bugfix: added import lgbm

app.py

@@ -139,10 +139,33 @@ 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)
 
                 if not X_cliente.empty:
                     # Make Prediction for the selected customer