smaller 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'🔮 for {option} on {curr_date} 👇🏽', icon="✅")
+        st.info(f'🔮 for {option} on {curr_date} 👇🏽', icon="✅")
 
         green_proba = seq_proba[0]
         red_proba = 1 - green_proba
@@ -345,11 +345,19 @@ with st.form("choose_model"):
         prev_close = data.loc[final_row,'Close']
         curr_close = data['Close'].iloc[-1]
 
-        confidence, success, nn = st.columns(3)
+        # confidence, success, nn = st.columns(3)
 
-        confidence.metric('Confidence',value=f'{text_cond} {score:.1%}')
-        success.metric('SuccessRate',value=f'{historical_proba:.1%}')
-        nn.metric(f'N{operator}{"" if do_not_play else score_fmt}',value=num_obs)
+        # confidence.metric('Confidence',value=f'{text_cond} {score:.1%}')
+        # success.metric('SuccessRate',value=f'{historical_proba:.1%}')
+        # nn.metric(f'N{operator}{"" if do_not_play else score_fmt}',value=num_obs)
+
+        top_of_fold = pd.DataFrame(
+            index=[''], 
+            data = {
+                'Confidence':[f'{text_cond} {score:.1%}'],
+                'Success':[f'{historical_proba:.1%}'],
+                f'N{operator}{score:.1%}':[num_obs]
+            })
 
         results = pd.DataFrame(index=[
             'PrevClose',
@@ -492,6 +500,8 @@ 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.write(top_of_fold)
 
         tab1, tab2, tab3, tab4 = st.tabs(["🤖 Stats", "✨ New Data", "📚 Historical", "📊 Performance"])