bugfix: Manufacturers Alerts

app.py

@@ -680,285 +680,34 @@ elif page == "Customer Analysis":
                                 else:
                                     st.warning(f"No predicted or actual data found for customer {customer_code} for 2024.")
 
-
-
-# elif page == "Customer Analysis":
-#     st.markdown("""
-#     <h2 style='text-align: center; font-size: 2.5rem;'>Customer Analysis</h2>
-#     <p style='text-align: center; font-size: 1.2rem; color: gray;'> 
-#     Enter the customer code to explore detailed customer insights, 
-#     including past sales, predictions for the current year, and manufacturer-specific information.
-#     </p>
-#     """, unsafe_allow_html=True)
-
-#     # Combine text input and dropdown into a single searchable selectbox
-#     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"
-#     )
-
-#     if st.button("Calcular"):
-#         if customer_code:
-#             with st.spinner("We are identifying the customer's cluster..."):
-#                 # Find Customer's Cluster
-#                 customer_match = customer_clusters[customer_clusters['cliente_id'] == customer_code]
-#                 time.sleep(1)
-                
-
-#                 if not customer_match.empty:
-#                     cluster = customer_match['cluster_id'].values[0]
-                
-#             with st.spinner(f"Selecting predictive model..."):
-#                 # Load the Corresponding Model
-#                 model_path = f'models/modelo_cluster_{cluster}.txt'
-#                 gbm = lgb.Booster(model_file=model_path)
-
-#             with st.spinner("Getting the data ready..."):
-#                 # Load predict data for that cluster
-#                 predict_data = pd.read_csv(f'predicts/predict_cluster_{cluster}.csv')
-                
-#                 # Convert cliente_id to string
-#                 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]
-
-#             with st.spinner("Generating sales predictions..."):       
-
-#                 if not customer_data.empty:
-#                     # Define features consistently with the training process
-#                     lag_features = [f'precio_total_lag_{lag}' for lag in range(1, 25)]
-#                     features = lag_features + ['mes', 'marca_id_encoded', 'año', 'cluster_id']
-    
-#                     # Prepare data for prediction
-#                     X_predict = customer_data[features]
-
-#                     # Convert categorical features to 'category' dtype
-#                     categorical_features = ['mes', 'marca_id_encoded', 'cluster_id']
-#                     for feature in categorical_features:
-#                         X_predict[feature] = X_predict[feature].astype('category')
-                    
-#                     # Make Prediction for the selected customer
-#                     y_pred = gbm.predict(X_predict, num_iteration=gbm.best_iteration)
-
-#                     # Reassemble the results
-#                     results = customer_data[['cliente_id', 'marca_id_encoded', 'fecha_mes']].copy()
-#                     results['ventas_predichas'] = y_pred
-
-#                     # Load actual data
-#                     actual_sales = df_agg_2024[df_agg_2024['cliente_id'] == customer_code_str]
-                    
-#                     if not actual_sales.empty:
-#                         results = results.merge(actual_sales[['cliente_id', 'marca_id_encoded', 'fecha_mes', 'precio_total']], 
-#                                                 on=['cliente_id', 'marca_id_encoded', 'fecha_mes'], 
-#                                                 how='left')
-#                         results.rename(columns={'precio_total': 'ventas_reales'}, inplace=True)
-#                         results['ventas_reales'].fillna(0, inplace=True)
-#                         # st.write("### Final Results DataFrame:")
-#                         # st.write(results.head())
-#                         # st.write(f"Shape: {results.shape}")
-                        
-#                         # Calculate metrics only for non-null actual sales
-#                         valid_results = results.dropna(subset=['ventas_reales'])
-#                         non_zero_actuals = valid_results[valid_results['ventas_reales'] != 0]
-#                         if not valid_results.empty:
-#                             mae = mean_absolute_error(valid_results['ventas_reales'], valid_results['ventas_predichas'])
-#                             mape = np.mean(np.abs((non_zero_actuals['ventas_reales'] - non_zero_actuals['ventas_predichas']) / non_zero_actuals['ventas_reales'])) * 100
-#                             rmse = np.sqrt(mean_squared_error(valid_results['ventas_reales'], valid_results['ventas_predichas']))
-
-#                             # st.write(f"Actual total sales for Customer {customer_code}: {valid_results['ventas_reales'].sum():.2f}")
-#                             # st.write(f"MAE: {mae:.2f}€")
-#                             # st.write(f"MAPE: {mape:.2f}%")
-#                             # st.write(f"RMSE: {rmse:.2f}")
-
-#                     #     # Analysis of results
-#                     #     threshold_good = 100  # You may want to adjust this threshold
-#                     #     if mae < threshold_good:
-#                     #         st.success(f"Customer {customer_code} is performing well based on the predictions.")
