File size: 4,217 Bytes
71a92ec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | import streamlit as st
import pandas as pd
import joblib
import os
# 1. Page Configuration
st.set_page_config(page_title="BgemBox Music Engine", layout="wide", page_icon="🎵")
# Custom CSS to improve font size and table padding
st.markdown("""
<style>
.main .block-container {padding-top: 2rem;}
th {background-color: #f0f2f6 !important; font-size: 16px !important;}
td {font-size: 15px !important;}
</style>
""", unsafe_allow_html=True)
# 2. Load Scaler and Data
@st.cache_resource
def load_assets():
# 1. Veriyi yükle
data = pd.read_pickle("final_music_data.pkl")
# 2. Scaler'ı yükle
scaler = joblib.load("scaler.pkl")
# 3. Kümeleme sütunlarını kontrol et (Return'den ÖNCE olmalı)
if 'sub_cluster' not in data.columns:
if 'cluster' in data.columns:
data['sub_cluster'] = data['cluster']
else:
# st.error burada çalışmayabilir, konsola yazdıralım
print("Critical Error: No clustering columns found!")
# İki nesneyi birden döndür
return data, scaler
try:
# Fonksiyon iki değer döndürdüğü için ikisini de ayrı ayrı almalıyız
df, music_scaler = load_assets()
except Exception as e:
st.error(f"Asset Loading Error: {e}")
st.stop()
# 3. Sidebar Configuration
st.sidebar.title("Music Engine Settings")
st.sidebar.markdown("---")
st.sidebar.write("This AI-powered engine clusters music based on technical audio features like BPM, Energy, and Acousticness.")
st.sidebar.info("Developed by Elif | 2026")
# 4. Cluster Labels
cluster_names = {
8.0: "🌟 Mainstream Pop Hits",
1.0: "🎸 Classic Rock & Dynamic Rhythms",
2.0: "🎹 Alternative & Indie Vibes",
7.0: "📜 Nostalgic Oldies",
3.0: "🌊 Chill & Low-Fi Moods",
4.0: "⚡ High-Energy / Gym Motivation",
0.0: "💎 Unique Rare Finds",
5.0: "💎 Unique Rare Finds",
6.0: "💎 Unique Rare Finds"
}
# 5. Main UI
st.title("🎵 BgemBox Music Recommendation System")
st.subheader("Discover music through Data Science")
st.markdown("---")
tab1, tab2 = st.tabs(["Search by Artist", "Discover by Mood"])
# TAB 1: Recommendation by Artist
with tab1:
st.write("### Find Similar Artists")
artist_list = sorted(df['Artist'].unique())
selected_artist = st.selectbox("Select an Artist you like:", artist_list)
if st.button("Recommend Similar Music"):
artist_data = df[df['Artist'] == selected_artist].iloc[0]
cluster_id = artist_data['sub_cluster']
friendly_name = cluster_names.get(cluster_id, "Similar Style Tracks")
st.success(f"Artist **{selected_artist}** belongs to the **{friendly_name}** segment.")
recommendations = df[(df['sub_cluster'] == cluster_id) & (df['Artist'] != selected_artist)]
recommendations = recommendations.sort_values(by='Popularity', ascending=False).head(5)
st.write("#### Recommended for you:")
# Used st.table for better readability and tighter columns
st.table(recommendations[['Artist', 'Top Genre', 'Popularity']])
# TAB 2: Discover by Mood
with tab2:
st.write("### How are you feeling today?")
selected_mood_name = st.selectbox("Select a Mood:", list(cluster_names.values()))
mood_id = [k for k, v in cluster_names.items() if v == selected_mood_name][0]
if st.button("Generate Playlist"):
mood_list = df[df['sub_cluster'] == mood_id]
if not mood_list.empty:
# Taking a random sample of 10
playlist = mood_list.sample(min(len(mood_list), 10))
st.write(f"#### Your {selected_mood_name} Playlist:")
# Use st.table here instead of dataframe for better font size and compact columns
st.table(playlist[['Artist', 'Top Genre', 'Popularity', 'Energy', 'Beats Per Minute (BPM)']])
else:
st.warning("This specific cluster is currently empty or has only one track.")
# 6. Footer
st.markdown("---")
st.caption("© 2026 Data Science Specialization Project")
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