Update app.py

app.py

@@ -72,16 +72,22 @@ def init_baselines():
     raw_display = pd.DataFrame(worksheet.get_all_records())
     raw_display.replace('', np.nan, inplace=True)
     prop_frame = raw_display.dropna(subset='Player')
+    
+    worksheet = sh.worksheet('Pick6_ingest')
+    raw_display = pd.DataFrame(worksheet.get_all_records())
+    raw_display.replace('', np.nan, inplace=True)
+    pick_frame = raw_display.dropna(subset='Player')
 
     prop_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy'],
                                  ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.', 'Trey Murphy III'], inplace=True)
-    
-    return game_model, player_stats, prop_frame, timestamp
+    pick_frame['Player'].replace(['Jaren Jackson', 'Nic Claxton', 'Jabari Smith', 'Lu Dort', 'Moe Wagner', 'Kyle Kuzma', 'Trey Murphy'],
+                                 ['Jaren Jackson Jr.', 'Nicolas Claxton', 'Jabari Smith Jr.', 'Luguentz Dort', 'Moritz Wagner', 'Kyle Kuzma Jr.', 'Trey Murphy III'], inplace=True)
+    return game_model, player_stats, prop_frame, pick_frame, timestamp
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
-game_model, player_stats, prop_frame, timestamp = init_baselines()
+game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
 tab1, tab2, tab3, tab4, tab5 = st.tabs(["Game Betting Model", "Player Projections", "Prop Trend Table", "Player Prop Simulations", "Stat Specific Simulations"])
@@ -90,7 +96,7 @@ with tab1:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              game_model, player_stats, prop_frame, timestamp = init_baselines()
+              game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     line_var1 = st.radio('How would you like to display odds?', options = ['Percentage', 'American'], key='line_var1')
     team_frame = game_model
@@ -115,7 +121,7 @@ with tab2:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset2'):
               st.cache_data.clear()
-              game_model, player_stats, prop_frame, timestamp = init_baselines()
+              game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     split_var1 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var1')
     if split_var1 == 'Specific Teams':
@@ -137,7 +143,7 @@ with tab3:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
-              game_model, player_stats, prop_frame, timestamp = init_baselines()
+              game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     split_var5 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var5')
     if split_var5 == 'Specific Teams':
@@ -161,7 +167,7 @@ with tab4:
     st.info(t_stamp)
     if st.button("Reset Data", key='reset4'):
               st.cache_data.clear()
-              game_model, player_stats, prop_frame, timestamp = init_baselines()
+              game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])
     
@@ -306,7 +312,7 @@ with tab5:
     st.info('The Over and Under percentages are a composite percentage based on simulations, historical performance, and implied probabilities, and may be different than you would expect based purely on the median projection. Likewise, the Edge of a bet is not the only indicator of if you should make the bet or not as the suggestion is using a base acceptable threshold to determine how much edge you should have for each stat category.')
     if st.button("Reset Data/Load Data", key='reset5'):
               st.cache_data.clear()
-              game_model, player_stats, prop_frame, timestamp = init_baselines()
+              game_model, player_stats, prop_frame, pick_frame, timestamp = init_baselines()
               t_stamp = f"Last Update: " + str(timestamp) + f" CST"
     col1, col2 = st.columns([1, 5])
     
@@ -317,6 +323,7 @@ with tab5:
         export_container = st.empty()
     
     with col1:
+        game_select_var = st.selectbox('Select prop source', options = ['Props', 'Pick6'])
         prop_type_var = st.selectbox('Select prop category', options = ['All Props', 'points', 'rebounds', 'assists', 'threes', 'PRA', 'points+rebounds',
                                                                         'points+assists', 'rebounds+assists'])
         if prop_type_var == 'All Props':
@@ -328,7 +335,10 @@ with tab5:
                         if prop_type_var == 'All Props':
                             for prop in all_sim_vars:
                                 
-                                prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                if game_select_var == 'Props':
+                                    prop_df = prop_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
+                                if game_select_var == 'Pick6':
+                                    prop_df = pick_frame[['Player', 'over_prop', 'over_line', 'under_line', 'prop_type']]
                                 prop_df = prop_df.loc[prop_df['prop_type'] == prop]
                                 prop_df = prop_df[['Player', 'over_prop', 'over_line', 'under_line']]
                                 prop_df.rename(columns={"over_prop": "Prop"}, inplace = True)