added market table kind of

app.py

@@ -5,6 +5,7 @@ import pandas as pd
 import gspread
 import plotly.express as px
 import scipy.stats as stats
+from pymongo import MongoClient
 st.set_page_config(layout="wide")
 
 @st.cache_resource
@@ -39,12 +40,16 @@ def init_conn():
      
         NFL_Data = st.secrets['NFL_Data']
 
+        uri = st.secrets['mongo_uri']
+        client = MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=100000)
+        db = client["Props_DB"]
+
         gc = gspread.service_account_from_dict(credentials)
         gc2 = gspread.service_account_from_dict(credentials2)
 
-        return gc, gc2, NFL_Data
+        return gc, gc2, NFL_Data, db
     
-gcservice_account, gcservice_account2, NFL_Data = init_conn()
+gcservice_account, gcservice_account2, NFL_Data, db = init_conn()
 
 game_format = {'Win%': '{:.2%}', 'Vegas': '{:.2%}', 'Win% Diff': '{:.2%}'}
 american_format = {'First Inning Lead Percentage': '{:.2%}', 'Fifth Inning Lead Percentage': '{:.2%}'}
@@ -88,9 +93,15 @@ def init_baselines():
     raw_display.replace('', np.nan, inplace=True)
     pick_frame = raw_display.dropna(subset='Player')
 
-    return game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame
+    collection = db["NFL_Props"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    market_props = raw_display[['Name', 'Position', 'Projection', 'PropType', 'OddsType']]
+
+    return game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props
 
-game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = init_baselines()
+game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = 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']
@@ -107,7 +118,7 @@ all_sim_vars = ['NFL_GAME_PLAYER_PASSING_YARDS', 'NFL_GAME_PLAYER_RUSHING_YARDS'
 pick6_sim_vars = ['Rush + Rec Yards', 'Rush + Rec TDs', 'Passing Yards', 'Passing Attempts', 'Passing TDs', 'Completions', 'Rushing Yards', 'Receptions', 'Receiving Yards']
 sim_all_hold = pd.DataFrame(columns=['Player', 'Team', 'Book', 'Prop Type', 'Prop', 'Mean_Outcome', 'Imp Over', 'Over%', 'Imp Under', 'Under%', 'Bet?', 'Edge'])
 
-tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", "Player Prop Trends", "Player Prop Simulations", "Stat Specific Simulations"])
+tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(["Game Betting Model", "QB Projections", "RB/WR/TE Projections", 'Market Table', "Player Prop Trends", "Player Prop Simulations", "Stat Specific Simulations"])
 
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
@@ -116,7 +127,7 @@ with tab1:
     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 = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = 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']
@@ -149,7 +160,7 @@ with tab2:
     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 = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = 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']
@@ -177,7 +188,7 @@ with tab3:
     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 = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = 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']
@@ -200,12 +211,34 @@ with tab3:
         mime='text/csv',
         key='NFL_nonqb_stats_export',
     )
-    
+
 with tab4:
     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 = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = 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']
+              non_qb_stats = non_qb_stats.drop_duplicates(subset=['Player', 'Position'])
+              team_dict = dict(zip(prop_frame['Player'], prop_frame['Team']))
+              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
+    market_type = st.selectbox('Select type of prop are you wanting to view', options = prop_table_options, key = 'market_type_key')
+    disp_market = market_props.copy()
+    disp_market = disp_market[disp_market['PropType'].isin(market_type)]
+    st.dataframe(disp_market.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(prop_format, precision=2), height = 1000, use_container_width = True)
+    st.download_button(
+        label="Export Market Props",
+        data=convert_df_to_csv(disp_market),
+        file_name='NFL_market_props_export.csv',
+        mime='text/csv',
+    )
+
+with tab5:
+    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()
               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']
@@ -232,11 +265,11 @@ with tab4:
         mime='text/csv',
     )
 
-with tab5:
+with tab6:
     st.info(t_stamp)
-    if st.button("Reset Data", key='reset5'):
+    if st.button("Reset Data", key='reset6'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = 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']
@@ -382,12 +415,12 @@ with tab5:
                             plot_hold_container = st.empty()
                             st.plotly_chart(fig, use_container_width=True)
 
-with tab6:
+with tab7:
     st.info(t_stamp)
     st.info('The Over and Under percentages are a compositve 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='reset6'):
+    if st.button("Reset Data/Load Data", key='reset7'):
               st.cache_data.clear()
-              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame = init_baselines()
+              game_model, overall_stats, timestamp, prop_frame, prop_trends, pick_frame, market_props = 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']