make table pretty

app.py

@@ -394,17 +394,27 @@ if st.button('🤖 Run it'):
     # Calc green and red probas
     green_proba = seq_proba[0]
     red_proba = 1 - green_proba
+    do_not_play = (seq_proba[0] > 0.4) and (seq_proba[0] <= 0.6) 
     stdev = 0.01
     score = None
     num_obs = None
     cond = None
     historical_proba = None
+    text_cond = None
+    operator = None
 
-    if green_proba > red_proba:
+    if do_not_play:
+        text_cond = '⚠'
+        operator = ''
+        score = seq_proba[0]
+        
+    elif green_proba > red_proba:
         # If the day is predicted to be green, say so
+        text_cond = '🟩'
+        operator = '>='
         score = green_proba
         # How many with this score?
-        cond = (res1['Predicted'] <= (green_proba + stdev)) & (res1['Predicted'] >= (green_proba - stdev))
+        cond = (res1['Predicted'] >= green_proba)
         num_obs = len(res1.loc[cond])
         # How often green?
         historical_proba = res1.loc[cond, 'True'].mean()
@@ -413,21 +423,23 @@ if st.button('🤖 Run it'):
 
     elif green_proba <= red_proba:
         # If the day is predicted to be green, say so
+        text_cond = '🟥'
+        operator = '<='
         score = red_proba
         # How many with this score?
-        cond = (res1['Predicted'] <= (red_proba + stdev)) & (res1['Predicted'] >= (red_proba - stdev))
+        cond = (res1['Predicted'] <= red_proba)
         num_obs = len(res1.loc[cond])
         # How often green?
         historical_proba = 1 - res1.loc[cond, 'True'].mean()
         # print(cond)
 
-    text_cond = '🟩' if green_proba > red_proba else '🟥'
+    score_fmt = f'{score:.1%}'
 
     results = pd.DataFrame(index=[
         'ModelScore',
-        f'NumInRange ({score - stdev:.1%} - {score + stdev:.1%})',
         'HistoricalRate'
-    ], data = [f'{text_cond} {score:.1%}', num_obs, f'{text_cond} {historical_proba:.1%}'])
+        f'Num {operator} {"" if do_not_play else score_fmt}',
+    ], data = [f'{text_cond} {score:.1%}', f'{text_cond} {historical_proba:.1%}', num_obs])
 
     results.columns = ['Outputs']