Refactor app.py: Remove Google Sheets integration, simplify connection initialization, and optimize caching. Adjusted data frame initialization and improved contest simulation logic. Updated cache timeouts and enhanced player ID handling for DraftKings and FanDuel. Cleaned up unused variables and streamlined data export functionality.

app.py

@@ -2,59 +2,26 @@ import streamlit as st
 st.set_page_config(layout="wide")
 import numpy as np
 import pandas as pd
-import gspread
 import pymongo
 import time
 
 @st.cache_resource
 def init_conn():
-        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
-
-        credentials = {
-          "type": "service_account",
-          "project_id": "model-sheets-connect",
-          "private_key_id": st.secrets['model_sheets_connect_pk'],
-          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
-          "client_email": "[email protected]",
-          "client_id": "100369174533302798535",
-          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-          "token_uri": "https://oauth2.googleapis.com/token",
-          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
-        }
-        
-        credentials2 = {
-          "type": "service_account",
-          "project_id": "sheets-api-connect-378620",
-          "private_key_id": st.secrets['sheets_api_connect_pk'],
-          "private_key": "-----BEGIN PRIVATE KEY-----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\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
-          "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
-          "client_id": "106625872877651920064",
-          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-          "token_uri": "https://oauth2.googleapis.com/token",
-          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
-        }
         
         uri = st.secrets['mongo_uri']
         client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
         db = client["NBA_DFS"]
-     
-        NBA_Data = st.secrets['NBA_Data']
-
-        gc = gspread.service_account_from_dict(credentials)
-        gc2 = gspread.service_account_from_dict(credentials2)
 
-        return gc, gc2, db, NBA_Data
+        return db
     
-gcservice_account, gcservice_account2, db, NBA_Data = init_conn()
+db = init_conn()
 
 percentages_format = {'Exposure': '{:.2%}'}
 freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'}
 dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
-@st.cache_data(ttl = 300)
+@st.cache_data(ttl = 60)
 def init_DK_seed_frames():  
     
         collection = db["DK_NBA_seed_frame"] 
@@ -66,7 +33,7 @@ def init_DK_seed_frames():
 
         return DK_seed
 
-@st.cache_data(ttl = 300)
+@st.cache_data(ttl = 60)
 def init_DK_secondary_seed_frames():  
     
         collection = db["DK_NBA_Secondary_seed_frame"] 
@@ -78,7 +45,7 @@ def init_DK_secondary_seed_frames():
 
         return DK_secondary
 
-@st.cache_data(ttl = 300)
+@st.cache_data(ttl = 60)
 def init_FD_seed_frames():  
     
         collection = db["FD_NBA_seed_frame"] 
@@ -90,7 +57,7 @@ def init_FD_seed_frames():
 
         return FD_seed
 
-@st.cache_data(ttl = 300)
+@st.cache_data(ttl = 60)
 def init_FD_secondary_seed_frames():  
     
         collection = db["FD_NBA_Secondary_seed_frame"] 
@@ -102,13 +69,15 @@ def init_FD_secondary_seed_frames():
 
         return FD_secondary
 
-@st.cache_resource(ttl = 301)
+@st.cache_resource(ttl = 60)
 def init_baselines():
-    sh = gcservice_account.open_by_url('https://docs.google.com/spreadsheets/d/1Yq0vGriWK-bS79e-bD6_u9pqrYE6Yrlbb_wEkmH-ot0/edit#gid=172632260')
-    worksheet = sh.worksheet('Player_Level_ROO')
-    load_display = pd.DataFrame(worksheet.get_all_records())
+    collection = db["Player_Range_Of_Outcomes"] 
+    cursor = collection.find()
+
+    load_display = pd.DataFrame(list(cursor))
+
     load_display.replace('', np.nan, inplace=True)
-    load_display.rename(columns={"Fantasy": "Median", 'Name': 'Player'}, inplace = True)
+    load_display.rename(columns={"Fantasy": "Median", 'Name': 'Player', 'player_ID': 'player_id'}, inplace = True)
     load_display = load_display[load_display['Median'] > 0]
 
     dk_roo_raw = load_display[load_display['site'] == 'Draftkings']
@@ -153,10 +122,9 @@ def calculate_FD_value_frequencies(np_array):
     return combined_array
 
 @st.cache_data
-def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
+def sim_contest(Sim_size, seed_frame, maps_dict, Contest_Size):
     SimVar = 1
     Sim_Winners = []
-    fp_array = seed_frame[:sharp_split, :]
     
