feat: updated website

app.py

@@ -71,6 +71,12 @@ customer_clusters = pd.read_csv('predicts/customer_clusters.csv')  # Load the cu
 df_agg_2024 = pd.read_csv('predicts/df_agg_2024.csv')
 pca_data_5 = pd.read_csv('pca_data.csv')
 
+# Generamos la columna total_sales
+ventas_clientes['total_sales'] = ventas_clientes[['VENTA_2021', 'VENTA_2022', 'VENTA_2023']].sum(axis=1)
+# Ordenar los clientes de mayor a menor según sus ventas totales
+ventas_top_100 = ventas_clientes.sort_values(by='total_sales', ascending=False).head(100)
+
+
 # Ensure customer codes are strings
 df['CLIENTE'] = df['CLIENTE'].astype(str)
 nombres_proveedores['codigo'] = nombres_proveedores['codigo'].astype(str)
@@ -212,18 +218,26 @@ if page == "Summary":
     
     # Right Column (Blue): Key Metrics Overview and Data Preparation Summary
     with col3:
-        st.markdown('#### Key Metrics Overview')
-        st.write("""
-            - **Customers Analyzed**: 4,000
-            - **Suppliers Analyzed**: 400
-            - **Invoice Lines Processed**: 800,000
-        """)
-
-        st.markdown('#### Data Preparation Summary')
-        st.write("""
-            - Cleaned and standardized product codes and descriptions.
-            - Excluded customers with fewer than 12 purchases or sales below €1,200.
-        """)
+        # Mostrar la tabla con los 100 mejores clientes
+        st.markdown('#### Top 100 Clients by Total Sales')
+
+        # Configurar columnas para mostrar los clientes y las ventas totales
+        st.dataframe(ventas_top_100[['codigo_cliente', 'total_sales']],
+                    column_order=("codigo_cliente", "total_sales"),
+                    hide_index=True,
+                    width=None,
+                    column_config={
+                        "codigo_cliente": st.column_config.TextColumn(
+                            "Client Code",
+                        ),
+                        "total_sales": st.column_config.ProgressColumn(
+                            "Total Sales (€)",
+                            format="%d",
+                            min_value=0,
+                            max_value=ventas_top_100['total_sales'].max(),
+                            bar_color="blue",  # Cambiar el color si lo prefieres
+                        )}
+                    )
 
 # Customer Analysis Page
 elif page == "Customer Analysis":