Create app.py

app.py

@@ -0,0 +1,589 @@
+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)
+     
+        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
+    
+gcservice_account, gcservice_account2, client, NFL_Data, NBA_Data = init_conn()
+
+percentages_format = {'Exposure': '{:.2%}'}
+freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
+dk_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+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): 
+        if sport == 'NFL':
+            db = client["testing_db"]
+        elif sport == 'NBA':
+            db = client["NBA_DFS"]
+        
+        collection = db[f"DK_{sport}_SD_seed_frame"] 
+        cursor = collection.find()
+    
+        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']]
+        DK_seed = raw_display.to_numpy()
+
+        return DK_seed
+    
+@st.cache_data(ttl = 599)
+def init_DK_secondary_seed_frames(sport):  
+        
+        if sport == 'NFL':
+            db = client["testing_db"]
+        elif sport == 'NBA':
+            db = client["NBA_DFS"]
+    
+        collection = db[f"DK_{sport}_Secondary_SD_seed_frame"] 
+        cursor = collection.find()
+    
+        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']]
+        DK_second_seed = raw_display.to_numpy()
+
+        return DK_second_seed
+    
+@st.cache_data(ttl = 599)
+def init_FD_seed_frames(sport):  
+        
+        if sport == 'NFL':
+            db = client["testing_db"]
+        elif sport == 'NBA':
+            db = client["NBA_DFS"]
+    
+        collection = db[f"FD_{sport}_SD_seed_frame"] 
+        cursor = collection.find()
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        FD_seed = raw_display.to_numpy()
+
+        return FD_seed
+    
+@st.cache_data(ttl = 599)
+def init_FD_secondary_seed_frames(sport):  
+        
+        if sport == 'NFL':
+            db = client["testing_db"]
+        elif sport == 'NBA':
+            db = client["NBA_DFS"]
+    
+        collection = db[f"FD_{sport}_Secondary_SD_seed_frame"] 
+        cursor = collection.find()
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        FD_second_seed = raw_display.to_numpy()
+
+        return FD_second_seed
+
+@st.cache_data(ttl = 599)
+def init_baselines(sport):
+    if sport == 'NFL':
+        try:
+            sh = gcservice_account.open_by_url(NFL_Data)
+        except:
+            sh = gcservice_account2.open_by_url(NFL_Data)
+        
+        worksheet = sh.worksheet('DK_SD_ROO')
+        load_display = pd.DataFrame(worksheet.get_all_records())
+        load_display.replace('', np.nan, inplace=True)
+        load_display['STDev'] = load_display['Median'] / 4
+        load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
+        
+        dk_raw = load_display.dropna(subset=['Median'])
+        
+        worksheet = sh.worksheet('FD_SD_ROO')
+        load_display = pd.DataFrame(worksheet.get_all_records())
+        load_display.replace('', np.nan, inplace=True)
+        load_display['STDev'] = load_display['Median'] / 4
+        load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
+        
+        fd_raw = load_display.dropna(subset=['Median'])
+    
+    elif sport == 'NBA':
+         
+        try:
+            sh = gcservice_account.open_by_url(NBA_Data)
+        except:
+            sh = gcservice_account2.open_by_url(NBA_Data)
+        
+        worksheet = sh.worksheet('Player_Level_SD_ROO')
+        load_display = pd.DataFrame(worksheet.get_all_records())
+        load_display.replace('', np.nan, inplace=True)
+        load_display['STDev'] = load_display['Median'] / 4
+        load_display = load_display[load_display['site'] == 'Draftkings']
+        load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
+        
+        dk_raw = load_display.dropna(subset=['Median'])
+        
+        worksheet = sh.worksheet('Player_Level_SD_ROO')
+        load_display = pd.DataFrame(worksheet.get_all_records())
+        load_display.replace('', np.nan, inplace=True)
+        load_display['STDev'] = load_display['Median'] / 4
+        load_display = load_display[load_display['site'] == 'Fanduel']
+        load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
+        
+        fd_raw = load_display.dropna(subset=['Median'])
+
+    return dk_raw, fd_raw
+
+@st.cache_data
+def convert_df(array):
+    array = pd.DataFrame(array, columns=column_names)
+    return array.to_csv().encode('utf-8')
+
+@st.cache_data
+def calculate_DK_value_frequencies(np_array):
+    unique, counts = np.unique(np_array[:, :6], return_counts=True)
+    frequencies = counts / len(np_array)  # Normalize by the number of rows 
+    combined_array = np.column_stack((unique, frequencies))  
+    return combined_array 
+
+@st.cache_data
+def calculate_FD_value_frequencies(np_array):
+    unique, counts = np.unique(np_array[:, :5], return_counts=True)
+    frequencies = counts / len(np_array)  # Normalize by the number of rows 
+    combined_array = np.column_stack((unique, frequencies))  
+    return combined_array
+
+@st.cache_data
+def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, 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__)
+    vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
+    
+    st.write('Simulating contest on frames')
+    
+    while SimVar <= Sim_size:
+        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
+            
+        sample_arrays1 = np.c_[
+            fp_random, 
+            np.sum(np.random.normal(
+                loc=vec_projection_map(fp_random[:, :-7]),
+                scale=vec_stdev_map(fp_random[:, :-7])),
+            axis=1)
+        ]
+
+        sample_arrays = sample_arrays1
+
+        final_array = sample_arrays[sample_arrays[:, 7].argsort()[::-1]]
+        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
+        Sim_Winners.append(best_lineup)
+        SimVar += 1
+        
+    return Sim_Winners
+
+tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
+with tab2:
+    col1, col2 = st.columns([1, 7])
+    with col1:
+        if st.button("Load/Reset Data", key='reset1'):
+              st.cache_data.clear()
+              for key in st.session_state.keys():
+                  del st.session_state[key]
+              dk_raw, fd_raw = init_baselines('NFL')
+        
+        sport_var1 = st.radio("What sport are you working with?", ('NBA', 'NFL'), key='sport_var1')
+        dk_raw, fd_raw = init_baselines(sport_var1)
+        slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary Showdown'), key='slate_var1')
+            
+        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)
+            elif slate_var1 == 'Secondary Showdown':
+                DK_seed = init_DK_secondary_seed_frames(sport_var1)
+            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':
+                    team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique())
+            elif team_var1 == 'Full Slate':
+                    team_var2 = dk_raw.Team.values.tolist()
+            
+            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
+            if stack_var1 == 'Specific Stack Sizes':
+                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [5, 4, 3, 2, 1, 0])
+            elif stack_var1 == 'Full Slate':
+                    stack_var2 = [5, 4, 3, 2, 1, 0]
+                    
+        elif site_var1 == 'Fanduel':
+            if slate_var1 == 'Showdown':
+                FD_seed = init_FD_seed_frames(sport_var1)
+            elif slate_var1 == 'Secondary Showdown':
+                FD_seed = init_FD_secondary_seed_frames(sport_var1)
+            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':
+                    team_var2 = st.multiselect('Which teams do you want?', options = fd_raw['Team'].unique())
+            elif team_var1 == 'Full Slate':
+                    team_var2 = fd_raw.Team.values.tolist()
+            
+            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
+            if stack_var1 == 'Specific Stack Sizes':
+                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [4, 3, 2, 1, 0])
+            elif stack_var1 == 'Full Slate':
+                    stack_var2 = [4, 3, 2, 1, 0]
+        
+
+        if st.button("Prepare data export", key='data_export'):
+                data_export = st.session_state.working_seed.copy()
+                st.download_button(
+                    label="Export optimals set",
+                    data=convert_df(data_export),
+                    file_name='NFL_SD_optimals_export.csv',
+                    mime='text/csv',
+                )
+            
+    with col2:
+        if st.button("Load Data", key='load_data'):
+            if site_var1 == 'Draftkings':
+                if 'working_seed' in st.session_state:
+                    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)
+                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[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)
+                
+            elif site_var1 == 'Fanduel':
+                if 'working_seed' in st.session_state:
+                    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)
+                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[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)
+                
+        with st.container():
+            if 'data_export_display' in st.session_state:
+                st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True)
+            
+with tab1:
+    col1, col2 = st.columns([1, 7])
+    with col1:
+        if st.button("Load/Reset Data", key='reset2'):
+              st.cache_data.clear()
+              for key in st.session_state.keys():
+                  del st.session_state[key]
+              dk_raw, fd_raw = init_baselines('NFL')
+        sim_sport_var1 = st.radio("What sport are you working with?", ('NBA', 'NFL'), key='sim_sport_var1')
+        dk_raw, fd_raw = init_baselines(sim_sport_var1)
+        sim_slate_var1 = st.radio("Which data are you loading?", ('Showdown', 'Secondary 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)
+            elif sim_slate_var1 == 'Secondary Showdown':
+                DK_seed = init_DK_secondary_seed_frames(sim_sport_var1)
+            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)
+            elif sim_slate_var1 == 'Secondary Showdown':
+                FD_seed = init_FD_secondary_seed_frames(sim_sport_var1)
+            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
+        elif contest_var1 == 'Medium':
+            Contest_Size = 5000
+        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...")