-#                     #     else:
-#                     #         st.warning(f"Customer {customer_code} is not performing well based on the predictions.")
-#                     # else:
-#                     #     st.warning(f"No actual sales data found for customer {customer_code} in df_agg_2024.")
-
-#                     # st.write("### Debug Information for Radar Chart:")
-#                     # st.write(f"Shape of customer_data: {customer_data.shape}")
-#                     # st.write(f"Shape of euros_proveedor: {euros_proveedor.shape}")
-
-#                     # Get percentage of units sold for each manufacturer
-#                     customer_df = df[df["CLIENTE"] == str(customer_code)]  # Get the customer data
-#                     all_manufacturers = customer_df.iloc[:, 1:].T  # Exclude CLIENTE column (manufacturers are in columns)
-#                     all_manufacturers.index = all_manufacturers.index.astype(str)
-
-#                     # Get total sales for each manufacturer from euros_proveedor
-#                     customer_euros = euros_proveedor[euros_proveedor["CLIENTE"] == str(customer_code)]
-#                     sales_data = customer_euros.iloc[:, 1:].T  # Exclude CLIENTE column
-#                     sales_data.index = sales_data.index.astype(str)
-
-#                     # Remove the 'CLIENTE' row from sales_data to avoid issues with mixed types
-#                     sales_data_filtered = sales_data.drop(index='CLIENTE', errors='ignore')
-
-#                     # Ensure all values are numeric
-#                     sales_data_filtered = sales_data_filtered.apply(pd.to_numeric, errors='coerce')
-#                     all_manufacturers = all_manufacturers.apply(pd.to_numeric, errors='coerce')
-
-#                     # Sort manufacturers by percentage of units and get top 10
-#                     top_units = all_manufacturers.sort_values(by=all_manufacturers.columns[0], ascending=False).head(10)
-
-#                     # Sort manufacturers by total sales and get top 10
-#                     top_sales = sales_data_filtered.sort_values(by=sales_data_filtered.columns[0], ascending=False).head(10)
-
-#                     # Combine top manufacturers from both lists and get up to 20 unique manufacturers
-#                     combined_top = pd.concat([top_units, top_sales]).index.unique()[:20]
-
-#                     # Filter out manufacturers that are not present in both datasets
-#                     combined_top = [m for m in combined_top if m in all_manufacturers.index and m in sales_data_filtered.index]
-
-#                     # st.write(f"Number of combined top manufacturers: {len(combined_top)}")
-
-#                     if combined_top:
-#                         # Create a DataFrame with combined data for these top manufacturers
-#                         combined_data = pd.DataFrame({
-#                             'units': all_manufacturers.loc[combined_top, all_manufacturers.columns[0]],
-#                             'sales': sales_data_filtered.loc[combined_top, sales_data_filtered.columns[0]]
-#                         }).fillna(0)
-
-#                         # Sort by units, then by sales
-#                         combined_data_sorted = combined_data.sort_values(by=['units', 'sales'], ascending=False)
-
-#                         # Filter out manufacturers with 0 units
-#                         non_zero_manufacturers = combined_data_sorted[combined_data_sorted['units'] > 0]
-
-#                         # If we have less than 3 non-zero manufacturers, add some zero-value ones
-#                         if len(non_zero_manufacturers) < 3:
-#                             zero_manufacturers = combined_data_sorted[combined_data_sorted['units'] == 0].head(3 - len(non_zero_manufacturers))
-#                             manufacturers_to_show = pd.concat([non_zero_manufacturers, zero_manufacturers])
-#                         else:
-#                             manufacturers_to_show = non_zero_manufacturers
-
-#                         values = manufacturers_to_show['units'].tolist()
-#                         amounts = manufacturers_to_show['sales'].tolist()
-#                         manufacturers = [get_supplier_name(m) for m in manufacturers_to_show.index]
-
-#                         # st.write(f"### Results for top {len(manufacturers)} manufacturers:")
-#                         # for manufacturer, value, amount in zip(manufacturers, values, amounts):
-#                         #     (f"{manufacturer} = {value:.2f}% of units, €{amount:.2f} total sales")
-
-#                         if manufacturers:  # Only create the chart if we have data
-#                             fig = radar_chart(manufacturers, values, amounts, f'Radar Chart for Top {len(manufacturers)} Manufacturers of Customer {customer_code}')
-#                             st.pyplot(fig)
-#                         else:
-#                             st.warning("No data available to create the radar chart.")
