This project is a React.js webapp based on a Python Fast API seving a Tensorflow Neural Network (link).
This repository contains the React.js webapp.
The project also uses sass (.scss) files instead of .css. Overall it works the same, any classic css will work fine but it is possible to use functions and variables for the css (very useful).
This project aims to predict the genre of a given song file using Data Science and Machine Learning techniques.
This repository contains the REST API built using Fast API that serves the Tensorflow model and librosa to extract features from uploaded MP3 files.
- Web application: https://classify.k8s.pouretadev.com/
- Backend - REST API: https://github.com/andbluedev/Music-Genre-Classification-Model-REST-API
- Project Notebooks: https://github.com/andbluedev/Music-Genre-Classification-Notebooks
For this project, it is recommended to use yarn to manage npm packages.
Installing dependencies
yarn install
yarn start
If necessary, you may need to change the backend url in the .env file, the name of the variable is: REACT_APP_API_URI.
You also need to configure your IDE for eslinctrc so that everyone can follow the same coding format and conventions.
The Fast API for production uses Docker, python and uvicorn and is deployed on a kubernetes cluster.
Compiling static files in the / build folder (dev "React" files to "html / css / js" browser-friendly files for the web)":
yarn install
yarn build
Build the Docker image :
docker build -t classify-front:v1 .
Run the container on port 3000 :
docker run -d -p 3000:80 classify-front:v1