Feature Recovery: Restored Top Products, Pareto, Churn Probability, and CLV with fixed Plotly color attributes

app.py

@@ -158,7 +158,7 @@ def main():
                 values=counts.values, 
                 names=counts.index, 
                 hole=0.5,
-                color_discrete_sequence=px.colors.sequential.Ice_r
+                color_discrete_sequence=px.colors.sequential.ice_r
             )
             fig.update_layout(
                 paper_bgcolor='rgba(0,0,0,0)', 
@@ -175,7 +175,7 @@ def main():
             fig = px.scatter(
                 pca_df, x='PCA1', y='PCA2', color='Segment',
                 opacity=0.6,
-                color_discrete_sequence=px.colors.sequential.Ice_r,
+                color_discrete_sequence=px.colors.sequential.ice_r,
                 hover_data=[pca_df.index]
             )
             fig.update_layout(
@@ -211,6 +211,26 @@ def main():
             fig.update_traces(line_width=3, fill='tozeroy', fillcolor='rgba(59, 130, 246, 0.1)')
             st.plotly_chart(fig, use_container_width=True)
 
+        st.markdown("<br>", unsafe_allow_html=True)
+        st.markdown("### 📊 Revenue Concentration (Pareto)")
+        seg_rev = df_filtered.groupby('Segment')['Monetary'].sum().sort_values(ascending=False).reset_index()
+        fig_bar = px.bar(
+            seg_rev, x='Segment', y='Monetary',
+            color='Monetary',
+            color_continuous_scale='ice'
+        )
+        fig_bar.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(15, 23, 42, 0.5)', font_color="#f8fafc")
+        st.plotly_chart(fig_bar, use_container_width=True)
+
+        st.markdown("---")
+        st.subheader("🚨 Risk Analytics")
+        high_risk = len(df_filtered[df_filtered['Recency'] > 90])
+        risk_pct = (high_risk / len(df_filtered)) * 100
+        if risk_pct > 30:
+            st.warning(f"**Critical Warning**: {risk_pct:.1f}% of selected customers are churn-risk (90+ days inactive).")
+        else:
+            st.success(f"**Healthy Signal**: Retention is stable with only {risk_pct:.1f}% churn-risk.")
+
         st.markdown("<br>", unsafe_allow_html=True)
         st.subheader("📥 Data Export & Actions")
         csv = df_filtered.to_csv().encode('utf-8')
@@ -232,6 +252,24 @@ def main():
         st.markdown("#### Mean Values Matrix")
         st.table(profile_stats.style.format(lambda x: f"£{x:,.2f}" if x > 100 else f"{x:.2f}"))
 
+        st.markdown("---")
+        st.subheader("🛍️ Segment Affinity: Top 10 Products")
+        if os.path.exists(SEGMENT_PRODUCTS_PATH):
+            all_top_prods = pd.read_csv(SEGMENT_PRODUCTS_PATH)
+            display_segs = selected_segments[:3]
+            cols = st.columns(len(display_segs)) if display_segs else [st.container()]
+            
+            for i, seg in enumerate(display_segs):
+                with cols[i]:
+                    st.markdown(f"**{seg}**")
+                    seg_prods = all_top_prods[all_top_prods['Segment'] == seg].head(10)
+                    if not seg_prods.empty:
+                        seg_prods['Description'] = seg_prods['Description'].str.slice(0, 30) + '...'
+                        st.table(seg_prods[['Description', 'Quantity']].set_index('Description'))
+                    else: st.info("No data.")
+        else:
+            st.info("Run product pipeline to see affinities.")
+
     elif page == "Customer Lookup":
         st.subheader("🔍 Intelligent Query")
         
@@ -258,14 +296,28 @@ def main():
         l3.metric("LTV GBP", f"£{cust_data['Monetary']:,.2f}")
         
         st.markdown("---")
-        st.markdown("### 🛡️ Strategic Recommendation")
+        st.markdown("### 🛡️ Strategic Intelligence")
+        
+        ci1, ci2 = st.columns(2)
+        # Churn Probability Logic
+        avg_rec = df['Recency'].mean()
+        churn_prob = 1 - np.exp(-cust_data['Recency'] / (avg_rec * 1.5))
+        churn_pct = min(max(churn_prob * 100, 0), 100)
+        ci1.metric("Churn Risk Score", f"{churn_pct:.1f}%")
+        
+        # Predicted CLV
+        avg_order = cust_data['Monetary'] / cust_data['Frequency']
+        projected_clv = cust_data['Monetary'] + (avg_order * cust_data['Frequency'])
+        ci2.metric("Projected 1Y-LTV", f"£{projected_clv:,.2f}")
+
+        st.markdown("<br>", unsafe_allow_html=True)
         recommendations = {
             "Champions": "High Value, Low Churn. Goal: Retention. Strategy: Early Access, Loyalty Rewards.",
             "Loyal Customers": "Consistent Value. Goal: Growth. Strategy: Cross-sell related categories.",
             "At-Risk": "Recent Inactivity. Goal: Re-activation. Strategy: Limited-time win-back discounts.",
             "Lost/Hibernating": "Historical only. Goal: Win-back or Pause. Strategy: Reactivate only high LTV types."
         }
-        st.info(recommendations.get(cust_data['Segment'], "Maintain baseline engagement."))
+        st.info(f"**Execution Strategy**: {recommendations.get(cust_data['Segment'], 'Maintain baseline engagement.')}")
 
 if __name__ == "__main__":
     main()