app.py

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": "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)
        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
    
gcservice_account, gcservice_account2, db, NBA_Data = 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 = 600)
def init_DK_seed_frames():  
    
        collection = db["DK_NBA_seed_frame"] 
        cursor = collection.find()
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        DK_seed = raw_display.to_numpy()

        return DK_seed

@st.cache_data(ttl = 600)
def init_DK_secondary_seed_frames():  
    
        collection = db["DK_NBA_secondary_seed_frame"] 
        cursor = collection.find()
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        DK_secondary = raw_display.to_numpy()

        return DK_secondary

@st.cache_data(ttl = 599)
def init_FD_seed_frames():  
    
        collection = db["FD_NBA_seed_frame"] 
        cursor = collection.find()
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', '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():  
    
        collection = db["FD_NBA_secondary_seed_frame"] 
        cursor = collection.find()
    
        raw_display = pd.DataFrame(list(cursor))
        raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
        FD_secondary = raw_display.to_numpy()

        return FD_secondary

@st.cache_resource(ttl = 301)
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())
    load_display.replace('', np.nan, inplace=True)
    load_display.rename(columns={"Fantasy": "Median", 'Name': 'Player'}, inplace = True)
    load_display = load_display[load_display['Median'] > 0]

    dk_roo_raw = load_display[load_display['site'] == 'Draftkings']
    dk_roo_raw = dk_roo_raw[dk_roo_raw['slate'] == 'Main Slate']
    dk_roo_raw['STDev'] = dk_roo_raw['Median'] / 4
    dk_raw = dk_roo_raw.dropna(subset=['Median'])

    fd_roo_raw = load_display[load_display['site'] == 'Fanduel']
    fd_roo_raw = fd_roo_raw[fd_roo_raw['slate'] == 'Main Slate']
    fd_roo_raw['STDev'] = fd_roo_raw['Median'] / 4
    fd_raw = fd_roo_raw.dropna(subset=['Median'])

    dk_secondary_roo_raw = load_display[load_display['site'] == 'Draftkings']
    dk_secondary_roo_raw = dk_secondary_roo_raw[dk_secondary_roo_raw['slate'] == 'Secondary Slate']
    dk_secondary_roo_raw['STDev'] = dk_secondary_roo_raw['Median'] / 4
    dk_secondary = dk_secondary_roo_raw.dropna(subset=['Median'])

    fd_secondary_roo_raw = load_display[load_display['site'] == 'Fanduel']
    fd_secondary_roo_raw = fd_secondary_roo_raw[fd_secondary_roo_raw['slate'] == 'Secondary Slate']
    fd_secondary_roo_raw['STDev'] = fd_secondary_roo_raw['Median'] / 4
    fd_secondary = fd_secondary_roo_raw.dropna(subset=['Median'])

    return dk_raw, fd_raw, dk_secondary, fd_secondary

@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[:, :8], 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[:, :9], 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
        if sim_site_var1 == 'Draftkings':
            final_array = sample_arrays[sample_arrays[:, 9].argsort()[::-1]]
        elif sim_site_var1 == 'Fanduel':
            final_array = sample_arrays[sample_arrays[:, 10].argsort()[::-1]]
        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
        Sim_Winners.append(best_lineup)
        SimVar += 1
        
    return Sim_Winners

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_raw, fd_raw, dk_secondary, fd_secondary = init_baselines()

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_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_raw, fd_raw, dk_secondary, fd_secondary = init_baselines()
              
        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')
        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':
                raw_baselines = dk_raw
                column_names = dk_columns
            elif slate_var1 == 'Secondary Slate':
                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':
                    player_var2 = st.multiselect('Which players do you want?', options = dk_raw['Player'].unique())
            elif player_var1 == 'Full Slate':
                    player_var2 = dk_raw.Player.values.tolist()
                    
        elif site_var1 == 'Fanduel':
            if slate_var1 == 'Main Slate':
                raw_baselines = fd_raw
                column_names = fd_columns
            elif slate_var1 == 'Secondary Slate':
                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()

        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='NBA_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
                    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 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':
                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)]
                    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 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)
            
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_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_raw, fd_raw, dk_secondary, fd_secondary = init_baselines()
        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':
                raw_baselines = dk_raw
                column_names = dk_columns
            elif sim_slate_var1 == 'Secondary Slate':
                raw_baselines = dk_secondary
                column_names = dk_columns
        elif sim_site_var1 == 'Fanduel':
            if sim_slate_var1 == 'Main Slate':
                raw_baselines = fd_raw
                column_names = fd_columns
            elif sim_slate_var1 == 'Secondary Slate':
                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':
            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:
                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, st.session_state.maps_dict, sharp_split, Contest_Size)
                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                            
                # 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()
                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, st.session_state.maps_dict, sharp_split, Contest_Size)
                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                            
                # 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()
                st.session_state.freq_copy = st.session_state.Sim_Winner_Display
                
