bugfix: added import lgbm

app.py

@@ -139,37 +139,16 @@ elif page == "Customer Analysis":
                 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 and extract customer-specific data
                 X_predict_cluster = pd.read_csv(f'predicts/X_predict_cluster_{cluster}.csv')
 
-                # Debugging information
-                st.write(f"Shape of X_predict_cluster: {X_predict_cluster.shape}")
-                st.write("First few rows of X_predict_cluster:")
-                st.write(X_predict_cluster.head())
-                st.write("Unique cliente_id values in X_predict_cluster:")
-                st.write(X_predict_cluster['cliente_id'].unique())
-
-                st.write(f"Type of customer_code: {type(customer_code)}")
-                st.write(f"Value of customer_code: {customer_code}")
-
-                X_cliente = X_predict_cluster[X_predict_cluster['cliente_id'] == customer_code]
-                st.write(f"Shape of X_cliente after filtering: {X_cliente.shape}")
-
-                # Additional checks if X_cliente is empty
-                if X_cliente.empty:
-                    X_cliente_str = X_predict_cluster[X_predict_cluster['cliente_id'].astype(str) == str(customer_code)]
-                    X_cliente_int = X_predict_cluster[X_predict_cluster['cliente_id'].astype(int) == int(customer_code)]
-                    st.write(f"Shape of X_cliente_str: {X_cliente_str.shape}")
-                    st.write(f"Shape of X_cliente_int: {X_cliente_int.shape}")
-
-                st.write("Data types in X_predict_cluster:")
-                st.write(X_predict_cluster.dtypes)
+                # Filter for the specific customer and drop the 'cliente_id' column
+                X_cliente = X_predict_cluster[X_predict_cluster['cliente_id'] == customer_code].drop(columns=['cliente_id'])
 
                 if not X_cliente.empty:
                     # Make Prediction for the selected customer
-                    y_pred = gbm.predict(X_cliente.drop(columns=['cliente_id']), num_iteration=gbm.best_iteration)
+                    y_pred = gbm.predict(X_cliente, num_iteration=gbm.best_iteration)
                     st.write(f"Predicted sales for Customer {customer_code}: {y_pred.sum():.2f}")
 
                     # Merge with actual data from df_agg_2024
@@ -203,7 +182,7 @@ elif page == "Customer Analysis":
                     else:
                         st.warning(f"No actual sales data found for customer {customer_code} in df_agg_2024.")
 
-                    # **Show the radar chart**
+                    # Show the radar chart
                     all_manufacturers = customer_data.iloc[:, 1:].T  # Exclude CLIENTE column
                     all_manufacturers.index = all_manufacturers.index.astype(str)
 
@@ -246,7 +225,7 @@ elif page == "Customer Analysis":
                     else:
                         st.warning("No data available to create the radar chart.")
 
-                    # **Show sales over the years graph**
+                    # 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']
@@ -264,7 +243,6 @@ elif page == "Customer Analysis":
         else:
             st.warning("Please select a customer.")
 
-
 # Customer Recommendations Page
 elif page == "Articles Recommendations":
     st.title("Articles Recommendations")