Refactor app.py to streamline database connections and enhance seed frame initialization. Removed hardcoded credentials and improved function signatures to include a 'split' parameter for limiting data retrieval. Updated user interface logic for selecting sports and contest types, ensuring better data handling and export functionality.

app.py

@@ -8,47 +8,13 @@ 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": "gspread-connection@model-sheets-connect.iam.gserviceaccount.com",
-          "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)
-     
-        NFL_Data = st.secrets['NFL_Data']
-
-        NBA_Data = st.secrets['NBA_Data']
 
-        gc = gspread.service_account_from_dict(credentials)
-        gc2 = gspread.service_account_from_dict(credentials2)
-
-        return gc, gc2, client, NFL_Data, NBA_Data
+        return client
     
-gcservice_account, gcservice_account2, client, NFL_Data, NBA_Data = init_conn()
+client = init_conn()
 
 percentages_format = {'Exposure': '{:.2%}'}
 freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
@@ -56,14 +22,14 @@ dk_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'pro
 fd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
 @st.cache_data(ttl = 599)
-def init_DK_seed_frames(sport): 
+def init_DK_seed_frames(sport, split): 
         if sport == 'NFL':
             db = client["NFL_Database"]
         elif sport == 'NBA':
             db = client["NBA_DFS"]
         
         collection = db[f"DK_{sport}_SD_seed_frame"] 
-        cursor = collection.find()
+        cursor = collection.find().limit(split)
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
@@ -72,7 +38,7 @@ def init_DK_seed_frames(sport):
         return DK_seed
     
 @st.cache_data(ttl = 599)
-def init_DK_secondary_seed_frames(sport):  
+def init_DK_secondary_seed_frames(sport, split):  
         
         if sport == 'NFL':
             db = client["NFL_Database"]
@@ -80,7 +46,7 @@ def init_DK_secondary_seed_frames(sport):
             db = client["NBA_DFS"]
     
         collection = db[f"DK_{sport}_Secondary_SD_seed_frame"] 
-        cursor = collection.find()
+        cursor = collection.find().limit(split)
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
@@ -89,7 +55,7 @@ def init_DK_secondary_seed_frames(sport):
         return DK_second_seed
 
 @st.cache_data(ttl = 599)
-def init_DK_auxiliary_seed_frames(sport):  
+def init_DK_auxiliary_seed_frames(sport, split):  
         
         if sport == 'NFL':
             db = client["NFL_Database"]
@@ -97,7 +63,7 @@ def init_DK_auxiliary_seed_frames(sport):
             db = client["NBA_DFS"]
     
         collection = db[f"DK_{sport}_Auxiliary_SD_seed_frame"] 
-        cursor = collection.find()
+        cursor = collection.find().limit(split)
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
@@ -106,7 +72,7 @@ def init_DK_auxiliary_seed_frames(sport):
         return DK_auxiliary_seed
     
 @st.cache_data(ttl = 599)
-def init_FD_seed_frames(sport):  
+def init_FD_seed_frames(sport, split):  
         
         if sport == 'NFL':
             db = client["NFL_Database"]
@@ -114,7 +80,7 @@ def init_FD_seed_frames(sport):
             db = client["NBA_DFS"]
     
         collection = db[f"FD_{sport}_SD_seed_frame"] 
-        cursor = collection.find()
+        cursor = collection.find().limit(split)
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
@@ -123,7 +89,7 @@ def init_FD_seed_frames(sport):
         return FD_seed
     
 @st.cache_data(ttl = 599)
-def init_FD_secondary_seed_frames(sport):  
+def init_FD_secondary_seed_frames(sport, split):  
         
         if sport == 'NFL':
             db = client["NFL_Database"]
@@ -131,7 +97,7 @@ def init_FD_secondary_seed_frames(sport):
             db = client["NBA_DFS"]
     
         collection = db[f"FD_{sport}_Secondary_SD_seed_frame"] 
-        cursor = collection.find()
+        cursor = collection.find().limit(split)
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
@@ -140,7 +106,7 @@ def init_FD_secondary_seed_frames(sport):
         return FD_second_seed
 
 @st.cache_data(ttl = 599)
-def init_FD_auxiliary_seed_frames(sport):  
+def init_FD_auxiliary_seed_frames(sport, split):  
         
         if sport == 'NFL':
             db = client["NFL_Database"]
@@ -148,7 +114,7 @@ def init_FD_auxiliary_seed_frames(sport):
             db = client["NBA_DFS"]
     
         collection = db[f"FD_{sport}_Auxiliary_SD_seed_frame"] 
-        cursor = collection.find()
+        cursor = collection.find().limit(split)
     