            if sim_site_var1 == 'Draftkings':
                freq_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:8].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(st.session_state.freq_copy.iloc[:,0:9].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(st.session_state.maps_dict['Pos_map'])
            freq_working['Salary'] = freq_working['Player'].map(st.session_state.maps_dict['Salary_map'])
            freq_working['Proj Own'] = freq_working['Player'].map(st.session_state.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(st.session_state.maps_dict['Team_map'])
            st.session_state.player_freq = freq_working.copy()

            if sim_site_var1 == 'Draftkings':
                pg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:1].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            elif sim_site_var1 == 'Fanduel':
                pg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:2].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            pg_working['Freq'] = pg_working['Freq'].astype(int)
            pg_working['Position'] = pg_working['Player'].map(st.session_state.maps_dict['Pos_map'])
            pg_working['Salary'] = pg_working['Player'].map(st.session_state.maps_dict['Salary_map'])
            pg_working['Proj Own'] = pg_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
            pg_working['Exposure'] = pg_working['Freq']/(1000)
            pg_working['Edge'] = pg_working['Exposure'] - pg_working['Proj Own']
            pg_working['Team'] = pg_working['Player'].map(st.session_state.maps_dict['Team_map'])
            st.session_state.pg_freq = pg_working.copy()
            
            if sim_site_var1 == 'Draftkings':
                sg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,1:2].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            elif sim_site_var1 == 'Fanduel':
                sg_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:4].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            sg_working['Freq'] = sg_working['Freq'].astype(int)
            sg_working['Position'] = sg_working['Player'].map(st.session_state.maps_dict['Pos_map'])
            sg_working['Salary'] = sg_working['Player'].map(st.session_state.maps_dict['Salary_map'])
            sg_working['Proj Own'] = sg_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
            sg_working['Exposure'] = sg_working['Freq']/(1000)
            sg_working['Edge'] = sg_working['Exposure'] - sg_working['Proj Own']
            sg_working['Team'] = sg_working['Player'].map(st.session_state.maps_dict['Team_map'])
            st.session_state.sg_freq = sg_working.copy()
            
            if sim_site_var1 == 'Draftkings':
                sf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,2:3].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            elif sim_site_var1 == 'Fanduel':
                sf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:6].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            sf_working['Freq'] = sf_working['Freq'].astype(int)
            sf_working['Position'] = sf_working['Player'].map(st.session_state.maps_dict['Pos_map'])
            sf_working['Salary'] = sf_working['Player'].map(st.session_state.maps_dict['Salary_map'])
            sf_working['Proj Own'] = sf_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
            sf_working['Exposure'] = sf_working['Freq']/(1000)
            sf_working['Edge'] = sf_working['Exposure'] - sf_working['Proj Own']
            sf_working['Team'] = sf_working['Player'].map(st.session_state.maps_dict['Team_map'])
            st.session_state.sf_freq = sf_working.copy()
            
            if sim_site_var1 == 'Draftkings':
                pf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,3:4].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            elif sim_site_var1 == 'Fanduel':
                pf_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:8].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            pf_working['Freq'] = pf_working['Freq'].astype(int)
            pf_working['Position'] = pf_working['Player'].map(st.session_state.maps_dict['Pos_map'])
            pf_working['Salary'] = pf_working['Player'].map(st.session_state.maps_dict['Salary_map'])
            pf_working['Proj Own'] = pf_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
            pf_working['Exposure'] = pf_working['Freq']/(1000)
            pf_working['Edge'] = pf_working['Exposure'] - pf_working['Proj Own']
            pf_working['Team'] = pf_working['Player'].map(st.session_state.maps_dict['Team_map'])
            st.session_state.pf_freq = pf_working.copy()
            
            if sim_site_var1 == 'Draftkings':
                c_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:5].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            elif sim_site_var1 == 'Fanduel':
                c_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,8:9].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            c_working['Freq'] = c_working['Freq'].astype(int)
            c_working['Position'] = c_working['Player'].map(st.session_state.maps_dict['Pos_map'])
            c_working['Salary'] = c_working['Player'].map(st.session_state.maps_dict['Salary_map'])
            c_working['Proj Own'] = c_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
            c_working['Exposure'] = c_working['Freq']/(1000)
            c_working['Edge'] = c_working['Exposure'] - c_working['Proj Own']
            c_working['Team'] = c_working['Player'].map(st.session_state.maps_dict['Team_map'])
            st.session_state.c_freq = c_working.copy()
            
            if sim_site_var1 == 'Draftkings':
                g_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,5:6].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            elif sim_site_var1 == 'Fanduel':
                g_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:4].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            g_working['Freq'] = g_working['Freq'].astype(int)
            g_working['Position'] = g_working['Player'].map(st.session_state.maps_dict['Pos_map'])
            g_working['Salary'] = g_working['Player'].map(st.session_state.maps_dict['Salary_map'])
            g_working['Proj Own'] = g_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
            g_working['Exposure'] = g_working['Freq']/(1000)
            g_working['Edge'] = g_working['Exposure'] - g_working['Proj Own']
            g_working['Team'] = g_working['Player'].map(st.session_state.maps_dict['Team_map'])
            st.session_state.g_freq = g_working.copy()
            
