Add Bet Sheet functionality to streamlit_app.py, including data retrieval, filtering options, and export capabilities for improved user experience.

src/streamlit_app.py

@@ -9,6 +9,7 @@ st.set_page_config(layout="wide")
 from database import props_db, dfs_db
 
 game_format = {'Win%': '{:.2%}', 'Vegas': '{:.2%}', 'Win% Diff': '{:.2%}'}
+bet_format = {'Edge': '{:.2%}'}
 american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead Percentage': '{:.2%}'}
 
 st.markdown("""
@@ -81,6 +82,20 @@ def init_baselines():
                                'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_ID', 'site']]
     load_display = raw_display[raw_display['Position'] != 'K']
     timestamp = load_display['timestamp'][0]
+
+    collection = dfs_db["Bet_Sheet"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display.replace('', np.nan, inplace=True)
+    raw_display = raw_display[['Name', 'PropType', 'No Vig', 'Projection', 'Edge']]
+    raw_display = raw_display.rename(columns={"Name": "Player", "PropType": "prop_type"})
+    raw_display = raw_display.dropna(subset='Player')
+    raw_display = raw_display.drop_duplicates(subset=['Player', 'prop_type'], keep='first')
+    raw_display = raw_display.sort_values(by='Edge', ascending=False)
+    raw_display = raw_display.reset_index(drop=True)
+    raw_display['O/U'] = np.where(raw_display['No Vig'] > raw_display['Projection'], 'Under', 'Over')
+    bet_sheet = raw_display[['Player', 'prop_type', 'O/U', 'No Vig', 'Projection', 'Edge']]
     
     collection = dfs_db["Prop_Trends"] 
     cursor = collection.find()
@@ -104,7 +119,7 @@ def init_baselines():
     market_props['under_prop'] = market_props['Projection']
     market_props['under_line'] = market_props['under_pay'].apply(lambda x: (x - 1) * 100 if x >= 2.0 else -100 / (x - 1))
 
-    return game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props
+    return game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet
 
 def calculate_no_vig(row):
     def implied_probability(american_odds):
@@ -147,19 +162,37 @@ sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Book', 'Prop Type', 'Pro
 
 selected_tab = st.segmented_control(
     "Select Tab",
-    options=["Game Betting Model", 'Prop Market', "QB Projections", "RB/WR/TE Projections", "Player Prop Trends", "Player Prop Simulations", "Stat Specific Simulations"],
+    options=["Bet Sheet", "Game Betting Model", 'Prop Market', "QB Projections", "RB/WR/TE Projections", "Player Prop Trends", "Player Prop Simulations", "Stat Specific Simulations"],
     selection_mode='single',
-    default='Game Betting Model',
+    default='Bet Sheet',
     width='stretch',
     label_visibility='collapsed',
     key='tab_selector'
 )
 
+if selected_tab == 'Bet Sheet':
+    with st.expander("Info and Filters"):
+        st.info(t_stamp)
+        if st.button("Reset Data", key='reset_bet_sheet'):
+                st.cache_data.clear()
+                game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
+                t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+        prop_selection = st.multiselect('Select prop type to view', default = bet_sheet['prop_type'].unique(), options = bet_sheet['prop_type'].unique(), key='prop_selection')
+    bet_sheet = bet_sheet[bet_sheet['prop_type'].isin(prop_selection)]
+    st.dataframe(bet_sheet.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(bet_format, precision=2), use_container_width = True)
+    st.download_button(
+        label="Export Bet Sheet",
+        data=convert_df_to_csv(bet_sheet),
+        file_name='NFL_bet_sheet_export.csv',
+        mime='text/csv',
+        key='bet_sheet_export',
+    )
+
 if selected_tab == 'Game Betting Model':
     st.info(t_stamp)
     if st.button("Reset Data", key='reset1'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
               qb_stats = overall_stats[overall_stats['Position'] == 'QB']
               qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
               non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
@@ -192,7 +225,7 @@ if selected_tab == 'Prop Market':
     st.info(t_stamp)
     if st.button("Reset Data", key='reset4'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
               qb_stats = overall_stats[overall_stats['Position'] == 'QB']
               qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
               non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
@@ -232,7 +265,7 @@ if selected_tab == 'QB Projections':
     st.info(t_stamp)
     if st.button("Reset Data", key='reset2'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
               qb_stats = overall_stats[overall_stats['Position'] == 'QB']
               qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
               non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
@@ -260,7 +293,7 @@ if selected_tab == 'RB/WR/TE Projections':
     st.info(t_stamp)
     if st.button("Reset Data", key='reset3'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
               qb_stats = overall_stats[overall_stats['Position'] == 'QB']
               qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
               non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
@@ -288,7 +321,7 @@ if selected_tab == 'Player Prop Trends':
     st.info(t_stamp)
     if st.button("Reset Data", key='reset5'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
               qb_stats = overall_stats[overall_stats['Position'] == 'QB']
               qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
               non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']
@@ -319,7 +352,7 @@ if selected_tab == 'Player Prop Simulations':
     st.info(t_stamp)
     if st.button("Reset Data", key='reset6'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props, bet_sheet = init_baselines()
               qb_stats = overall_stats[overall_stats['Position'] == 'QB']
               qb_stats = qb_stats.drop_duplicates(subset=['Player', 'Position'])
               non_qb_stats = overall_stats[overall_stats['Position'] != 'QB']