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app.py

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+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")

final_music_data.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:46783ba5dca1b249c97f1eaf06162acb1243c4826fc397d07498406c760709ee
+size 227193

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c5a84ecceac33f1e27c541c08fd306700339c6bb4879089864100c73cd965df5
+size 8567