UI.

Athlon007/football-mixer