feat: updated website

app.py

@@ -430,22 +430,66 @@ elif page == "Customer Analysis":
                     sales_columns = ['VENTA_2021', 'VENTA_2022', 'VENTA_2023']
                     if all(col in ventas_clientes.columns for col in sales_columns):
                         customer_sales_data = ventas_clientes[ventas_clientes['codigo_cliente'] == customer_code]
-                        
+
                         if not customer_sales_data.empty:
                             customer_sales = customer_sales_data[sales_columns].values[0]
                             years = ['2021', '2022', '2023']
-                            
-                            fig_sales = px.line(x=years, y=customer_sales, markers=True, title=f'Sales Over the Years for Customer {customer_code}')
-                            fig_sales.update_layout(xaxis_title="Year", yaxis_title="Sales")
-                            st.plotly_chart(fig_sales)
+
+                            # Add the 2024 actual and predicted data
+                            if 'ventas_predichas' in results.columns and 'ventas_reales' in results.columns:
+                                # Get the actual and predicted sales for 2024
+                                actual_sales_2024 = results[results['fecha_mes'].str.startswith('2024')]['ventas_reales'].sum()
+                                predicted_sales_2024 = results[results['fecha_mes'].str.startswith('2024')]['ventas_predichas'].sum()
+
+                                # Estimate full-year predicted sales (assuming predictions available until September)
+                                months_available = 9  # Data available until September
+                                predicted_sales_2024_annual = (predicted_sales_2024 / months_available) * 12
+
+                                # Add 2024 actual and predicted sales
+                                sales_values = list(customer_sales) + [actual_sales_2024]  # Actual sales
+                                predicted_values = list(customer_sales) + [predicted_sales_2024_annual]  # Predicted sales
+
+                                # Add 2024 to the years list
+                                years.append('2024')
+
+                                # Create an interactive bar chart using Plotly
+                                fig_sales_bar = px.bar(
+                                    x=years,
+                                    y=[sales_values, predicted_values],
+                                    labels={"x": "Year", "y": "Sales (€)"},
+                                    barmode='group',
+                                    title=f"Sales Over the Years for Customer {customer_code} (Actual vs Predicted)"
+                                )
+
+                                # Update the layout to make the bar chart interactive
+                                fig_sales_bar.update_layout(
+                                    yaxis_title="Sales (€)",
+                                    xaxis_title="Year",
+                                    hovermode="x unified",
+                                    height=600,
+                                    legend_title_text="Sales Type"
+                                )
+
+                                # Add traces for actual and predicted sales
+                                fig_sales_bar.add_trace(
+                                    px.bar(x=years, y=sales_values, name="Actual Sales", marker_color='blue').data[0]
+                                )
+
+                                fig_sales_bar.add_trace(
+                                    px.bar(x=years, y=predicted_values, name="Predicted Sales (Annualized for 2024)", marker_color='orange').data[0]
+                                )
+
+                                # Show the interactive bar chart in Streamlit
+                                st.plotly_chart(fig_sales_bar, use_container_width=True)
+
+                            else:
+                                st.warning(f"No predicted or actual data found for customer {customer_code} for 2024.")
+
                         else:
                             st.warning(f"No historical sales data found for customer {customer_code}")
+
                     else:
                         st.warning("Sales data for 2021-2023 not available in the dataset.")
-                else:
-                    st.warning(f"No data found for customer {customer_code}. Please check the code.")
-        else:
-            st.warning("Please select a customer.")
 
 
 # Customer Recommendations Page