above the fold

app.py

@@ -294,7 +294,7 @@ with st.form("choose_model"):
 
                 seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
 
-        st.success(f"All done for {option}!", icon="✅")
+        st.success(f"All done!", icon="✅")
 
         green_proba = seq_proba[0]
         red_proba = 1 - green_proba
@@ -436,8 +436,6 @@ with st.form("choose_model"):
 
         csv = convert_df(perf_daily)
             
-        tab1, tab2, tab3, tab4 = st.tabs(["🔮 Prediction", "✨ New Data", "🗄 Historical", "📊 Performance"])
-
         check = data.tail(1)
 
         data['VIX_EM'] = data['Close'] * (data['Close_VIX']/100) * (np.sqrt( 1 ) / np.sqrt(252))
@@ -489,10 +487,12 @@ with st.form("choose_model"):
             f"{len(data.query('Close <= VIX_EM_15_High & Close >= VIX_EM_15_Low')) / len(data):.1%}",
             f"{len(data.query('High > VIX_EM_15_High | Low < VIX_EM_15_Low')) / len(data):.1%}"
             ]
-        
+        st.subheader(f'🔮 for {option} on {curr_date}')
+        st.write(results.T)
+
+        tab1, tab2, tab3, tab4 = st.tabs(["🤖 Stats", "✨ New Data", "📚 Historical", "📊 Performance"])
+
         with tab1:
-            st.subheader(f'{option} on {curr_date}')
-            st.write(results)
             st.write(df_probas)
             st.text(f'VIX EM ({curr_em:.2f} / {fwd_em:.2f})')
             st.write(df_em)