     # Pre-vectorize functions
     vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
@@ -165,7 +133,7 @@ def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
     st.write('Simulating contest on frames')
     
     while SimVar <= Sim_size:
-        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
+        fp_random = seed_frame[np.random.choice(seed_frame.shape[0], Contest_Size)]
             
         sample_arrays1 = np.c_[
             fp_random, 
@@ -187,6 +155,8 @@ def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
     return Sim_Winners
 
 dk_raw, fd_raw, dk_secondary, fd_secondary = init_baselines()
+dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
 
 tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
 
@@ -197,25 +167,20 @@ with tab2:
               st.cache_data.clear()
               for key in st.session_state.keys():
                   del st.session_state[key]
-              DK_seed = init_DK_seed_frames()
-              FD_seed = init_FD_seed_frames()
-              DK_secondary = init_DK_secondary_seed_frames()
-              FD_secondary = init_FD_secondary_seed_frames()
+              DK_seed = init_DK_seed_frames(10000)
+              FD_seed = init_FD_seed_frames(10000)
+              DK_secondary = init_DK_secondary_seed_frames(10000)
+              FD_secondary = init_FD_secondary_seed_frames(10000)
               dk_raw, fd_raw, dk_secondary, fd_secondary = init_baselines()
+              dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+              fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
               
         slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='slate_var1')
         site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
+        sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000)
         lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=500, value=10, step=1)
 
         if site_var1 == 'Draftkings':
-            if slate_var1 == 'Main Slate':
-                DK_seed = init_DK_seed_frames()
-                raw_baselines = dk_raw
-                column_names = dk_columns
-            elif slate_var1 == 'Secondary Slate':
-                DK_seed = init_DK_secondary_seed_frames()
-                raw_baselines = dk_secondary
-                column_names = dk_columns
             
             player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
             if player_var1 == 'Specific Players':
@@ -223,24 +188,70 @@ with tab2:
             elif player_var1 == 'Full Slate':
                     player_var2 = dk_raw.Player.values.tolist()
                     
+            raw_baselines = dk_raw
+            column_names = dk_columns
+            
         elif site_var1 == 'Fanduel':
-            if slate_var1 == 'Main Slate':
-                FD_seed = init_FD_seed_frames()
-                raw_baselines = fd_raw
-                column_names = fd_columns
-            elif slate_var1 == 'Secondary Slate':
-                FD_seed = init_FD_secondary_seed_frames()
-                raw_baselines = fd_secondary
-                column_names = fd_columns
             
             player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
             if player_var1 == 'Specific Players':
                     player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique())
             elif player_var1 == 'Full Slate':
                     player_var2 = fd_raw.Player.values.tolist()
+            
+            raw_baselines = fd_raw
+            column_names = fd_columns
 
         if st.button("Prepare data export", key='data_export'):
-            data_export = st.session_state.working_seed.copy()
+            if site_var1 == 'Draftkings':
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed
+                    if player_var1 == 'Specific Players':
+                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+                elif 'working_seed' not in st.session_state:
+                    if slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
+                        dk_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
+                    elif slate_var1 == 'Secondary Slate':
+                        st.session_state.working_seed = init_DK_secondary_seed_frames(sharp_split_var)
+                        dk_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
+
+                    raw_baselines = dk_raw
+                    column_names = dk_columns
+                
+                    if player_var1 == 'Specific Players':
+                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+                    export_column_var = 8
+                data_export = st.session_state.data_export_display.copy()
+                for col in range(export_column_var):
+                        data_export[:, col] = np.array([dk_id_dict.get(x, x) for x in data_export[:, col]])
+                
+            elif site_var1 == 'Fanduel':
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed
+                    if player_var1 == 'Specific Players':
+                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+                elif 'working_seed' not in st.session_state:
+                    if slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
+                        fd_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
+                    elif slate_var1 == 'Secondary Slate':
+                        st.session_state.working_seed = init_FD_secondary_seed_frames(sharp_split_var)
+                        fd_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
+                    
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
+
+                    if player_var1 == 'Specific Players':
+                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
+                    export_column_var = 9
+                data_export = st.session_state.data_export_display.copy()
+                for col in range(export_column_var):
+                        data_export[:, col] = np.array([fd_id_dict.get(x, x) for x in fd_id_dict[:, col]])
             st.download_button(
                 label="Export optimals set",
                 data=convert_df(data_export),
@@ -255,16 +266,20 @@ with tab2:
                     st.session_state.working_seed = st.session_state.working_seed
                     if player_var1 == 'Specific Players':
                         st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
-                    elif player_var1 == 'Full Slate':
-                        st.session_state.working_seed = DK_seed.copy()
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
                 elif 'working_seed' not in st.session_state:
-                    st.session_state.working_seed = DK_seed.copy()
-                    st.session_state.working_seed = st.session_state.working_seed
+                    if slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
+                        dk_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
+                    elif slate_var1 == 'Secondary Slate':
+                        st.session_state.working_seed = init_DK_secondary_seed_frames(sharp_split_var)
+                        dk_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
+
+                    raw_baselines = dk_raw
+                    column_names = dk_columns
+                
                     if player_var1 == 'Specific Players':
                         st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
-                    elif player_var1 == 'Full Slate':
-                        st.session_state.working_seed = DK_seed.copy()
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
                 