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
@@ -246,10 +212,9 @@ 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__)
@@ -260,7 +225,7 @@ 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,
@@ -285,6 +250,8 @@ def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
         
     return Sim_Winners
 
+dk_raw, fd_raw = init_baselines('NFL')
+
 tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
 with tab2:
     col1, col2 = st.columns([1, 7])
@@ -296,31 +263,11 @@ with tab2:
               dk_raw, fd_raw = init_baselines('NFL')
         
         sport_var1 = st.radio("What sport are you working with?", ('NFL', 'NBA'), key='sport_var1')
-        dk_raw, fd_raw = init_baselines(sport_var1)
         slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), key='slate_var1')
+        sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000)
             
         site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
         if site_var1 == 'Draftkings':
-            if slate_var1 == 'Showdown':
-                DK_seed = init_DK_seed_frames(sport_var1)
-                if sport_var1 == 'NFL':
-                    export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                elif sport_var1 == 'NBA':
-                    export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-            elif slate_var1 == 'Secondary Showdown':
-                DK_seed = init_DK_secondary_seed_frames(sport_var1)
-                if sport_var1 == 'NFL':
-                    export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                elif sport_var1 == 'NBA':
-                    export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-            elif slate_var1 == 'Auxiliary Showdown':
-                DK_seed = init_DK_auxiliary_seed_frames(sport_var1)
-                if sport_var1 == 'NFL':
-                    export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                elif sport_var1 == 'NBA':
-                    export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-            raw_baselines = dk_raw
-            column_names = dk_columns
             
             team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
             if team_var1 == 'Specific Teams':
@@ -335,26 +282,6 @@ with tab2:
                     stack_var2 = [5, 4, 3, 2, 1, 0]
                     
         elif site_var1 == 'Fanduel':
-            if slate_var1 == 'Showdown':
-                FD_seed = init_FD_seed_frames(sport_var1)
-                if sport_var1 == 'NFL':
-                    export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                elif sport_var1 == 'NBA':
-                    export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-            elif slate_var1 == 'Secondary Showdown':
-                FD_seed = init_FD_secondary_seed_frames(sport_var1)
-                if sport_var1 == 'NFL':
-                    export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                elif sport_var1 == 'NBA':
-                    export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-            elif slate_var1 == 'Auxiliary Showdown':
-                FD_seed = init_FD_auxiliary_seed_frames(sport_var1)
-                if sport_var1 == 'NFL':
-                    export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                elif sport_var1 == 'NBA':
-                    export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-            raw_baselines = fd_raw
-            column_names = fd_columns
             
             team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
             if team_var1 == 'Specific Teams':
@@ -370,12 +297,58 @@ with tab2:
         
 
         if st.button("Prepare data export", key='data_export'):
+            if 'working_seed' in st.session_state:
                 data_export = st.session_state.working_seed.copy()
-                data_export[0:6, 0] = [export_id_dict[x] for x in data_export[0:6, 0]]
-                st.download_button(
-                    label="Export optimals set",
-                    data=convert_df(data_export),
-                    file_name='NFL_SD_optimals_export.csv',
+            elif 'working_seed' not in st.session_state:
+                if site_var1 == 'Draftkings':
+                    if slate_var1 == 'Showdown':
+                        st.session_state.working_seed = init_DK_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif slate_var1 == 'Secondary Showdown':
+                        st.session_state.working_seed = init_DK_secondary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif slate_var1 == 'Auxiliary Showdown':
+                        st.session_state.working_seed = init_DK_auxiliary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    raw_baselines = dk_raw
+                    column_names = dk_columns
+                elif site_var1 == 'Fanduel':
+                    if slate_var1 == 'Showdown':
+                        st.session_state.working_seed = init_FD_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif slate_var1 == 'Secondary Showdown':
+                        st.session_state.working_seed = init_FD_secondary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif slate_var1 == 'Auxiliary Showdown':
+                        st.session_state.working_seed = init_FD_auxiliary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
+            data_export = st.session_state.working_seed.copy()
+            for col in range(6):
+                data_export[:, col] = np.array([export_id_dict.get(x, x) for x in data_export[:, col]])
+            st.download_button(
+                label="Export optimals set",
+                data=convert_df(data_export),
+                file_name='NFL_SD_optimals_export.csv',
                     mime='text/csv',
                 )
             
