feat: updated website

app.py

@@ -267,13 +267,11 @@ elif page == "Customer Analysis":
 
                 if not customer_match.empty:
                     cluster = customer_match['cluster_id'].values[0]
-                    st.success(f"Customer {customer_code} belongs to cluster {cluster}")
                 
             with st.spinner(f"Loading customer model for cluster {cluster}..."):
                 # Load the Corresponding Model
                 model_path = f'models/modelo_cluster_{cluster}.txt'
                 gbm = lgb.Booster(model_file=model_path)
-                st.success(f"Loaded model for cluster {cluster}")
 
             with st.spinner("Getting the data ready..."):
                 # Load predict data for that cluster
@@ -282,25 +280,19 @@ elif page == "Customer Analysis":
                 # Convert cliente_id to string
                 predict_data['cliente_id'] = predict_data['cliente_id'].astype(str)
 
-            with st.spinner("Filtering for your customer..."):                
+            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]
-                
-                # Add debug statements
-                st.write(f"Unique customer IDs in predict data: {predict_data['cliente_id'].unique()}")
-                st.write(f"Customer code we're looking for: {customer_code_str}")
 
-                st.write("### Customer Data:")
-                st.write(customer_data.head())
-                st.write(f"Shape: {customer_data.shape}")
+            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]
 
@@ -309,34 +301,15 @@ elif page == "Customer Analysis":
                     for feature in categorical_features:
                         X_predict[feature] = X_predict[feature].astype('category')
                     
-                    st.write("### Features for Prediction:")
-                    st.write(X_predict.head())
-                    st.write(f"Shape: {X_predict.shape}")
-                    st.write("Data types:")
-                    st.write(X_predict.dtypes)
-                    
                     # Make Prediction for the selected customer
                     y_pred = gbm.predict(X_predict, num_iteration=gbm.best_iteration)
-                    st.write("### Prediction Results:")
-                    st.write(f"Type of y_pred: {type(y_pred)}")
-                    st.write(f"Shape of y_pred: {y_pred.shape}")
-                    st.write("First few predictions:")
-                    st.write(y_pred[:5])
-                    
+
                     # Reassemble the results
                     results = customer_data[['cliente_id', 'marca_id_encoded', 'fecha_mes']].copy()
                     results['ventas_predichas'] = y_pred
-                    st.write("### Results DataFrame:")
-                    st.write(results.head())
-                    st.write(f"Shape: {results.shape}")
-                    
-                    st.write(f"Predicted total sales for Customer {customer_code}: {results['ventas_predichas'].sum():.2f}")
 
                     # Load actual data
                     actual_sales = df_agg_2024[df_agg_2024['cliente_id'] == customer_code_str]
-                    st.write("### Actual Sales DataFrame:")
-                    st.write(actual_sales.head())
-                    st.write(f"Shape: {actual_sales.shape}")
                     
                     if not actual_sales.empty:
                         results = results.merge(actual_sales[['cliente_id', 'marca_id_encoded', 'fecha_mes', 'precio_total']],