This is the source code for my webpage which is hosted at https://deej-ai.online/. If you are interested in the deep learning models that are used in the backend, have a look at my other repo https://github.com/teticio/Deej-A.I./.
git clone https://github.com/teticio/deej-ai.online-app.git
cd deej-ai.online
make help
- FastAPI backend server that handles calls to TensorFlow deep learning models, serves web content, manages the database (SQL, SQLite, etc.) and takes care of the Spotify authentication flow.
- Dockerized for reproducibility.
- Scalable and highly available Kubernetes deployment which is easy to install on a cloud or bare metal server using Helm.
- SSL certificates for HTTPS connections provisioned automatically using Let's Encrypt.
- Responsive ReactJS frontend web app and React Native app for iOS and Android using common codebase.
- Client-side caching with service workers and server-side caching using Redis.
- PWA (Progressive Web App) that can be installed on the desktop.
- Automated unit tests and linting with Travis CI and / or GitHub Actions.
- Electron desktop app ready to deploy to Snap, Windows and Mac App Stores.
In order to run this, you will need to download the following files to the root directory:
- https://drive.google.com/file/d/1geEALPQTRBNUvkpI08B-oN4vsIiDTb5I/view?usp=sharing
- https://drive.google.com/file/d/1Mg924qqF3iDgVW5w34m6Zaki5fNBdfSy/view?usp=sharing
- https://drive.google.com/file/d/16JKjDGW2BMP-0KKJLFvwRvJdKhwukBeB/view?usp=sharing
- https://drive.google.com/file/d/1tLT_wmATWMC5UU-kERLsUNNcz0Vo19J3/view?usp=sharing
- https://drive.google.com/file/d/1LM1WW1GCGKeFD1AAHS8ijNwahqH4r4xV/view?usp=sharing
The easiest way to do this is by running
make download
This will also create a credentials.py file in the backend directory, which you should update with your Spotify Developer API credentials. These can be obtained from https://developer.spotify.com/dashboard/login.
SPOTIFY_CLIENT_ID='<Your client ID>'
SPOTIFY_CLIENT_SECRET='<Your secret>'
SPOTIFY_REDIRECT_URI='<Your external webpage URL>/api/v1/callback'
You should also set REACT_APP_API_URL to '<Your external webpage URL>/api/v1' in .env.production and APP_URL to <Your external webpage URL> in scripts/run.sh. If you want to avoid having to install Redis for caching requests, you should set the environment variable NO_CACHE. Assuming you have pipenv and yarn already installed, you can type
make install
and
make run
(In Windows, you will either have to run these commands in a Git Bash shell or adapt the scripts as appropriate). As a final step, you can then set up a reverse proxy to http://localhost:8000/.
To build the Docker image, run
make docker
You will need to have already created your credentials.py, as explained above. To install the Helm chart type
make k8s
The scripts assume you are running a minikube. This will install the backend FastAPI server, an SQL database to store the playlists and a Redis instance to cache server side static requests. To install on an AWS cluster with kOps run
scripts/deploy_kops.sh <Your external webpage domain>
provided your domain is hosted by Route 53 and you have configured the DNS, S3 and IAM settings appropriately to run kOps. If everything has gone to plan,
kubectl get svc -n deejai
will return an external IP for the Elastic Load Balancer (ELB). You will need to point your domain to the ELB by editing the relevant A record for your hosted zone in the AWS console. It will then automatically provision an SSL certificate for HTTPS connections.
You can also deploy the application as what I call a "serverless server" on AWS Fargate. It is "serverless" in the sense that you don't need to provision a server, but "serverful" in that it runs a Fargate task 24x7. The advantages are that you don't have to worry about patching your EC2 instances and it is very easy to update or restart at the click of a button. Running
TF_VAR_domain='<Your external webpage domain>' make tf_apply
in the terraform directory will create a VPC and Fargate ECS cluster and point your website to an Application Load Balancer. It currently uses a single Docker image running just the FastAPI server with persistent EFS storage. An advantage of Fargate is that you can use spot instances which are up to 70% cheaper than on-demand ones and are supposedly only interrupted about 5% of the time. To tear it down again
make tf_destroy
(While I was developing this script, my terraform.tfstate got in a bit of a state, so I had to destroy all the resources it had created by hand. I found it oddly reminiscent of trying to open one of those Japanese puzzle boxes as it would only let me delete the resources in exactly the right order.)
ReactJS and React Native are very similar but quite different at the same time. I wanted to avoid duplicating code as much as possible (following the DRY - Don't Repeat Yourself - principle) so I have wrapped the platform specific code in Platform.js and Platform.native.js. In particular, the standard HTML tags like <h1> or <a> have been replaced with wrapper components (e.g., Text and Link). To run on iOS or Android using Expo type
yarn start-native
You can build an APK or IPA if you have an Expo account with
expo build:android
or
expo build:ios
Electron is a great way to build and distribute JavaScript applications for the desktop (Windows, Mac, Linux) - Visual Studio Code is a good example of just what can be achieved. To build the application for your platform, type
yarn make
You will find the installers for your platform under the electron\out\make directory. Initially, I attempted to use the same package.json file for everything: ReactJS, React Native and Electron. It turned out to be very complicated to ensure that Electron packaged everything up correctly without interfering with React. It seems much neater to create a specific package.json file in a separate directory electron which has only the minimal node_modules needed - which are very few as the application has already been packaged with Webpack.