feat: updated website

app.py

@@ -362,21 +362,40 @@ elif page == "Customer Analysis":
                     # Ensure any missing sales data is filled with 0
                     results['ventas_reales'].fillna(0, inplace=True)
 
+                    # Define the cutoff date for the last 12 months
+                    fecha_corte = pd.to_datetime("2024-01-01")  # Adjust as needed
+                    fecha_inicio = fecha_corte - pd.DateOffset(months=12)
+
+                    # Filter historical data for the customer over the last 12 months
+                    datos_historicos = historical_data[
+                        (historical_data['cliente_id'] == customer_code_str) &
+                        (historical_data['fecha_mes'] >= fecha_inicio) & 
+                        (historical_data['fecha_mes'] < fecha_corte)
+                    ].groupby('fecha_mes')['precio_total'].sum().reset_index()
+
+                    # Rename 'precio_total' column to 'ventas_historicas'
+                    datos_historicos.rename(columns={'precio_total': 'ventas_historicas'}, inplace=True)
+
+                    # Filter prediction and real sales data and sum by month
+                    datos_cliente_total = results.groupby('fecha_mes').agg({
+                        'ventas_reales': 'sum',
+                        'ventas_predichas': 'sum'
+                    }).reset_index()
+
+                    # Combine historical data with prediction and real sales data
+                    datos_combinados = pd.concat([datos_historicos, datos_cliente_total], sort=False).sort_values('fecha_mes').reset_index(drop=True)
+
+                    # **Generate the graph**
+
                     st.markdown("### Sales History, Predictions, and Real Sales")
-                    fechas = pd.to_datetime(results['fecha_mes'])  # Extract the date
 
-                    # Extract the actual data for the plot
-                    ventas_historicas = customer_data['precio_total'].values[:12]  # Historical sales (first 12 months)
-                    ventas_predichas = results['ventas_predichas'].values  # Predicted sales (next months)
-                    ventas_reales = results['ventas_reales'].values  # Real sales (next months)
-                    
                     # Create the figure using Plotly
                     fig = go.Figure()
 
                     # Plot historical sales
                     fig.add_trace(go.Scatter(
-                        x=fechas[:12],  # First 12 months
-                        y=ventas_historicas, 
+                        x=datos_combinados['fecha_mes'],  # Dates
+                        y=datos_combinados['ventas_historicas'], 
                         mode='lines+markers', 
                         name='Ventas Históricas', 
                         line=dict(color='blue')
@@ -384,8 +403,8 @@ elif page == "Customer Analysis":
 
                     # Plot predicted sales
                     fig.add_trace(go.Scatter(
-                        x=fechas[12:],  # Remaining months
-                        y=ventas_predichas, 
+                        x=datos_combinados['fecha_mes'],  # Dates
+                        y=datos_combinados['ventas_predichas'], 
                         mode='lines+markers', 
                         name='Ventas Predichas', 
                         line=dict(color='orange')
@@ -393,8 +412,8 @@ elif page == "Customer Analysis":
 
                     # Plot real sales
                     fig.add_trace(go.Scatter(
-                        x=fechas[12:],  # Remaining months
-                        y=ventas_reales, 
+                        x=datos_combinados['fecha_mes'],  # Dates
+                        y=datos_combinados['ventas_reales'], 
                         mode='lines+markers', 
                         name='Ventas Reales', 
                         line=dict(color='green')