            if sim_site_var1 == 'Draftkings':
                f_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,6:7].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            elif sim_site_var1 == 'Fanduel':
                f_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,4:8].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            f_working['Freq'] = f_working['Freq'].astype(int)
            f_working['Position'] = f_working['Player'].map(st.session_state.maps_dict['Pos_map'])
            f_working['Salary'] = f_working['Player'].map(st.session_state.maps_dict['Salary_map'])
            f_working['Proj Own'] = f_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
            f_working['Exposure'] = f_working['Freq']/(1000)
            f_working['Edge'] = f_working['Exposure'] - f_working['Proj Own']
            f_working['Team'] = f_working['Player'].map(st.session_state.maps_dict['Team_map'])
            st.session_state.f_freq = f_working.copy()

            if sim_site_var1 == 'Draftkings':
                flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,7:8].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            elif sim_site_var1 == 'Fanduel':
                flex_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,0:9].values, return_counts=True)),
                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
            flex_working['Freq'] = flex_working['Freq'].astype(int)
            flex_working['Position'] = flex_working['Player'].map(st.session_state.maps_dict['Pos_map'])
            flex_working['Salary'] = flex_working['Player'].map(st.session_state.maps_dict['Salary_map'])
            flex_working['Proj Own'] = flex_working['Player'].map(st.session_state.maps_dict['Own_map']) / 100
            flex_working['Exposure'] = flex_working['Freq']/(1000)
            flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
            flex_working['Team'] = flex_working['Player'].map(st.session_state.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(st.session_state.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':
                team_working = pd.DataFrame(np.column_stack(np.unique(st.session_state.freq_copy.iloc[:,11:12].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()
            
        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='MLB_consim_export.csv',
                    mime='text/csv',
                )  
        tab1, tab2 = st.tabs(['Winning Frame Statistics', 'Flex Exposure Statistics'])
        
        with tab1:
            if 'Sim_Winner_Display' in st.session_state:
                # Create a new dataframe with summary statistics
                summary_df = pd.DataFrame({
                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
                    'Salary': [
                        st.session_state.Sim_Winner_Display['salary'].min(),
                        st.session_state.Sim_Winner_Display['salary'].mean(),
                        st.session_state.Sim_Winner_Display['salary'].max(),
                        st.session_state.Sim_Winner_Display['salary'].std()
                    ],
                    'Proj': [
                        st.session_state.Sim_Winner_Display['proj'].min(),
                        st.session_state.Sim_Winner_Display['proj'].mean(),
                        st.session_state.Sim_Winner_Display['proj'].max(),
                        st.session_state.Sim_Winner_Display['proj'].std()
                    ],
                    'Own': [
                        st.session_state.Sim_Winner_Display['Own'].min(),
                        st.session_state.Sim_Winner_Display['Own'].mean(),
                        st.session_state.Sim_Winner_Display['Own'].max(),
                        st.session_state.Sim_Winner_Display['Own'].std()
                    ],
                    'Fantasy': [
                        st.session_state.Sim_Winner_Display['Fantasy'].min(),
                        st.session_state.Sim_Winner_Display['Fantasy'].mean(),
                        st.session_state.Sim_Winner_Display['Fantasy'].max(),
                        st.session_state.Sim_Winner_Display['Fantasy'].std()
                    ],
                    'GPP_Proj': [
                        st.session_state.Sim_Winner_Display['GPP_Proj'].min(),
                        st.session_state.Sim_Winner_Display['GPP_Proj'].mean(),
                        st.session_state.Sim_Winner_Display['GPP_Proj'].max(),
                        st.session_state.Sim_Winner_Display['GPP_Proj'].std()
                    ]
                })

                # Set the index of the summary dataframe as the "Metric" column
                summary_df = summary_df.set_index('Metric')

                # Display the summary dataframe
                st.subheader("Winning Frame Statistics")
                st.dataframe(summary_df.style.format({
                    'Salary': '{:.2f}',
                    'Proj': '{:.2f}',
                    'Fantasy': '{:.2f}',
                    'GPP_Proj': '{:.2f}'
                }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own', 'Fantasy', 'GPP_Proj']), use_container_width=True)

        with tab2:
            if 'Sim_Winner_Display' in st.session_state:
                st.write("Yeah man that's crazy")
                
            else:
                st.write("Simulation data or position mapping not available.")
        with st.container():
            tab1, tab2 = st.tabs(['Overall 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 'team_freq' in st.session_state:
                    
                    st.dataframe(st.session_state.team_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.team_freq.to_csv().encode('utf-8'),
                        file_name='team_freq.csv',
                        mime='text/csv',
                        key='team'
                    )