Update app.py

app.py

@@ -21,11 +21,14 @@ def load_data():
     df_display = df.drop(columns=[c for c in unnecessary_cols if c in df.columns])
     return df, df_display
 
-df, df_display = load_data()
+@st.cache_data
+def load_model_scaler():
+    model = joblib.load(MODEL_PATH)
+    scaler = joblib.load(SCALER_PATH)
+    return model, scaler
 
-# Model ve scaler yükle
-model = joblib.load(MODEL_PATH)
-scaler = joblib.load(SCALER_PATH)
+df, df_display = load_data()
+model, scaler = load_model_scaler()
 
 # -------------------------------
 # 2️⃣ Sayfa ayarları
@@ -41,22 +44,30 @@ st.subheader("📄 Dataset Preview / Veri Önizleme")
 st.dataframe(df_display.head(10), use_container_width=True)
 
 # -------------------------------
-# 4️⃣ Görselleştirme
+# 4️⃣ Görselleştirme (Titreme önlendi)
 # -------------------------------
 col1, col2 = st.columns(2)
 
 with col1:
     st.subheader("📊 Cluster Distribution / Küme Dağılımı")
-    st.bar_chart(df['cluster'].value_counts())
+    # Bar chart için st.bar_chart doğrudan kullanıyoruz
+    cluster_counts = df['cluster'].value_counts()
+    st.bar_chart(cluster_counts)
 
 with col2:
     st.subheader("🎯 Feature Analysis / Özellik Analizi")
-    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")
-    plt.colorbar(scatter, label="Cluster / Küme")
-    st.pyplot(fig, clear_figure=True)
+    # Matplotlib figürünü cache ile tutuyoruz, titremeyi önlüyoruz
+    @st.cache_data
+    def create_scatter(df):
+        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")
+        plt.colorbar(scatter, label="Cluster / Küme")
+        return fig
+
+    fig = create_scatter(df)
+    st.pyplot(fig, clear_figure=False)
     plt.close(fig)
 
 # -------------------------------
@@ -101,4 +112,4 @@ st.subheader("🔍 Cluster Characteristics / Küme Özellikleri (Ortalamalar)")
 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)