bugfix: Manufacturers Alerts

app.py

@@ -78,6 +78,9 @@ with st.sidebar:
     if page == "Customer Analysis":
         st.sidebar.title("Filter Options")
         show_all = st.sidebar.checkbox('Show All Manufacturers', value=True)
+        
+        # Initialize selected_manufacturer
+        selected_manufacturer = None
 
         # If not showing all, allow filtering by manufacturer code
         if not show_all:
@@ -298,6 +301,18 @@ elif page == "Customer Analysis":
         help="Start typing to search for a specific customer code"
     )
 
+    # If show_all is checked, selected_manufacturer will be None
+    selected_manufacturer = None
+
+    # Add a checkbox for showing all manufacturers
+    show_all = st.sidebar.checkbox('Show All Manufacturers', value=True)
+    if not show_all:
+        # If not showing all manufacturers, allow user to select a specific manufacturer
+        selected_manufacturer = st.sidebar.selectbox(
+            'Select Manufacturer Code', 
+            historical_data['marca_id_encoded'].unique()
+        )
+
     if st.button("Calcular"):
         if customer_code:
             with st.spinner("We are identifying the customer's cluster..."):
@@ -321,13 +336,27 @@ elif page == "Customer Analysis":
                 predict_data['cliente_id'] = predict_data['cliente_id'].astype(str)
 
             with st.spinner("Filtering data..."):
-                # Filter for the specific customer
-                customer_code_str = str(customer_code)
-                customer_data = predict_data[predict_data['cliente_id'] == customer_code_str]
-
-                    # Apply filter for the selected manufacturer if not showing all manufacturers
+                # If selected_manufacturer is not None, filter by manufacturer, otherwise use all
                 if selected_manufacturer:
-                    customer_data = customer_data[customer_data['marca_id_encoded'] == selected_manufacturer]
+                    customer_data = predict_data[predict_data['marca_id_encoded'] == selected_manufacturer]
+                else:
+                    # Sum all manufacturers if no specific manufacturer is selected
+                    customer_data = predict_data.groupby(['cliente_id', 'fecha_mes']).agg({
+                        'precio_total': 'sum',
+                        'ventas_predichas': 'sum'
+                    }).reset_index()
+
+            # Historical data filtering based on whether a manufacturer is selected or not
+            if selected_manufacturer:
+                filtered_historical_data = historical_data[
+                    (historical_data['cliente_id'] == customer_code) & 
+                    (historical_data['marca_id_encoded'] == selected_manufacturer)
+                ]
+            else:
+                # Sum the historical data for all manufacturers if none is selected
+                filtered_historical_data = historical_data[
+                    historical_data['cliente_id'] == customer_code
+                ].groupby('fecha_mes')['precio_total'].sum().reset_index()
 
             with st.spinner("Generating sales predictions..."):
                 if not customer_data.empty:
@@ -351,7 +380,7 @@ elif page == "Customer Analysis":
                     results['ventas_predichas'] = y_pred
 
                     # Load actual data from df_agg_2024
-                    actual_sales = df_agg_2024[df_agg_2024['cliente_id'] == customer_code_str]
+                    actual_sales = df_agg_2024[df_agg_2024['cliente_id'] == customer_code]
 
                     if selected_manufacturer:
                         actual_sales = actual_sales[actual_sales['marca_id_encoded'] == selected_manufacturer]
@@ -369,11 +398,11 @@ elif page == "Customer Analysis":
                     # Ensure any missing sales data is filled with 0
                     results['ventas_reales'].fillna(0, inplace=True)
 
-                    # Filtrar los datos históricos por cliente y fabricante, si se ha seleccionado uno
+                    # Filter historical data by customer and manufacturer, if one is selected
                     filtered_historical_data = historical_data[
-                        (historical_data['cliente_id'] == customer_code_str) & 
+                        (historical_data['cliente_id'] == customer_code) & 
                         (historical_data['marca_id_encoded'] == selected_manufacturer)
-                    ] if selected_manufacturer else historical_data[historical_data['cliente_id'] == customer_code_str]
+                    ] if selected_manufacturer else historical_data[historical_data['cliente_id'] == customer_code]
 
                     # Define the cutoff date for the last 12 months
                     fecha_inicio = pd.to_datetime("2023-01-01")