feat: updated website

app.py

@@ -609,66 +609,75 @@ elif page == "Customer Analysis":
 
                         st.plotly_chart(fig_comparison, use_container_width=True)
 
-                    # Gráfico de ventas anuales
-                    ventas_clientes['codigo_cliente'] = ventas_clientes['codigo_cliente'].astype(str).str.strip()
-
-                    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']
-
-                            # Add the 2024 actual and predicted data
-                            if 'ventas_predichas' in results.columns and 'ventas_reales' in results.columns:
-                                actual_sales_2024 = results[results['fecha_mes'].dt.year == 2024]['ventas_reales'].sum()
-                                predicted_sales_2024 = results[results['fecha_mes'].dt.year == 2024]['ventas_predichas'].sum()
-
-                                months_available = 9
-                                actual_sales_2024_annual = (actual_sales_2024 / months_available) * 12
-
-                                sales_values = list(customer_sales) + [actual_sales_2024_annual]
-                                predicted_values = list(customer_sales) + [predicted_sales_2024]
-
-                                years.append('2024')
-
-                                fig_sales_bar = go.Figure()
-                                fig_sales_bar.add_trace(go.Bar(
-                                    x=years[:3],  
-                                    y=sales_values[:3],
-                                    name="Historical Sales",
-                                    marker_color='blue'
-                                ))
-
-                                fig_sales_bar.add_trace(go.Bar(
-                                    x=[years[3]],  
-                                    y=[sales_values[3]],
-                                    name="2024 Actual Sales (Annualized)",
-                                    marker_color='green'
-                                ))
-
-                                fig_sales_bar.add_trace(go.Bar(
-                                    x=[years[3]],  
-                                    y=[predicted_values[3]],
-                                    name="2024 Predicted Sales",
-                                    marker_color='orange'
-                                ))
-
-                                fig_sales_bar.update_layout(
-                                    title=f"Sales Over the Years for Customer {customer_code}",
-                                    xaxis_title="Year",
-                                    yaxis_title="Sales (€)",
-                                    barmode='group',
-                                    height=600,
-                                    legend_title_text="Sales Type",
-                                    hovermode="x unified"
-                                )
-
-                                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.")
+                        # Gráfico de ventas anuales
+                        ventas_clientes['codigo_cliente'] = ventas_clientes['codigo_cliente'].astype(str).str.strip()
+
+                        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']
+
+                                # Convert 'fecha_mes' to datetime format if it's not already
+                                if not pd.api.types.is_datetime64_any_dtype(results['fecha_mes']):
+                                    results['fecha_mes'] = pd.to_datetime(results['fecha_mes'], errors='coerce')
+
+                                # Add the 2024 actual and predicted data
+                                if 'ventas_predichas' in results.columns and 'ventas_reales' in results.columns:
+                                    actual_sales_2024 = results[results['fecha_mes'].dt.year == 2024]['ventas_reales'].sum()
+                                    predicted_sales_2024 = results[results['fecha_mes'].dt.year == 2024]['ventas_predichas'].sum()
+
+                                    # Assuming only 9 months of actual data are available, annualize the sales
+                                    months_available = 9
+                                    actual_sales_2024_annual = (actual_sales_2024 / months_available) * 12
+
+                                    # Prepare data for the bar chart
+                                    sales_values = list(customer_sales) + [actual_sales_2024_annual]
+                                    predicted_values = list(customer_sales) + [predicted_sales_2024]
+
+                                    years.append('2024')
+
+                                    # Create the bar chart for historical and 2024 data
+                                    fig_sales_bar = go.Figure()
+                                    fig_sales_bar.add_trace(go.Bar(
+                                        x=years[:3],  
+                                        y=sales_values[:3],
+                                        name="Historical Sales",
+                                        marker_color='blue'
+                                    ))
+
+                                    fig_sales_bar.add_trace(go.Bar(
+                                        x=[years[3]],  
+                                        y=[sales_values[3]],
+                                        name="2024 Actual Sales (Annualized)",
+                                        marker_color='green'
+                                    ))
+
+                                    fig_sales_bar.add_trace(go.Bar(
+                                        x=[years[3]],  
+                                        y=[predicted_values[3]],
+                                        name="2024 Predicted Sales",
+                                        marker_color='orange'
+                                    ))
+
+                                    # Customize layout
+                                    fig_sales_bar.update_layout(
+                                        title=f"Sales Over the Years for Customer {customer_code}",
+                                        xaxis_title="Year",
+                                        yaxis_title="Sales (€)",
+                                        barmode='group',
+                                        height=600,
+                                        legend_title_text="Sales Type",
+                                        hovermode="x unified"
+                                    )
+
+                                    # Display the chart
+                                    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.")