Update app.py

app.py

@@ -3,9 +3,9 @@ import pandas as pd
 import joblib
 import matplotlib.pyplot as plt
 
-# --------------------------------------
-# 1️⃣ Dosya Yolları
-# --------------------------------------
+# -------------------------------
+# 1️⃣ Dosya yolları
+# -------------------------------
 DATA_PATH = "spotify_clustered.csv"
 MODEL_PATH = "kmeans_music_model.pkl"
 SCALER_PATH = "scaler_music.pkl"
@@ -17,32 +17,32 @@ def load_data():
     except FileNotFoundError:
         st.error("spotify_clustered.csv bulunamadı! Lütfen root klasöre yükleyin.")
         st.stop()
-    # Gereksiz sütunları kaldır
     unnecessary_cols = ['Unnamed: 0', 'track_id', 'album_name', 'explicit']
     df_display = df.drop(columns=[c for c in unnecessary_cols if c in df.columns])
     return df, df_display
 
-# Veri ve model yükle
 df, df_display = load_data()
+
+# Model ve scaler yükle
 model = joblib.load(MODEL_PATH)
 scaler = joblib.load(SCALER_PATH)
 
-# --------------------------------------
-# 2️⃣ Sayfa Ayarları
-# --------------------------------------
+# -------------------------------
+# 2️⃣ Sayfa ayarları
+# -------------------------------
 st.set_page_config(page_title="Spotify Clusters", layout="wide")
 st.title("🎵 Spotify Music Clustering / Spotify Müzik Kümeleme")
 st.markdown("---")
 
-# --------------------------------------
-# 3️⃣ Veri Önizleme
-# --------------------------------------
+# -------------------------------
+# 3️⃣ Veri önizleme
+# -------------------------------
 st.subheader("📄 Dataset Preview / Veri Önizleme")
 st.dataframe(df_display.head(10), use_container_width=True)
 
-# --------------------------------------
+# -------------------------------
 # 4️⃣ Görselleştirme
-# --------------------------------------
+# -------------------------------
 col1, col2 = st.columns(2)
 
 with col1:
@@ -51,7 +51,7 @@ with col1:
 
 with col2:
     st.subheader("🎯 Feature Analysis / Özellik Analizi")
-    fig, ax = plt.subplots(figsize=(8, 5))
+    fig, ax = plt.subplots(figsize=(8,5))
     scatter = ax.scatter(df['danceability'], df['energy'], c=df['cluster'], cmap='viridis', alpha=0.6)
     ax.set_xlabel("Danceability / Dans Edilebilirlik")
     ax.set_ylabel("Energy / Enerji")
@@ -59,9 +59,9 @@ with col2:
     st.pyplot(fig, clear_figure=True)
     plt.close(fig)
 
-# --------------------------------------
-# 5️⃣ Tahmin Bölümü
-# --------------------------------------
+# -------------------------------
+# 5️⃣ Tahmin bölümü
+# -------------------------------
 st.divider()
 st.subheader("🤖 Predict New Song Cluster / Yeni Şarkı Tahmini")
 st.info("Adjust sliders to see which cluster a song belongs to / Sürgüleri ayarlayın.")
@@ -93,12 +93,12 @@ if st.button("Predict Cluster / Kümeyi Tahmin Et ✨"):
     except Exception as e:
         st.error(f"Prediction Error / Tahmin Hatası: {e}")
 
-# --------------------------------------
-# 6️⃣ Küme Ortalamaları
-# --------------------------------------
+# -------------------------------
+# 6️⃣ Küme ortalamaları
+# -------------------------------
 st.divider()
 st.subheader("🔍 Cluster Characteristics / Küme Özellikleri (Ortalamalar)")
-numeric_only = df.select_dtypes(include=['float64', 'int64'])
+numeric_only = df.select_dtypes(include=['float64','int64'])
 if 'cluster' in df.columns:
     means = numeric_only.groupby(df['cluster']).mean()
-    st.dataframe(means, use_container_width=True)
+    st.dataframe(means, use_container_width=True)