bugfix: Manufacturers Alerts

app.py

@@ -284,6 +284,7 @@ if page == "Summary":
                         )}
                     )
 # Customer Analysis Page
+# Customer Analysis Page
 elif page == "Customer Analysis":
     st.markdown("""
     <h2 style='text-align: center; font-size: 2.5rem;'>Customer Analysis</h2>
@@ -297,6 +298,7 @@ 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"
     )
@@ -323,35 +325,10 @@ elif page == "Customer Analysis":
                 # Convert cliente_id to string
                 predict_data['cliente_id'] = predict_data['cliente_id'].astype(str)
 
-                st.write("Columns in predict_data:", predict_data.columns)
-                st.write("First few rows of predict_data:")
-                st.write(predict_data.head())
-
             with st.spinner("Filtering data..."):
-                # If selected_manufacturer is not None, filter by manufacturer, otherwise use all
-                if 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()
-
-                st.write("Filtered customer_data:")
-                st.write(customer_data.head())
-
-            # 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()
+                # Filter for the specific customer
+                customer_code_str = str(customer_code)
+                customer_data = predict_data[predict_data['cliente_id'] == customer_code_str]
 
             with st.spinner("Generating sales predictions..."):
                 if not customer_data.empty:
@@ -375,10 +352,7 @@ 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]
-
-                    if selected_manufacturer:
-                        actual_sales = actual_sales[actual_sales['marca_id_encoded'] == selected_manufacturer]
+                    actual_sales = df_agg_2024[df_agg_2024['cliente_id'] == customer_code_str]
                     
                     if not actual_sales.empty:
                         # Merge predictions with actual sales
@@ -393,12 +367,6 @@ elif page == "Customer Analysis":
                     # Ensure any missing sales data is filled with 0
                     results['ventas_reales'].fillna(0, inplace=True)
 
-                    # Filter historical data by customer and manufacturer, if one is selected
-                    filtered_historical_data = historical_data[
-                        (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]
-
                     # 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")