+        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
+        elif strength_var1 == 'Below Average':
+            sharp_split = 400000
+        elif strength_var1 == 'Average':
+            sharp_split = 300000
+        elif strength_var1 == 'Above Average':
+            sharp_split = 200000
+        elif strength_var1 == 'Very':
+            sharp_split = 100000
+
+    
+    with col2:
+        if st.button("Run Contest Sim"):
+            if 'working_seed' in 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)),
+                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                        'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                        '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, maps_dict, sharp_split, Contest_Size)
+                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+                
+                #st.table(Sim_Winner_Frame)
+                            
+                # Initial setup
+                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
+                Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
+                Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+                Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+                
+                # Type Casting
+                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
+                Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+                
+                # Sorting
+                st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+                st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+                
+                # Data Copying
+                st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                
+                # Data Copying
+                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+                
+            else:
+                if sim_site_var1 == 'Draftkings':
+                    st.session_state.working_seed = DK_seed.copy()
+                elif sim_site_var1 == 'Fanduel':
+                    st.session_state.working_seed = FD_seed.copy()
+                maps_dict = {
+                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                        'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                        'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                        '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, maps_dict, sharp_split, Contest_Size)
+                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+                
+                #st.table(Sim_Winner_Frame)
+                            
+                # Initial setup
+                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
+                Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
+                Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+                Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+                
+                # Type Casting
+                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
+                Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+                
+                # Sorting
+                st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+                st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+                
+               # Data Copying
+                st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                
+                # Data Copying
+                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+                freq_copy = st.session_state.Sim_Winner_Display
+            
+            if sim_site_var1 == 'Draftkings':
+                freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:5].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            freq_working['Freq'] = freq_working['Freq'].astype(int)
+            freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map'])
+            if sim_site_var1 == 'Draftkings':
+                freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5
+            elif sim_site_var1 == 'Fanduel':
+                freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
+            freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100
+            freq_working['Exposure'] = freq_working['Freq']/(1000)
+            freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
+            freq_working['Team'] = freq_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.player_freq = freq_working.copy()
+
+            if sim_site_var1 == 'Draftkings':
+                cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                cpt_own_div = 600
+            elif sim_site_var1 == 'Fanduel':
+                cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                cpt_own_div = 500
+            cpt_working['Freq'] = cpt_working['Freq'].astype(int)
+            cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
+            cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
+            cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['Own_map']) / cpt_own_div
+            cpt_working['Exposure'] = cpt_working['Freq']/(1000)
+            cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']
+            cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.sp_freq = cpt_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:6].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                cpt_own_div = 600
+            elif sim_site_var1 == 'Fanduel':
+                flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:5].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                cpt_own_div = 500
+            flex_working['Freq'] = flex_working['Freq'].astype(int)
+            flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map'])
+            if sim_site_var1 == 'Draftkings':
+                flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5
+            elif sim_site_var1 == 'Fanduel':
+                flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map'])
+            flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['Own_map']) / cpt_own_div)
+            flex_working['Exposure'] = flex_working['Freq']/(1000)
+            flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
+            flex_working['Team'] = flex_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.flex_freq = flex_working.copy()
+
+            if sim_site_var1 == 'Draftkings':
+                team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,8:9].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,7:8].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            team_working['Freq'] = team_working['Freq'].astype(int)
+            team_working['Exposure'] = team_working['Freq']/(1000)
+            st.session_state.team_freq = team_working.copy()
+
+            if sim_site_var1 == 'Draftkings':
+                stack_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,10:11].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            elif sim_site_var1 == 'Fanduel':
+                stack_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,9:10].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            stack_working['Freq'] = stack_working['Freq'].astype(int)
+            stack_working['Exposure'] = stack_working['Freq']/(1000)
+            st.session_state.stack_freq = stack_working.copy()
+            
+        with st.container():
+            if st.button("Reset Sim", key='reset_sim'):
+                for key in st.session_state.keys():
+                    del st.session_state[key]
+            if 'player_freq' in st.session_state: 
+                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
+                if player_split_var2 == 'Specific Players':
+                          find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
+                elif player_split_var2 == 'Full Players':
+                          find_var2 = st.session_state.player_freq.Player.values.tolist()
+    
+                if player_split_var2 == 'Specific Players':
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
+                if player_split_var2 == 'Full Players':
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
+            if 'Sim_Winner_Display' in st.session_state:
+                st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+            if 'Sim_Winner_Export' in st.session_state:
+                st.download_button(
+                    label="Export Full Frame",
+                    data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
+                    file_name='NFL_SD_consim_export.csv',
+                    mime='text/csv',
+                )  
+                
+        with st.container():
+            tab1, tab2, tab3, tab4 = st.tabs(['Overall Exposures', 'CPT Exposures', 'FLEX Exposures', 'Team Exposures'])
+            with tab1:
+                if 'player_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.player_freq.to_csv().encode('utf-8'),
+                        file_name='player_freq_export.csv',
+                        mime='text/csv',
+                        key='overall'
+                    )
+            with tab2:
+                if 'sp_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.sp_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.sp_freq.to_csv().encode('utf-8'),
+                        file_name='cpt_freq.csv',
+                        mime='text/csv',
+                        key='sp'
+                    )
+            with tab3:
+                if 'flex_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.flex_freq.to_csv().encode('utf-8'),
+                        file_name='flex_freq.csv',
+                        mime='text/csv',
+                        key='flex'
+                    )
+            with tab4:
+                if 'team_freq' in st.session_state:
+                    
+                    st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.team_freq.to_csv().encode('utf-8'),
+                        file_name='team_freq.csv',
+                        mime='text/csv',
+                        key='team'
+                    )