-
-#                     else:
-#                         st.warning("No combined top manufacturers found.")
-
-#                     # Ensure codigo_cliente in ventas_clientes is a string
-#                     ventas_clientes['codigo_cliente'] = ventas_clientes['codigo_cliente'].astype(str).str.strip()
-
-#                     # Ensure customer_code is a string and strip any spaces
-#                     customer_code = str(customer_code).strip()
-
-#                     # if customer_code in ventas_clientes['codigo_cliente'].unique():
-#                     #     (f"Customer {customer_code} found in ventas_clientes")  
-#                     # else:
-#                     #     (f"Customer {customer_code} not found in ventas_clientes")
-
-#                     # Customer sales 2021-2024 (if data exists)
-#                     sales_columns = ['VENTA_2021', 'VENTA_2022', 'VENTA_2023']
-#                     if all(col in ventas_clientes.columns for col in sales_columns):
-#                         customer_sales_data = ventas_clientes[ventas_clientes['codigo_cliente'] == customer_code]
-
-#                         if not customer_sales_data.empty:
-#                             customer_sales = customer_sales_data[sales_columns].values[0]
-#                             years = ['2021', '2022', '2023']
-
-#                             # Add the 2024 actual and predicted data
-#                             if 'ventas_predichas' in results.columns and 'ventas_reales' in results.columns:
-#                                 # Get the actual and predicted sales for 2024
-#                                 actual_sales_2024 = results[results['fecha_mes'].str.startswith('2024')]['ventas_reales'].sum()
-#                                 predicted_sales_2024 = results[results['fecha_mes'].str.startswith('2024')]['ventas_predichas'].sum()
-
-#                                 # Estimate full-year predicted sales (assuming predictions available until September)
-#                                 months_available = 9  # Data available until September
-#                                 actual_sales_2024_annual = (actual_sales_2024 / months_available) * 12
-
-#                                 # Add 2024 actual and predicted sales
-#                                 sales_values = list(customer_sales) + [actual_sales_2024_annual]  # Actual sales
-#                                 predicted_values = list(customer_sales) + [predicted_sales_2024]  # Predicted sales
-
-#                                 # Add 2024 to the years list
-#                                 years.append('2024')
-
-#                                 fig_sales_bar = go.Figure()
-#                                 # Add trace for historical sales (2021-2023)
-#                                 fig_sales_bar.add_trace(go.Bar(
-#                                     x=years[:3],  # 2021, 2022, 2023
-#                                     y=sales_values[:3],
-#                                     name="Historical Sales",
-#                                     marker_color='blue'
-#                                 ))
-
-#                                 # Add trace for 2024 actual sales
-#                                 fig_sales_bar.add_trace(go.Bar(
-#                                     x=[years[3]],  # 2024
-#                                     y=[sales_values[3]],
-#                                     name="2024 Actual Sales (Annualized)",
-#                                     marker_color='green'
-#                                 ))
-
-#                                 # Add trace for 2024 predicted sales
-#                                 fig_sales_bar.add_trace(go.Bar(
-#                                     x=[years[3]],  # 2024
-#                                     y=[predicted_values[3]],
-#                                     name="2024 Predicted Sales",
-#                                     marker_color='orange'
-#                                 ))
-
-#                                 # Update layout
-#                                 fig_sales_bar.update_layout(
-#                                     title=f"Sales Over the Years for Customer {customer_code}",
-#                                     xaxis_title="Year",
-#                                     yaxis_title="Sales (€)",
-#                                     barmode='group',
-#                                     height=600,
-#                                     legend_title_text="Sales Type",
-#                                     hovermode="x unified"
-#                                 )
-
-#                                 # Show the interactive bar chart in Streamlit
-#                                 st.plotly_chart(fig_sales_bar, use_container_width=True)
-
-#                             else:
-#                                 st.warning(f"No predicted or actual data found for customer {customer_code} for 2024.")
