A Flask web app that allows sptotify users to view detailed information and statistics about their usage data. Also features an audio analysis and personality classification based on the user's top songs, deployed with Microsoft Azure.
Utilizes the Spotify Web API for authentication and data collection, uses sklearn's Decision Tree Classifier for the personality classficiation, uses Matplotlib/seaborn for creating the graphs.
- You must make a few changes to the code before attempting to run it locally, the following changes are at the beginning of app.py.
'''
# Redirect uri and authorization scopes
redirect_uri = "https://myspotifydata.azurewebsites.net/home"
scope = "user-top-read user-read-recently-played playlist-read-collaborative playlist-read-private"
# UNCOMMENT TO USE FOR LOCAL TESTING
# redirect_uri = "http://127.0.0.1:8000/home"
'''
- Uncomment the local redirect url and comment out the azurewebsites redirect urls, it should look like this when finished.
'''
# Redirect uri and authorization scopes
#redirect_uri = "https://myspotifydata.azurewebsites.net/home"
scope = "user-top-read user-read-recently-played playlist-read-collaborative playlist-read-private"
# UNCOMMENT TO USE FOR LOCAL TESTING
redirect_uri = "http://127.0.0.1:8000/home"
'''
- Additionally, you must uncomment the following block of code at the end of app.py.
'''
# # Run the server (uncomment for local testing)
# if __name__ == "__main__":
# app.run(debug=True, port=8000)
'''
-
Run "pip install -r requirements.txt" to install all the required dependancies, or "pipenv install -r requirements.txt" if you are using a pipenv.
-
If all of the dependancies have been installed, run python app.py to run the Flask app locally.
-
Dependencies are located in requirements.txt