Layout Fix: Switched to Centered layout and 100% column-free Overview for total stability

app.py

@@ -21,8 +21,8 @@ SEGMENT_PRODUCTS_PATH = os.path.join(BASE_DIR, "outputs", "segment_products.csv"
 st.set_page_config(
     page_title="SegmentX: Customer Intelligence",
     page_icon="🛍️",
-    layout="wide",
-    initial_sidebar_state="expanded"
+    layout="centered",
+    initial_sidebar_state="auto"
 )
 
 # Custom Styling
@@ -37,22 +37,18 @@ st.markdown("""
         padding: 24px;
         border-radius: 12px;
         border: 1px solid #374151;
-        margin-bottom: 10px;
+        margin-bottom: 20px;
     }
     h1, h2, h3 {
         color: #60a5fa;
         font-family: 'Inter', sans-serif;
     }
-    .block-container {
-        padding: 2rem 10% 5rem 10%;
-    }
-    /* Simple Info Box */
+    /* Minimalist Info Box */
     .info-box {
         background-color: rgba(96, 165, 250, 0.1);
         padding: 20px;
         border-radius: 15px;
         border-left: 6px solid #60a5fa;
-        margin: 20px 0;
     }
 </style>
 """, unsafe_allow_html=True)
@@ -218,24 +214,22 @@ def main():
 
         st.markdown("---")
         
-        # Extraordinary Feature: Export & Churn Insights
-        e1, e2 = st.columns(2)
-        with e1:
-            st.subheader("📥 Export Segment List")
-            st.write(f"Download the list of **{len(df_filtered)}** customers in the selected segments for your next email campaign.")
-            csv = df_filtered.to_csv().encode('utf-8')
-            st.download_button("Download CSV", data=csv, file_name='customer_segments_export.csv', mime='text/csv')
-            
-        with e2:
-            st.subheader("🚨 Behavioral Analytics")
-            # Predictive Churn Insight: Aggregated for selection
-            high_risk = len(df_filtered[df_filtered['Recency'] > 90])
-            risk_pct = (high_risk / len(df_filtered)) * 100
-            st.write(f"**Selection Overview**: {risk_pct:.1f}% Customers are beyond a 90-day purchase gap.")
-            if risk_pct > 30:
-                st.warning("⚠️ High overall churn risk detected in selected segments.")
-            else:
-                st.success("✅ Healthy retention rate for these segments.")
+        # Extraordinary Feature: Export & Churn Insights - Absolute Vertical Stability
+        st.subheader("📥 Export Segment List")
+        st.write(f"Download the list of **{len(df_filtered)}** customers in the selected segments for your next email campaign.")
+        csv = df_filtered.to_csv().encode('utf-8')
+        st.download_button("Download CSV", data=csv, file_name='customer_segments_export.csv', mime='text/csv')
+        
+        st.markdown("<br>", unsafe_allow_html=True)
+        st.subheader("🚨 Behavioral Analytics")
+        # Predictive Churn Insight: Aggregated for selection
+        high_risk = len(df_filtered[df_filtered['Recency'] > 90])
+        risk_pct = (high_risk / len(df_filtered)) * 100
+        st.write(f"**Selection Overview**: {risk_pct:.1f}% Customers are beyond a 90-day purchase gap.")
+        if risk_pct > 30:
+            st.warning("⚠️ High overall churn risk detected in selected segments.")
+        else:
+            st.success("✅ Healthy retention rate for these segments.")
 
     elif page == "Segment Profiles":
         st.subheader("Cluster Behavioral Profiles")