             elif site_var1 == 'Fanduel':
@@ -272,26 +287,22 @@ with tab2:
                     st.session_state.working_seed = st.session_state.working_seed
                     if player_var1 == 'Specific Players':
                         st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
-                    elif player_var1 == 'Full Slate':
-                        st.session_state.working_seed = FD_seed.copy()
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
                 elif 'working_seed' not in st.session_state:
-                    st.session_state.working_seed = FD_seed.copy()
-                    st.session_state.working_seed = st.session_state.working_seed
+                    if slate_var1 == 'Main Slate':
+                        st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
+                        fd_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
+                    elif slate_var1 == 'Secondary Slate':
+                        st.session_state.working_seed = init_FD_secondary_seed_frames(sharp_split_var)
+                        fd_id_dict = dict(zip(st.session_state.working_seed.Player, st.session_state.working_seed.player_id))
+                    
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
+
                     if player_var1 == 'Specific Players':
                         st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
-                    elif player_var1 == 'Full Slate':
-                        st.session_state.working_seed = FD_seed.copy()
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
                 
-        with st.container():
-            if st.button("Reset Optimals", key='reset3'):
-                for key in st.session_state.keys():
-                    del st.session_state[key]
-                if site_var1 == 'Draftkings':
-                    st.session_state.working_seed = DK_seed.copy()
-                elif site_var1 == 'Fanduel':
-                    st.session_state.working_seed = FD_seed.copy()
             if 'data_export_display' in st.session_state:
                 st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
             
@@ -302,31 +313,16 @@ with tab1:
               st.cache_data.clear()
               for key in st.session_state.keys():
                   del st.session_state[key]
-              DK_seed = init_DK_seed_frames()
-              FD_seed = init_FD_seed_frames()
-              DK_secondary = init_DK_secondary_seed_frames()
-              FD_secondary = init_FD_secondary_seed_frames()
+              DK_seed = init_DK_seed_frames(10000)
+              FD_seed = init_FD_seed_frames(10000)
+              DK_secondary = init_DK_secondary_seed_frames(10000)
+              FD_secondary = init_FD_secondary_seed_frames(10000)
               dk_raw, fd_raw, dk_secondary, fd_secondary = init_baselines()
+              dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
+              fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
+
         sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='sim_slate_var1')
         sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
-        if sim_site_var1 == 'Draftkings':
-            if sim_slate_var1 == 'Main Slate':
-                DK_seed = init_DK_seed_frames()
-                raw_baselines = dk_raw
-                column_names = dk_columns
-            elif sim_slate_var1 == 'Secondary Slate':
-                DK_seed = init_DK_secondary_seed_frames()
-                raw_baselines = dk_secondary
-                column_names = dk_columns
-        elif sim_site_var1 == 'Fanduel':
-            if sim_slate_var1 == 'Main Slate':
-                FD_seed = init_FD_seed_frames()
-                raw_baselines = fd_raw
-                column_names = fd_columns
-            elif sim_slate_var1 == 'Secondary Slate':
-                FD_seed = init_FD_secondary_seed_frames()
-                raw_baselines = fd_secondary
-                column_names = fd_columns
             
         contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
         if contest_var1 == 'Small':
@@ -336,7 +332,7 @@ with tab1:
         elif contest_var1 == 'Large':
             Contest_Size = 10000
         elif contest_var1 == 'Custom':
-            Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...")
+            Contest_Size = st.number_input("Insert contest size", value=100, min_value=100, max_value=100000, step=50)
         strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'))
         if strength_var1 == 'Not Very':
             sharp_split = 500000
@@ -361,7 +357,7 @@ with tab1:
                         'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
                         'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
                         }
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, sharp_split, Contest_Size)
+                Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size)
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                             
                 # Initial setup
@@ -386,9 +382,25 @@ with tab1:
                 
             else:
                 if sim_site_var1 == 'Draftkings':
-                    st.session_state.working_seed = DK_seed.copy()
+                    if sim_slate_var1 == 'Main Slate':
+                        DK_seed = init_DK_seed_frames(sharp_split)
+                        raw_baselines = dk_raw
+                        column_names = dk_columns
+                    elif sim_slate_var1 == 'Secondary Slate':
+                        DK_seed = init_DK_secondary_seed_frames(sharp_split)
+                        raw_baselines = dk_secondary
+                        column_names = dk_columns
+
                 elif sim_site_var1 == 'Fanduel':
-                    st.session_state.working_seed = FD_seed.copy()
+                    if sim_slate_var1 == 'Main Slate':
+                        FD_seed = init_FD_seed_frames(sharp_split)
+                        raw_baselines = fd_raw
+                        column_names = fd_columns
+                    elif sim_slate_var1 == 'Secondary Slate':
+                        FD_seed = init_FD_secondary_seed_frames(sharp_split)
+                        raw_baselines = fd_secondary
+                        column_names = fd_columns
+
                 st.session_state.maps_dict = {
                         'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
                         'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
@@ -397,7 +409,7 @@ with tab1:
                         'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
                         'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
                         }
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, sharp_split, Contest_Size)
+                Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size)
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                             
                 # Initial setup