remove refresh culprit and add em qol

app.py

@@ -8,6 +8,11 @@ from datetime import datetime, time, timedelta
 import pytz
 import holidays
 
+st.set_page_config(
+    page_title="Gameday $SPX",
+    page_icon="🎮"
+)
+
 # Function to convert UTC to PST
 def convert_to_pst(utc_time):
     utc_timezone = pytz.timezone('UTC')
@@ -38,13 +43,13 @@ def convert_to_datetime(time_str, pst_now):
 
 # Function to determine the emoji for each time
 @st.cache_data
-def get_time_emojis(times, pst_now):
+def get_time_emojis(_times, _pst_now):
     pst_now_time = pst_now.time()
     us_holidays = get_us_holidays()
     emojis = []
     
-    for t in times:
-        time_obj = convert_to_datetime(t, pst_now)
+    for t in _times:
+        time_obj = convert_to_datetime(t, _pst_now)
         
         # Check if the time is less than or equal to the current time in PST
         if time_obj.time() <= pst_now_time:
@@ -64,10 +69,6 @@ def get_time_emojis(times, pst_now):
 times_list = ['06:30', '07:00', '07:30', '08:00']
 emojis_list = get_time_emojis(times_list, pst_now)
 
-st.set_page_config(
-    page_title="Gameday $SPX",
-    page_icon="🎮"
-)
 
 st.title('🎮 Gameday Model for $SPX')
 st.markdown('**PLEASE NOTE:** Model should be run at or after market open. Documentation on the model and its features [can be found here.](https://huggingface.co/spaces/boomsss/gamedayspx/blob/main/README.md)')
@@ -81,7 +82,7 @@ with st.form("choose_model"):
         
     with col1:
         option = st.select_slider(
-            f"""Slide the scale based on PST, then run [{pst_now.strftime('%H:%M')}]""",
+            f"""Slide the scale based on PST, then run.""",
             times_list,
             format_func=lambda x: f"{emojis_list[times_list.index(x)]} {x}"
         )
@@ -540,28 +541,7 @@ with st.form("choose_model"):
         data['VIX_EM_125_High'] = data['VIX_EM_125_High'].shift(1)
         data['VIX_EM_125_Low'] = data['VIX_EM_125_Low'].shift(1)
 
-        df_em = pd.DataFrame(columns=['EM','Low','High','WithinRange','Tested'])
-        df_em.loc['EM 1X'] = [
-            data['VIX_EM'].iloc[-1].round(2),
-            data['VIX_EM_Low'].iloc[-1].round(2), 
-            data['VIX_EM_High'].iloc[-1].round(2), 
-            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'] = [
-            data['VIX_EM_125'].iloc[-1].round(2),
-            data['VIX_EM_125_Low'].iloc[-1].round(2), 
-            data['VIX_EM_125_High'].iloc[-1].round(2), 
-            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"] = [
-            data['VIX_EM_15'].iloc[-1].round(2),
-            data['VIX_EM_15_Low'].iloc[-1].round(2), 
-            data['VIX_EM_15_High'].iloc[-1].round(2), 
-            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%}"
-            ]
+        
         
         # Cache all DFs
         all_dfs = []
@@ -573,8 +553,43 @@ with st.form("choose_model"):
         with tab1:
             st.dataframe(prices.T.set_index('PrevClose', drop=True))
             st.write(df_probas)
-            st.text(f'VIX EM ({curr_em:.2f} / {fwd_em:.2f})')
+
+            em_option = st.radio(f'🌊 VIX EM',['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)
+
         with tab2:
             st.subheader('Latest Data for Pred')
             st.write(new_pred)