🎶 AI MoodSync 🎶 (Weaviate Project)

ℹ Introduction

A small web-app that matches a song to your input mood based on lyrics, using Weaviate and a kaggle dataset of song lyrics.

Note: you will need a Weaviate sandbox cluster along with its corresponding API key and URL for performing the vector search, a Spotify WebAPI for retrieving song information such as album art, as well as an OpenAI account and API key for actually running the embeddings and text generation. This is explained more in the Installation section below.

📋 TODO 📋:

• Add previously recommended history list
• Use a larger database of songs
• Implement multiple search strategies using more embedding models.
• Replace Spotify API with another way of retrieving album art, and make the Spotify embed optional

🛠 How it works

First, the input mood or text given by the user is embedded via OpenAI's text2vec embedding model as a high-dimensional vector.

Then, this embedding is compared to all the equivalent vectors in the song database via using Weaviate's near_text query function, which returns the song with the most similar vector to the target input. This song is returned (with an element of randomness, so that the same search can yield up to 5 different songs) and displayed.

The backend of this app is developed in Python, and the front-end is developed in React.

🚀 Installation

Provided that you have installed Node.js and Python, you can clone the github repository via

git clone https://github.com/dannyjameswilliams/song-matcher.git 

then enter the directory

cd song-matcher

(Optional) Virtual Environment

I would recommend using a virtual python environment such as conda, and creating one with

conda create -n song-matcher
conda install pip

Setting up Python

You can use pip to install the required python packages

pip install -r requirements.txt

Setting up React

Navigate to the app directory via

cd app

and install the required packages with npm

npm install 

Setting up API Keys

This app relies on access to API keys for Weaviate, Spotify and OpenAI account and API key. You need to save these keys in a keys.py file in the root directory so that the python code can find it. The variables should be named as follows

weaviate_key   = ... # Obtained from Weavaite cloud dashboard
weaviate_url   = ... # Obtained from Weaviate cloud dashboard
openai_key     = ... # Obtained from OpenAI account
spotify_id     = ... # Obtained from Spotify WebAPI
spotify_secret = ... # Obtained from Spotify WebAPI

Setting up the database

When you have created a Weaviate account, you need to run

python setup.py

from the root directory, which will populate a Weaviate collection called 'lyrics' on your account for the given Weaviate URL supplied in the 'keys.py' file. This collection will be queried when the app is run.

🔦 Usage

Back-end

We need to set up the front-end and the back-end separately. Ensure you are in the root directory (i.e. .../song-finder and not .../song-finder/app). The back-end is managed by Python and is located entirely within the home directory, and thus can be started via

python backend.py

This will run on port 5000, so make sure there is nothing else running on that port. If you would like to modify the port number, simply change 5000 in the line app.run(port=5000, debug=False) at the bottom of backend.py to a different value. Additionally, change any instances of localhost:5000 inside of app/App.js to the new port number.

Front-end

If you have the python file running in one terminal, you can open a second, separate terminal to run the remainder of this section, so that two processes are running concurrently.

The front-end is entirely via React and you must first change to the app directory via

cd app

and then

npm start

This will run on port 3000, if you would like to change it, you can instead use

PORT=XXXX npm start

which will change it to whatever number you replace XXXX with.

🪛 Modifications

If you would like to modify the code or app in any way, it should be relatively straightforward to figure out as the code should be fully commented and easy to understand.

Some ideas for modifications would be to change how the search works, or to change the embedding model of the songs/queries.

Credits

• Weaviate for the Python library.
• OpenAI for the embedding model and generative model.
• 'Lizzie' for the kaggle dataset.