feat: updated website

app.py

@@ -1,4 +1,5 @@
 import streamlit as st
+import time
 import pandas as pd
 import plotly.express as px
 import matplotlib.pyplot as plt
@@ -259,34 +260,29 @@ elif page == "Customer Analysis":
 
     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}")
+            with st.spinner("We are identifying the customer's cluster..."):
+                # 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.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.write(f"Loaded model for cluster {cluster}")
-
-                # Inspect the model
-                st.write("### Model Information:")
-                st.write(f"Number of trees: {gbm.num_trees()}")
-                st.write(f"Number of features: {gbm.num_feature()}")
-                st.write("Feature names:")
-                st.write(gbm.feature_name())
+                st.success(f"Loaded model for cluster {cluster}")
 
+            with st.spinner("Getting the data ready..."):
                 # Load predict data for that cluster
                 predict_data = pd.read_csv(f'predicts/predict_cluster_{cluster}.csv')
                 
                 # Convert cliente_id to string
                 predict_data['cliente_id'] = predict_data['cliente_id'].astype(str)
-                
-                st.write("### Predict Data DataFrame:")
-                st.write(predict_data.head())
-                st.write(f"Shape: {predict_data.shape}")
+
+            with st.spinner("Filtering for your customer..."):                
 
                 # Filter for the specific customer
                 customer_code_str = str(customer_code)