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#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import streamlit as st
st.set_page_config(page_title="Karaoke Playlist Generator", layout="wide")
import pandas as pd
from sklearn.neighbors import NearestNeighbors
import plotly.express as px
import streamlit.components.v1 as components
@st.cache(allow_output_mutation=True)
def load_data():
df = pd.read_csv("data/filtered_track_df.csv")
df['genres'] = df.genres.apply(lambda x: [i[1:-1] for i in str(x)[1:-1].split(", ")])
exploded_track_df = df.explode("genres")
return exploded_track_df
genre_names = ['Dance Pop', 'Electronic', 'Electropop', 'Hip Hop', 'Jazz', 'K-pop', 'Latin', 'Pop', 'Pop Rap', 'R&B', 'Rock']
audio_feats = ["acousticness", "danceability", "energy", "instrumentalness", "valence", "tempo"]
exploded_track_df = load_data()
def n_neighbors_uri_audio(genre, start_year, end_year, test_feat):
genre = genre.lower()
genre_data = exploded_track_df[(exploded_track_df["genres"]==genre) & (exploded_track_df["release_year"]>=start_year) & (exploded_track_df["release_year"]<=end_year)]
genre_data = genre_data.sort_values(by='popularity', ascending=False)[:500]
neigh = NearestNeighbors()
neigh.fit(genre_data[audio_feats].to_numpy())
n_neighbors = neigh.kneighbors([test_feat], n_neighbors=len(genre_data), return_distance=False)[0]
uris = genre_data.iloc[n_neighbors]["uri"].tolist()
audios = genre_data.iloc[n_neighbors][audio_feats].to_numpy()
return uris, audios
title = "Karaoke Performance Generator"
st.title(title)
st.write("Customize your set based on genre and several key audio features!")
st.markdown("##")
with st.container():
col1, col2,col3,col4 = st.columns((2,0.5,0.5,0.5))
with col3:
st.markdown("***Choose your genre:***")
genre = st.radio(
"",
genre_names, index=genre_names.index("Pop"))
with col1:
st.markdown("***Choose features to customize:***")
start_year, end_year = st.slider(
'Select the year range',
1990, 2019, (2015, 2019)
)
acousticness = st.slider(
'Acousticness',
0.0, 1.0, 0.5)
danceability = st.slider(
'Danceability',
0.0, 1.0, 0.5)
energy = st.slider(
'Energy',
0.0, 1.0, 0.5)
instrumentalness = st.slider(
'Instrumentalness',
0.0, 1.0, 0.0)
valence = st.slider(
'Valence',
0.0, 1.0, 0.45)
tempo = st.slider(
'Tempo',
0.0, 244.0, 118.0)
tracks_per_page = 6
test_feat = [acousticness, danceability, energy, instrumentalness, valence, tempo]
uris, audios = n_neighbors_uri_audio(genre, start_year, end_year, test_feat)
tracks = []
for uri in uris:
track = """<iframe src="https://open.spotify.com/embed/track/{}" width="260" height="380" frameborder="0" allowtransparency="true" allow="encrypted-media"></iframe>""".format(uri)
tracks.append(track)
if 'previous_inputs' not in st.session_state:
st.session_state['previous_inputs'] = [genre, start_year, end_year] + test_feat
current_inputs = [genre, start_year, end_year] + test_feat
if current_inputs != st.session_state['previous_inputs']:
if 'start_track_i' in st.session_state:
st.session_state['start_track_i'] = 0
st.session_state['previous_inputs'] = current_inputs
if 'start_track_i' not in st.session_state:
st.session_state['start_track_i'] = 0
with st.container():
col1, col2, col3 = st.columns([2,1,2])
if st.button("Recommend More Songs"):
if st.session_state['start_track_i'] < len(tracks):
st.session_state['start_track_i'] += tracks_per_page
current_tracks = tracks[st.session_state['start_track_i']: st.session_state['start_track_i'] + tracks_per_page]
current_audios = audios[st.session_state['start_track_i']: st.session_state['start_track_i'] + tracks_per_page]
if st.session_state['start_track_i'] < len(tracks):
for i, (track, audio) in enumerate(zip(current_tracks, current_audios)):
if i%2==0:
with col1:
components.html(
track,
height=400,
)
with st.expander("See more details"):
df = pd.DataFrame(dict(
r=audio[:5],
theta=audio_feats[:5]))
fig = px.line_polar(df, r='r', theta='theta', line_close=True)
fig.update_layout(height=400, width=340)
st.plotly_chart(fig)
else:
with col3:
components.html(
track,
height=400,
)
with st.expander("See more details"):
df = pd.DataFrame(dict(
r=audio[:5],
theta=audio_feats[:5]))
fig = px.line_polar(df, r='r', theta='theta', line_close=True)
fig.update_layout(height=400, width=340)
st.plotly_chart(fig)
else:
st.write("No songs left to recommend")
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