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import streamlit as st |
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from streamlit import session_state as session |
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st.set_page_config(page_title="Song Recommender🎶", page_icon="🎶") |
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st.title("Song Recommender🎶") |
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st.markdown("Click on '**Recommender**' from the side panel to get started.") |
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st.markdown("**How does it work?**") |
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st.markdown( |
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"The songs come from the [Spotify and Genius Track Dataset](https://www.kaggle.com/datasets/saurabhshahane/spotgen-music-dataset) on Kaggle. The [k-Nearest Neighbor algorithm](https://scikit-learn.org/stable/modules/neighbors.html) is used to obtain recommendations, i.e., the top songs which are closest in distance to the set of parameter inputs specified by you." |
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) |
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st.markdown("This app will recommend you songs based on the characteristics below.") |
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st.markdown( |
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""" |
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**Acousticness**: A metric describing the 'acousticness' of a song. 1.0 represents high confidence the song is acoustic.<br> |
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**Danceability**: Describes a song's suitability for dancing based on combination of elements including tempo, rhythm stability, beat strength, and overall regularity. |
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0.0 is least danceable and 1.0 is most danceable.<br> |
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**Energy**: Measure of intensity and activity. Often, energetic songs feel fast, loud, and noisy.<br> |
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**Liveness**: A metric describing the likelihood that a track is a recording of a live performance.<br> |
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**Speechiness**: How much lyrics the track contains.<br> |
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**Valence**: A metric ranging from 0.0 to 1.0 describing the positivity conveyed by a track.<br> |
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Source: [Spotify Web API](https://developer.spotify.com/documentation/web-api/reference) |
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""", |
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unsafe_allow_html=True, |
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) |
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