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metadata
license: apache-2.0
title: English Music Recommender
sdk: streamlit
emoji: 🐨
colorFrom: green
colorTo: green

English Music Recommender

About

This project aims to create a music recommendation application for non-native English speakers who intend to learn English through the lyrics of English songs. Music is one of the most entertaining and practical approaches to learning a language. People can learn new words and expressions by listening to and singing the lyrics. However, different artists vary in their songwriting choices of words and expressions, which may lead to a difference in the difficulties of comprehending the lyrics for English learners. OpenAI is one of the most useful tools when it comes to recommending people. Therefore, we are creating an application to combine OpenAI with our intention to recommend suitable songs for different levels of learners. On top of that, OpenAI will serve as an English teacher to answer users’ questions regarding the use of English words in the lyrics.

How To Play

  1. Go to https://huggingface.co/spaces/si568project2/English_Music_Recommender
  2. Pick your preferences for the music on the left side
  3. Click the Submit button
  4. The recommendation will show on the same page
  5. Go to the right side of the page and interact with OpenAI about the song by choosing one of the prompts or typing in your own

File Description

File Name Description
data_fetch.py Fetch and consolidate song data from Spotify, Genius, and Bill Board Hot 100
data.json The output song data from data_fetch.py
app_function.py Implement OpenAI API
choosingdata.py Filter the dataframe based on user's preference
app.py Display user interface
README.md The instructions for running the code
requirement.txt A list of packages and libraries required for the app

Used Third-Party Python Packages

  • BeautifulSoup
  • requests
  • nltk
  • OpenAI
  • streamlit
  • langchain

Data Structure

As below screenshot shows, the original data retrieved from the APIs is constructed in a DataFrame structure.

Original data retrieved from the APIs (data.json)

In the choosingdata.py file, the data is filtered based on the user's preference:

  1. Danceability
  2. Valence
  3. Difficulty (speechiness, fres, vocabComplex, sentenceLength) - Binned into quantiles to rank the difficulty

According to the user's preference, we will be filtering out a list of songs that match their choice. After filtering the dataframe, it will generate and print a random song from the corresponding dataframe. Users will then be able to ask questions about the song or regenerate for another song.