feat: updated website

app.py

@@ -273,7 +273,6 @@ 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(1)
 
             with st.spinner("Getting the data ready..."):
                 # Load predict data for that cluster
@@ -281,14 +280,12 @@ elif page == "Customer Analysis":
                 
                 # Convert cliente_id to string
                 predict_data['cliente_id'] = predict_data['cliente_id'].astype(str)
-                time.sleep(1)
 
             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]
-                time.sleep(1)
 
             with st.spinner("Generating sales predictions..."):       
 
@@ -408,7 +405,7 @@ elif page == "Customer Analysis":
 
                         # st.write(f"### Results for top {len(manufacturers)} manufacturers:")
                         for manufacturer, value, amount in zip(manufacturers, values, amounts):
-                            st.write(f"{manufacturer} = {value:.2f}% of units, €{amount:.2f} total sales")
+                            (f"{manufacturer} = {value:.2f}% of units, €{amount:.2f} total sales")
 
                         if manufacturers:  # Only create the chart if we have data
                             fig = radar_chart(manufacturers, values, amounts, f'Radar Chart for Top {len(manufacturers)} Manufacturers of Customer {customer_code}')