bugfix: Manufacturers Alerts

app.py

@@ -325,6 +325,10 @@ elif page == "Customer Analysis":
                 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:
+                    customer_data = customer_data[customer_data['marca_id_encoded'] == selected_manufacturer]
+
             with st.spinner("Generating sales predictions..."):
                 if not customer_data.empty:
                     # Define features consistently with the training process
@@ -348,6 +352,9 @@ elif page == "Customer Analysis":
 
                     # Load actual data from df_agg_2024
                     actual_sales = df_agg_2024[df_agg_2024['cliente_id'] == customer_code_str]
+
+                    if selected_manufacturer:
+                        actual_sales = actual_sales[actual_sales['marca_id_encoded'] == selected_manufacturer]
                     
                     if not actual_sales.empty:
                         # Merge predictions with actual sales
@@ -362,6 +369,12 @@ 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
+                    filtered_historical_data = historical_data[
+                        (historical_data['cliente_id'] == customer_code_str) & 
+                        (historical_data['marca_id_encoded'] == selected_manufacturer)
+                    ] if selected_manufacturer else historical_data[historical_data['cliente_id'] == customer_code_str]
+
                     # Define the cutoff date for the last 12 months
                     fecha_inicio = pd.to_datetime("2023-01-01")
                     fecha_corte = pd.to_datetime("2024-09-01")
@@ -954,33 +967,31 @@ elif page == "Customer Analysis":
 #                         st.warning("Sales data for 2021-2023 not available in the dataset.")
 
 
+# Customer Recommendations Page
 # Customer Recommendations Page
 elif page == "Articles Recommendations":
     st.title("Articles Recommendations")
-
     st.markdown("""
         Get tailored recommendations for your customers based on their basket.
     """)
-
     st.write("Select items and assign quantities for the basket:")
-
     # Mostrar lista de artículos disponibles
     available_articles = productos['ARTICULO'].unique()
     selected_articles = st.multiselect("Select Articles", available_articles)
-
     # Crear inputs para ingresar las cantidades de cada artículo seleccionado
     quantities = {}
     for article in selected_articles:
         quantities[article] = st.number_input(f"Quantity for {article}", min_value=0, step=1)
-
     if st.button("Calcular"):  # Añadimos el botón "Calcular"
-                # Crear una lista de artículos basada en la selección
-        new_basket = [f"{article} x{quantities[article]}" for article in selected_articles if quantities[article] > 0]
-
+        # Crear una lista de artículos basada en la selección de códigos y cantidades
+        new_basket = []
+        for article in selected_articles:
+            quantity = quantities[article]
+            if quantity > 0:
+                new_basket.extend([article] * quantity)  # Añadir el código 'article' tantas veces como 'quantity'
         if new_basket:
             # Procesar la lista para recomendar
             recommendations_df = recomienda_tfid(new_basket)
-
             if not recommendations_df.empty:
                 st.write("### Recommendations based on the current basket:")
                 st.dataframe(recommendations_df)