To use the database, please do the following:
- pip install -r requirements.txt
- A default database is already implemented. If you would like to change this, see Dataset to Database Extraction below. To then use that database, change line 35 of spotify.py "DATABASE_USED = 'yourDatabaseNameHere'"
- python3 NicheRank/manage.py runserver
- Open a second terminal.
- python spotify.py
- go to the http://127.0.0.1:5000 page
- To use the Spotify API, leave spotify.py line 32 "user_login" as 0. You must log in with these credentials, as Spotify for Developers only lets manually authorized users log in on unpublished projects. [username: AmandaBrannon pw: Workingonit1!] (you can see what I have been listening to! If you give me your full name and email, I can allow up to 25 people access while it is in development mode). Change user_login to 1 through 7, and it will randomly generate a fake user profile with 100000 points (or more) of data, as well as use different sorting types (q=quick, m=merge). If you really want to, these can be manually changed for even more combinations for various user profiles
Due to the size of the database, we have already created it on our own. If you would like to generate your own database, do the following:
- Download zip from https://drive.google.com/drive/folders/1P_A_GMWGeT8Z4Lz1Z0E65n_gdTMMibZ9?usp=sharing
- Unzip file into the NicheRank/playlist_database folder.
- Run the playlist_parse.py as
playlist_parse.py --load_percent 0.3 --profile True where load_percent is % of million playlists to load and profile is if to display how much time it takes.
Keep in mind, it takes around 1.5 minutes to parse 10% of the database, so either keep the value low or give it some time.
Our project aimed to solve the question as to whether a Spotify user is listening to popular or niche music and artists in comparison with the one-million playlist Spotify dataset.
Our motivation was that Spotify does not have any sort of metrics as to how popular your current listening history is. Through our web application, a user is able to sign into their Spotify account, give access permission, and then be presented with how popular their music taste is, as well as their top songs and artists.
- ReactJS: Utilize ReactJS, a JavaScript framework that provides a foundation for developing dynamic and responsive web applications.
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Django with RESt: Utilize Django with REST framework to build powerful and flexible APIs for web applications.
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FLASK: Utilize Flask framework to integrate Spotify and communicate with our frontend.
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Visual Studio Code: A versatile code editor that provides debugging, task running, and version control for streamlined development.
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Github: Version control and collaboration were managed using GitHub, enabling effective team collaboration and code tracking.
Role:
Frontend Developer
Contribution
Led the frontend development using ReactJS and DJango with REST Frameworks, creating user interface, including buttons, text fields, and ensuring easy integration between the backend and frontend.
Role:
Data retrieval and Backend Developer
Contribution
Created and utilized the Spotify API for user authentication and user data extraction to be used in the comparison algorithms. Assisted with integration of the backend and frontend.
Role:
Backend Developer
Contribution
Created inheritance heirarcy for songs, artists, songstats and artist stats. Parsed the million playlists dataset and created an compression serialization and deserialization workflow. Created greedy user history generation and metrics for scoring users popularity score in comparison to global database. Created the quicksort and mergesort for quickly sorting the listening history by its local listens and according to the global listens, all within a inheritance based sorting structure. Controlled serialization of data into json and control flow of analysis algorithm and population scoring.