@@ -387,7 +360,24 @@ with tab2:
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)]
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
                 elif 'working_seed' not in st.session_state:
-                    st.session_state.working_seed = DK_seed.copy()
+                    if slate_var1 == 'Showdown':
+                        st.session_state.working_seed = init_DK_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif slate_var1 == 'Secondary Showdown':
+                        st.session_state.working_seed = init_DK_secondary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif slate_var1 == 'Auxiliary Showdown':
+                        st.session_state.working_seed = init_DK_auxiliary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], team_var2)]
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 10], stack_var2)]
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
@@ -398,7 +388,26 @@ with tab2:
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
                 elif 'working_seed' not in st.session_state:
-                    st.session_state.working_seed = FD_seed.copy()
+                    if slate_var1 == 'Showdown':
+                        st.session_state.working_seed = init_FD_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif slate_var1 == 'Secondary Showdown':
+                        st.session_state.working_seed = init_FD_secondary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif slate_var1 == 'Auxiliary Showdown':
+                        st.session_state.working_seed = init_FD_auxiliary_seed_frames(sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)]
                     st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
                     st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
@@ -420,48 +429,11 @@ with tab1:
         sim_slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown', 'Auxiliary Showdown'), 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 == 'Showdown':
-                DK_seed = init_DK_seed_frames(sim_sport_var1)
-                if sport_var1 == 'NFL':
-                    export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                elif sport_var1 == 'NBA':
-                    export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-            elif sim_slate_var1 == 'Secondary Showdown':
-                DK_seed = init_DK_secondary_seed_frames(sim_sport_var1)
-                if sport_var1 == 'NFL':
-                    export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                elif sport_var1 == 'NBA':
-                    export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-            elif sim_slate_var1 == 'Auxiliary Showdown':
-                DK_seed = init_DK_auxiliary_seed_frames(sim_sport_var1)
-                if sport_var1 == 'NFL':
-                    export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
-                elif sport_var1 == 'NBA':
-                    export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
             raw_baselines = dk_raw
             column_names = dk_columns
         elif sim_site_var1 == 'Fanduel':
-            if sim_slate_var1 == 'Showdown':
-                FD_seed = init_FD_seed_frames(sim_sport_var1)
-                if sport_var1 == 'NFL':
-                    export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                elif sport_var1 == 'NBA':
-                    export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-            elif sim_slate_var1 == 'Secondary Showdown':
-                FD_seed = init_FD_secondary_seed_frames(sim_sport_var1)
-                if sport_var1 == 'NFL':
-                    export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                elif sport_var1 == 'NBA':
-                    export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-            elif sim_slate_var1 == 'Auxiliary Showdown':
-                FD_seed = init_FD_auxiliary_seed_frames(sim_sport_var1)
-                if sport_var1 == 'NFL':
-                    export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
-                elif sport_var1 == 'NBA':
-                    export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
             raw_baselines = fd_raw
             column_names = fd_columns
-            
         contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
         if contest_var1 == 'Small':
             Contest_Size = 1000
@@ -503,7 +475,7 @@ with tab1:
                         'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
                         'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
                         }
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, Contest_Size)
+                Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, Contest_Size)
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                 
                 #st.table(Sim_Winner_Frame)
@@ -530,9 +502,47 @@ with tab1:
                 
             else:
                 if sim_site_var1 == 'Draftkings':
-                    st.session_state.working_seed = DK_seed.copy()
+                    if sim_slate_var1 == 'Showdown':
+                        st.session_state.working_seed = init_DK_seed_frames(sim_sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif sim_slate_var1 == 'Secondary Showdown':
+                        st.session_state.working_seed = init_DK_secondary_seed_frames(sim_sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    elif sim_slate_var1 == 'Auxiliary Showdown':
+                        st.session_state.working_seed = init_DK_auxiliary_seed_frames(sim_sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(dk_raw['Player'], dk_raw['player_id']))
+                    raw_baselines = dk_raw
+                    column_names = dk_columns
                 elif sim_site_var1 == 'Fanduel':
-                    st.session_state.working_seed = FD_seed.copy()
+                    if sim_slate_var1 == 'Showdown':
+                        st.session_state.working_seed = init_FD_seed_frames(sim_sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif sim_slate_var1 == 'Secondary Showdown':
+                        st.session_state.working_seed = init_FD_secondary_seed_frames(sim_sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    elif sim_slate_var1 == 'Auxiliary Showdown':
+                        st.session_state.working_seed = init_FD_auxiliary_seed_frames(sim_sport_var1, sharp_split_var)
+                        if sport_var1 == 'NFL':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                        elif sport_var1 == 'NBA':
+                            export_id_dict = dict(zip(fd_raw['Player'], fd_raw['player_id']))
+                    raw_baselines = fd_raw
+                    column_names = fd_columns
                 maps_dict = {
                         'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
                         'cpt_projection_map':dict(zip(raw_baselines.Player,raw_baselines.cpt_Median)),
@@ -544,7 +554,7 @@ with tab1:
                         'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev)),
                         'cpt_STDev_map':dict(zip(raw_baselines.Player,raw_baselines['CPT_STDev']))
                         }
-                Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, sharp_split, Contest_Size)
+                Sim_Winners = sim_contest(1000, st.session_state.working_seed, maps_dict, Contest_Size)
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                 
                 #st.table(Sim_Winner_Frame)