feat: updated website

app.py

@@ -59,10 +59,6 @@ st.markdown(f"""
     </style>
 """, unsafe_allow_html=True)
 
-# Navigation menu
-with st.sidebar:
-    st.sidebar.title("DeepInsightz")
-    page = st.sidebar.selectbox("Select the tool you want to use", ["Summary", "Customer Analysis", "Articles Recommendations"])
 
 # Load CSV files at the top
 df = pd.read_csv("df_clean.csv")
@@ -72,6 +68,23 @@ ventas_clientes = pd.read_csv("ventas_clientes.csv", sep=',')
 customer_clusters = pd.read_csv('predicts/customer_clusters.csv')  # Load the customer clusters here
 df_agg_2024 = pd.read_csv('predicts/df_agg_2024.csv')
 pca_data_5 = pd.read_csv('pca_data.csv')
+historical_data = pd.read_csv('historical_data.csv')
+
+with st.sidebar:
+    st.sidebar.title("DeepInsightz")
+    page = st.sidebar.selectbox("Select the tool you want to use", ["Summary", "Customer Analysis", "Articles Recommendations"])
+
+    # If the user selects "Customer Analysis," show filter options in the sidebar
+    if page == "Customer Analysis":
+        st.sidebar.title("Filter Options")
+        show_all = st.sidebar.checkbox('Show All Manufacturers', value=True)
+
+        # If not showing all, allow filtering by manufacturer code
+        if not show_all:
+            selected_manufacturer = st.sidebar.selectbox(
+                'Select Manufacturer Code', 
+                historical_data['marca_id_encoded'].unique()
+            )
 
 # Generamos la columna total_sales
 ventas_clientes['total_sales'] = ventas_clientes[['VENTA_2021', 'VENTA_2022', 'VENTA_2023']].sum(axis=1)
@@ -280,6 +293,7 @@ elif page == "Customer Analysis":
     customer_code = st.selectbox(
         "Search and Select Customer Code",
         df['CLIENTE'].unique(),  # All customer codes
+
         format_func=lambda x: str(x),  # Ensures the values are displayed as strings
         help="Start typing to search for a specific customer code"
     )
@@ -348,6 +362,57 @@ elif page == "Customer Analysis":
                     # Ensure any missing sales data is filled with 0
                     results['ventas_reales'].fillna(0, inplace=True)
 
+                    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, 
+                        mode='lines+markers', 
+                        name='Ventas Históricas', 
+                        line=dict(color='blue')
+                    ))
+
+                    # Plot predicted sales
+                    fig.add_trace(go.Scatter(
+                        x=fechas[12:],  # Remaining months
+                        y=ventas_predichas, 
+                        mode='lines+markers', 
+                        name='Ventas Predichas', 
+                        line=dict(color='orange')
+                    ))
+
+                    # Plot real sales
+                    fig.add_trace(go.Scatter(
+                        x=fechas[12:],  # Remaining months
+                        y=ventas_reales, 
+                        mode='lines+markers', 
+                        name='Ventas Reales', 
+                        line=dict(color='green')
+                    ))
+
+                    # Customize the layout
+                    fig.update_layout(
+                        title=f"Ventas Históricas, Predichas y Reales para Cliente {customer_code}",
+                        xaxis_title="Fecha",
+                        yaxis_title="Ventas (€)",
+                        height=600,
+                        legend_title="Tipo de Ventas",
+                        hovermode="x unified"
+                    )
+
+                    # Display the Plotly figure
+                    st.plotly_chart(fig)
+
                     # Split space into two columns
                     col1, col2 = st.columns(2)