feat: updated website

app.py

@@ -263,7 +263,7 @@ elif page == "Customer Analysis":
             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(2)
+                time.sleep(1)
                 
 
                 if not customer_match.empty:
@@ -273,7 +273,7 @@ elif page == "Customer Analysis":
                 # Load the Corresponding Model
                 model_path = f'models/modelo_cluster_{cluster}.txt'
                 gbm = lgb.Booster(model_file=model_path)
-                time.sleep(2)
+                time.sleep(1)
 
             with st.spinner("Getting the data ready..."):
                 # Load predict data for that cluster
@@ -321,9 +321,9 @@ elif page == "Customer Analysis":
                                                 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}")
+                        # 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'])
@@ -333,23 +333,23 @@ elif page == "Customer Analysis":
                             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}")
+                            # 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