-
-#                         else:
-#                             st.warning(f"No historical sales data found for customer {customer_code}")
-
-#                     else:
-#                         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")
+    # Estilo principal de la página
+    st.markdown(
+        "<h1 style='text-align: center; color: #2E86C1;'>Articles Recommendations</h1>", 
+        unsafe_allow_html=True
+    )
     st.markdown("""
-        Get tailored recommendations for your customers based on their basket.
-    """)
-    st.write("Select items and assign quantities for the basket:")
+        <p style='text-align: center; color: #5D6D7E;'>Get tailored recommendations for your customers based on their basket.</p>
+    """, unsafe_allow_html=True)
+    st.write("### Select items and assign quantities for the basket:")
+    # Añadir separador para mejorar la segmentación visual
+    st.divider()
     # Mostrar lista de artículos disponibles
-    available_articles = productos['ARTICULO'].unique()
+    available_articles = productos['DESCRIPCION'].unique()
     selected_articles = st.multiselect("Select Articles", available_articles)
-    # Crear inputs para ingresar las cantidades de cada artículo seleccionado
+    # Mostrar cantidades en la misma fila
     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"
+    if selected_articles:
+        st.write("### Selected Items and Quantities:")
+        for article in selected_articles:
+            col1, col2 = st.columns([3, 1])  # Configurar las proporciones de las columnas
+            with col1:
+                st.write(article)  # Mostrar el nombre del artículo
+            with col2:
+                quantities[article] = st.number_input(f"Qty for {article}", min_value=0, step=1, key=article)  # Pedir cantidad
+    # Añadir un botón estilizado "Calcular" con icono
+    if st.button("🛒 Calculate Recommendations"):
         # Crear una lista de artículos basada en la selección de códigos y cantidades
         new_basket = []
         for article in selected_articles:
@@ -968,155 +717,43 @@ elif page == "Articles Recommendations":
         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)
+                st.success("### Recommendations based on the current basket:")
+                st.dataframe(recommendations_df, height=300, width=800)  # Ajustar el tamaño del DataFrame
             else:
-                st.warning("No recommendations found for the provided basket.")
+                st.warning("⚠️ No recommendations found for the provided basket.")
         else:
-            st.warning("Please select at least one article and set its quantity.")
+            st.warning("⚠️ Please select at least one article and set its quantity.")
 
