simplify top of fold

app.py

@@ -419,7 +419,8 @@ with st.form("choose_model"):
             data = {
                 'Confidence':[f'{text_cond} {score:.1%}'],
                 'Success':[f'{historical_proba:.1%}'],
-                f'N{operator}{score:.1%}':[f'{num_obs} / {len(data)}']
+                f'N{operator}{score:.1%}':[f'{num_obs} / {len(data)}'],
+                'Prev / Curr':[f'{prev_close:.2f} / {curr_close:.2f}']
             })
 
         prices = pd.DataFrame(index=[
@@ -549,44 +550,36 @@ with st.form("choose_model"):
         tab1, tab2, tab3, tab4 = st.tabs(["🤖 Stats", "✨ New Data", "📚 Historical", "📊 Performance"])
 
         with tab1:
-            st.dataframe(prices.T.set_index('PrevClose', drop=True))
+            # st.dataframe(prices.T.set_index('PrevClose', drop=True))
             st.write(df_probas)
 
-            em_option = st.radio(f'🌊 VIX EM ({curr_em:.2f} / {fwd_em:.2f})',['Curr','Fwd'],horizontal=True)
-            em_use = None
-            close_use = None
-            if em_option == 'Curr':
-                em_use = curr_em
-                close_use = prev_close
-
-            elif em_option == 'Fwd':
-                em_use = fwd_em
-                close_use = curr_close
-
-            df_em = pd.DataFrame(columns=['EM','Low','High','WithinRange','Tested'])
-            df_em.index.name = f'{close_use:.2f}'
-            df_em.loc['EM 1X'] = [
-                f"{em_use:.2f} ({em_use / close_use:.1%})",
-                f"{close_use - em_use:.2f}",
-                f"{close_use + em_use:.2f}",
-                f"{len(data.query('Close <= VIX_EM_High & Close >= VIX_EM_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_High | Low < VIX_EM_Low')) / len(data):.1%}"
-                ]
-            df_em.loc['EM 1.25X'] = [
-                f"{curr_em * 1.25:.2f} ({(em_use * 1.25) / close_use:.1%})",
-                f"{close_use - (em_use * 1.25):.2f}",
-                f"{close_use + (em_use * 1.25):.2f}",
-                f"{len(data.query('Close <= VIX_EM_125_High & Close >= VIX_EM_125_Low')) / len(data):.1%}",
-                f"{len(data.query('High > VIX_EM_125_High | Low < VIX_EM_125_Low')) / len(data):.1%}"
-                ]
-            df_em.loc[f"EM 1.5X"] = [
-                f"{curr_em * 1.5:.2f} ({(em_use * 1.5) / close_use:.1%})",
-                f"{close_use - (em_use * 1.5):.2f}",
-                f"{close_use + (em_use * 1.5):.2f}",
-                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(df_em)
+            for close_use, em_use, lbl in zip([prev_close, curr_close], [curr_em, fwd_em], ['Current','Forward']):
+
+                st.write(f'🌊 VIX EM {lbl}: {em_use:.2f}')
+                df_em = pd.DataFrame(columns=['EM','Low','High','InRange','Tested'])
+                df_em.index.name = f'{close_use:.2f}'
+                df_em.loc['EM 1X'] = [
+                    f"{em_use:.2f} ({em_use / close_use:.1%})",
+                    f"{close_use - em_use:.2f}",
+                    f"{close_use + em_use:.2f}",
+                    f"{len(data.query('Close <= VIX_EM_High & Close >= VIX_EM_Low')) / len(data):.1%}",
+                    f"{len(data.query('High > VIX_EM_High | Low < VIX_EM_Low')) / len(data):.1%}"
+                    ]
+                df_em.loc['EM 1.25X'] = [
+                    f"{curr_em * 1.25:.2f} ({(em_use * 1.25) / close_use:.1%})",
+                    f"{close_use - (em_use * 1.25):.2f}",
+                    f"{close_use + (em_use * 1.25):.2f}",
+                    f"{len(data.query('Close <= VIX_EM_125_High & Close >= VIX_EM_125_Low')) / len(data):.1%}",
+                    f"{len(data.query('High > VIX_EM_125_High | Low < VIX_EM_125_Low')) / len(data):.1%}"
+                    ]
+                df_em.loc[f"EM 1.5X"] = [
+                    f"{curr_em * 1.5:.2f} ({(em_use * 1.5) / close_use:.1%})",
+                    f"{close_use - (em_use * 1.5):.2f}",
+                    f"{close_use + (em_use * 1.5):.2f}",
+                    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(df_em)
 
         with tab2:
             st.subheader('Latest Data for Pred')