feat: updated website

app.py

@@ -241,7 +241,6 @@ if page == "Summary":
                         )}
                     )
 # Customer Analysis Page
-
 elif page == "Customer Analysis":
     st.markdown("""
     <h2 style='text-align: center; font-size: 2.5rem;'>Customer Analysis</h2>
@@ -281,14 +280,12 @@ elif page == "Customer Analysis":
                 # Convert cliente_id to string
                 predict_data['cliente_id'] = predict_data['cliente_id'].astype(str)
 
-            with st.spinner("Filtering data..."):                
-
+            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..."):       
-
+            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)]
@@ -309,7 +306,7 @@ elif page == "Customer Analysis":
                     results = customer_data[['cliente_id', 'marca_id_encoded', 'fecha_mes']].copy()
                     results['ventas_predichas'] = y_pred
 
-                    # Load actual data
+                    # Load actual data from df_agg_2024
                     actual_sales = df_agg_2024[df_agg_2024['cliente_id'] == customer_code_str]
                     
                     if not actual_sales.empty:
@@ -321,7 +318,7 @@ elif page == "Customer Analysis":
                     else:
                         # If no actual sales data for 2024, fill 'ventas_reales' with 0
                         results['ventas_reales'] = 0
-                    
+
                     # Ensure any missing sales data is filled with 0
                     results['ventas_reales'].fillna(0, inplace=True)
 
@@ -409,7 +406,9 @@ elif page == "Customer Analysis":
 
                         st.plotly_chart(fig_comparison, use_container_width=True)
 
-                    # Ensure customer sales (2021-2024)
+                    # Gráfico de ventas anuales
+                    ventas_clientes['codigo_cliente'] = ventas_clientes['codigo_cliente'].astype(str).str.strip()
+
                     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]
@@ -420,47 +419,39 @@ elif page == "Customer Analysis":
 
                             # 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
-                                months_available = 9  # Data available until September
+                                months_available = 9
                                 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]
                                 predicted_values = list(customer_sales) + [predicted_sales_2024]
 
-                                # 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
+                                    x=years[:3],  
                                     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
+                                    x=[years[3]],  
                                     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
+                                    x=[years[3]],  
                                     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",
@@ -471,18 +462,11 @@ elif page == "Customer Analysis":
                                     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.")
-
 
 
 # elif page == "Customer Analysis":
@@ -925,141 +909,3 @@ elif page == "Articles Recommendations":
 #                 else:
 #                     st.warning("Please select at least one article and set its quantity.")
 
-
-# Customer Analysis Page
-# elif page == "Customer Analysis":
-#     st.title("Customer Analysis")
-#     st.markdown("Use the tools below to explore your customer data.")
-
-#     partial_code = st.text_input("Enter part of Customer Code (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", customer_list)
-
-#     if st.button("Calcular"):
-#         if customer_code:
-#             # Find Customer's Cluster
-#             customer_match = customer_clusters[customer_clusters['cliente_id'] == customer_code]
-
-#             if not customer_match.empty:
-#                 cluster = customer_match['cluster_id'].values[0]
-#                 st.write(f"Customer {customer_code} belongs to cluster {cluster}")
-
-#                 # Load the Corresponding Model
-#                 model_path = f'models/modelo_cluster_{cluster}.txt'
-#                 gbm = lgb.Booster(model_file=model_path)
-#                 st.write(f"Loaded model for cluster {cluster}")
-
-#                 # Load X_predict for that cluster
-#                 X_predict_cluster = pd.read_csv(f'predicts/X_predict_cluster_{cluster}.csv')
-
-#                 # Filter for the specific customer
-#                 X_cliente = X_predict_cluster[X_predict_cluster['cliente_id'] == customer_code]
-
-#                 if not X_cliente.empty:
-#                     # Prepare data for prediction
-#                     features_for_prediction = X_cliente.drop(columns=['cliente_id', 'fecha_mes'])
-                    
-#                     # Make Prediction for the selected customer
-#                     y_pred = gbm.predict(features_for_prediction, num_iteration=gbm.best_iteration)
-                    
-#                     # Reassemble the results
-#                     results = X_cliente[['cliente_id', 'marca_id_encoded', 'fecha_mes']].copy()
-#                     results['ventas_predichas'] = y_pred
-                    
-#                     st.write(f"Predicted total sales for Customer {customer_code}: {results['ventas_predichas'].sum():.2f}")
-
-#                     # Load actual data
-#                     df_agg_2024 = pd.read_csv('predicts/df_agg_2024.csv')
-#                     actual_sales = df_agg_2024[df_agg_2024['cliente_id'] == customer_code]
-                    
-#                     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)
-                        
-#                         # Calculate metrics only for non-null actual sales
-#                         valid_results = results.dropna(subset=['ventas_reales'])
-#                         if not valid_results.empty:
-#                             mae = mean_absolute_error(valid_results['ventas_reales'], valid_results['ventas_predichas'])
-#                             mape = np.mean(np.abs((valid_results['ventas_reales'] - valid_results['ventas_predichas']) / valid_results['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.")
-
-#                     # Show the radar chart
-#                     all_manufacturers = customer_data.iloc[:, 1:].T  # Exclude CLIENTE column
-#                     all_manufacturers.index = all_manufacturers.index.astype(str)
-
-#                     sales_data = customer_euros.iloc[:, 1:].T  # Exclude CLIENTE column
-#                     sales_data.index = sales_data.index.astype(str)
-
-#                     sales_data_filtered = sales_data.drop(index='CLIENTE', errors='ignore')
-#                     sales_data_filtered = sales_data_filtered.apply(pd.to_numeric, errors='coerce')
-
-#                     top_units = all_manufacturers.sort_values(by=all_manufacturers.columns[0], ascending=False).head(10)
-#                     top_sales = sales_data_filtered.sort_values(by=sales_data_filtered.columns[0], ascending=False).head(10)
-#                     combined_top = pd.concat([top_units, top_sales]).index.unique()[:20]
-#                     combined_top = [m for m in combined_top if m in all_manufacturers.index and m in sales_data_filtered.index]
-
-#                     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)
-
-#                     combined_data_sorted = combined_data.sort_values(by=['units', 'sales'], ascending=False)
-#                     non_zero_manufacturers = combined_data_sorted[combined_data_sorted['units'] > 0]
-
-#                     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):
-#                         st.write(f"{manufacturer} = {value:.2f}% of units, €{amount:.2f} total sales")
-
-#                     if manufacturers:
-#                         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.")
-
-#                     # Show sales over the years graph
-#                     sales_columns = ['VENTA_2021', 'VENTA_2022', 'VENTA_2023']
-#                     if all(col in ventas_clientes.columns for col in sales_columns):
-#                         years = ['2021', '2022', '2023']
-#                         customer_sales = ventas_clientes[ventas_clientes['codigo_cliente'] == customer_code][sales_columns].values[0]
-
-#                         fig_sales = px.line(x=years, y=customer_sales, markers=True, title=f'Sales Over the Years for Customer {customer_code}')
-#                         fig_sales.update_layout(xaxis_title="Year", yaxis_title="Sales")
-#                         st.plotly_chart(fig_sales)
-#                     else:
-#                         st.warning("Sales data for 2021-2023 not available.")
-#                 else:
-#                     st.warning(f"No prediction data found for customer {customer_code}.")
-#             else:
-#                 st.warning(f"No data found for customer {customer_code}. Please check the code.")
-#         else:
-#             st.warning("Please select a customer.")