 # # Customer Recommendations Page
 # elif page == "Articles Recommendations":
 #     st.title("Articles Recommendations")
-
 #     st.markdown("""
 #         Get tailored recommendations for your customers based on their basket.
 #     """)
-
-#     # Campo input para cliente
-#     partial_code = st.text_input("Enter part of Customer Code for Recommendations (or leave empty to see all)")
-#     if partial_code:
-#         filtered_customers = df[df['CLIENTE'].str.contains(partial_code)]
-#     else:
-#         filtered_customers = df
-#     customer_list = filtered_customers['CLIENTE'].unique()
-#     customer_code = st.selectbox("Select Customer Code for Recommendations", [""] + list(customer_list))
-
-#     # Definición de la función recomienda
-#     def recomienda(new_basket):
-#         # Calcular la matriz TF-IDF
-#         tfidf = TfidfVectorizer()
-#         tfidf_matrix = tfidf.fit_transform(cestas['Cestas'])
-
-#         # Convertir la nueva cesta en formato TF-IDF
-#         new_basket_str = ' '.join(new_basket)
-#         new_basket_tfidf = tfidf.transform([new_basket_str])
-
-#         # Comparar la nueva cesta con las anteriores
-#         similarities = cosine_similarity(new_basket_tfidf, tfidf_matrix)
-
-#         # Obtener los índices de las cestas más similares
-#         similar_indices = similarities.argsort()[0][-3:]  # Las 3 más similares
-
-#         # Crear un diccionario para contar las recomendaciones
-#         recommendations_count = {}
-#         total_similarity = 0
-
-#         # Recomendar productos de cestas similares
-#         for idx in similar_indices:
-#             sim_score = similarities[0][idx]
-#             total_similarity += sim_score
-#             products = cestas.iloc[idx]['Cestas'].split()
-
-#             for product in products:
-#                 if product.strip() not in new_basket:  # Evitar recomendar lo que ya está en la cesta
-#                     if product.strip() in recommendations_count:
-#                         recommendations_count[product.strip()] += sim_score
-#                     else:
-#                         recommendations_count[product.strip()] = sim_score
-
-#         # Calcular la probabilidad relativa de cada producto recomendado
-#         recommendations_with_prob = []
-#         if total_similarity > 0:  # Verificar que total_similarity no sea cero
-#             recommendations_with_prob = [(product, score / total_similarity) for product, score in recommendations_count.items()]
-#         else:
-#             print("No se encontraron similitudes suficientes para calcular probabilidades.")
-
-#         recommendations_with_prob.sort(key=lambda x: x[1], reverse=True)  # Ordenar por puntuación
-
-#         # Crear un nuevo DataFrame para almacenar las recomendaciones con descripciones y probabilidades
-#         recommendations_df = pd.DataFrame(columns=['ARTICULO', 'DESCRIPCION', 'PROBABILIDAD'])
-
-#         # Agregar las recomendaciones al DataFrame usando pd.concat
-#         for product, prob in recommendations_with_prob:
-#             # Buscar la descripción en el DataFrame de productos
-#             description = productos.loc[productos['ARTICULO'] == product, 'DESCRIPCION']
-#             if not description.empty:
-#                 # Crear un nuevo DataFrame temporal para la recomendación
-#                 temp_df = pd.DataFrame({
-#                     'ARTICULO': [product],
-#                     'DESCRIPCION': [description.values[0]],  # Obtener el primer valor encontrado
-#                     'PROBABILIDAD': [prob]
-#                 })
-#                 # Concatenar el DataFrame temporal al DataFrame de recomendaciones
-#                 recommendations_df = pd.concat([recommendations_df, temp_df], ignore_index=True)
-
-#         return recommendations_df
-
-#     # Comprobar si el cliente está en el CSV de fieles
-#     is_fiel = customer_code in fieles_df['Cliente'].astype(str).values
-
-#     if customer_code:
-#         if is_fiel:
-#             st.write(f"### Customer {customer_code} is a loyal customer.")
-#             option = st.selectbox("Select Recommendation Type", ["Select an option", "By Purchase History", "By Current Basket"])
-
-#             if option == "By Purchase History":
-#                 st.warning("Option not available... aún")
-#             elif option == "By Current Basket":
-#                 st.write("Select the 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]
-
-#                     if new_basket:
-#                         # Procesar la lista para recomendar
-#                         recommendations_df = recomienda(new_basket)
-
-#                         if not recommendations_df.empty:
-#                             st.write("### Recommendations based on the current basket:")
-#                             st.dataframe(recommendations_df)
-#                         else:
-#                             st.warning("No recommendations found for the provided basket.")
-#                     else:
-#                         st.warning("Please select at least one article and set its quantity.")
+#     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 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)
+#             else:
+#                 st.warning("No recommendations found for the provided basket.")
 #         else:
-#             st.write(f"### Customer {customer_code} is not a loyal customer.")
-#             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]
-
-#                 if new_basket:
-#                     # Procesar la lista para recomendar
-#                     recommendations_df = recomienda(new_basket)
-
-#                     if not recommendations_df.empty:
-#                         st.write("### Recommendations based on the current basket:")
-#                         st.dataframe(recommendations_df)
-#                     else:
-#                         st.warning("No recommendations found for the provided basket.")
-#                 else:
-#                     st.warning("Please select at least one article and set its quantity.")
-
+#             st.warning("Please select at least one article and set its quantity.")