import streamlit as st
st.set_page_config(layout="wide")
import numpy as np
import pandas as pd
import pymongo
import time

@st.cache_resource
def init_conn():
        
        uri = st.secrets['mongo_uri']
        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
        db = client["NBA_DFS"]

        return db
    
db = init_conn()

percentages_format = {'Exposure': '{:.2%}'}
freq_format = {'Proj Own': '{:.2%}', 'Exposure': '{:.2%}', 'Edge': '{:.2%}'}
dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']

st.markdown("""
<style>
    /* Tab styling */
    .stTabs [data-baseweb="tab-list"] {
        gap: 8px;
        padding: 4px;
    }

    .stTabs [data-baseweb="tab"] {
        height: 50px;
        white-space: pre-wrap;
        background-color: #FFD700;
        color: white;
        border-radius: 10px;
        gap: 1px;
        padding: 10px 20px;
        font-weight: bold;
        transition: all 0.3s ease;
    }

    .stTabs [aria-selected="true"] {
        background-color: #DAA520;
        color: white;
    }

    .stTabs [data-baseweb="tab"]:hover {
        background-color: #DAA520;
        cursor: pointer;
    }
</style>""", unsafe_allow_html=True)

@st.cache_data(ttl = 60)
def init_DK_seed_frames(load_size):  
        
        collection = db['DK_NBA_name_map']
        cursor = collection.find()
        raw_data = pd.DataFrame(list(cursor))
        names_dict = dict(zip(raw_data['key'], raw_data['value']))

        # Get the valid players from the Range of Outcomes collection
        collection = db["Player_Range_Of_Outcomes"]
        cursor = collection.find({"site": "Draftkings", "slate": "Main Slate"})
        valid_players = set(pd.DataFrame(list(cursor))['Player'].unique())
    
        collection = db["DK_NBA_seed_frame"] 
        cursor = collection.find().limit(load_size)
    
        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']]
        dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
        # Map names
        raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
        
        # Validate lineups against valid players
        raw_display = validate_lineup_players(raw_display, valid_players, dict_columns)
        
        # Remove any remaining NaN values
        raw_display = raw_display.dropna()
        DK_seed = raw_display.to_numpy()

        return DK_seed

@st.cache_data(ttl = 60)
def init_DK_secondary_seed_frames(load_size):  
        
        collection = db['DK_NBA_Secondary_name_map']
        cursor = collection.find()
        raw_data = pd.DataFrame(list(cursor))
        names_dict = dict(zip(raw_data['key'], raw_data['value']))

        # Get the valid players from the Range of Outcomes collection
        collection = db["Player_Range_Of_Outcomes"]
        cursor = collection.find({"site": "Draftkings", "slate": "Secondary Slate"})
        valid_players = set(pd.DataFrame(list(cursor))['Player'].unique())
    
        collection = db["DK_NBA_Secondary_seed_frame"] 
        cursor = collection.find().limit(load_size)
    
        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']]
        dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
        # Map names
        raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
        
        # Validate lineups against valid players
        raw_display = validate_lineup_players(raw_display, valid_players, dict_columns)
        
        # Remove any remaining NaN values
        raw_display = raw_display.dropna()
        DK_seed = raw_display.to_numpy()

        return DK_seed

@st.cache_data(ttl = 60)
def init_FD_seed_frames(load_size):  
        
        collection = db['FD_NBA_name_map']
        cursor = collection.find()
        raw_data = pd.DataFrame(list(cursor))
        names_dict = dict(zip(raw_data['key'], raw_data['value']))

        # Get the valid players from the Range of Outcomes collection
        collection = db["Player_Range_Of_Outcomes"]
        cursor = collection.find({"site": "Fanduel", "slate": "Main Slate"})
        valid_players = set(pd.DataFrame(list(cursor))['Player'].unique())
    
        collection = db["FD_NBA_seed_frame"] 
        cursor = collection.find().limit(load_size)
    
        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']]
        dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1']
        # Map names
        raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
        
        # Validate lineups against valid players
        raw_display = validate_lineup_players(raw_display, valid_players, dict_columns)
        
        # Remove any remaining NaN values
        raw_display = raw_display.dropna()
        FD_seed = raw_display.to_numpy()

        return FD_seed

@st.cache_data(ttl = 60)
def init_FD_secondary_seed_frames(load_size):  
        
        collection = db['FD_NBA_Secondary_name_map']
        cursor = collection.find()
        raw_data = pd.DataFrame(list(cursor))
        names_dict = dict(zip(raw_data['key'], raw_data['value']))

        # Get the valid players from the Range of Outcomes collection
        collection = db["Player_Range_Of_Outcomes"]
        cursor = collection.find({"site": "Fanduel", "slate": "Secondary Slate"})
        valid_players = set(pd.DataFrame(list(cursor))['Player'].unique())
    
        collection = db["FD_NBA_Secondary_seed_frame"] 
        cursor = collection.find().limit(load_size)
    
        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']]
        dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1']
        # Map names
        raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
        
        # Validate lineups against valid players
        raw_display = validate_lineup_players(raw_display, valid_players, dict_columns)
        
        # Remove any remaining NaN values
        raw_display = raw_display.dropna()
        FD_seed = raw_display.to_numpy()

        return FD_seed

@st.cache_resource(ttl = 60)
def init_baselines():
    collection = db["Player_Range_Of_Outcomes"] 
    cursor = collection.find()

    load_display = pd.DataFrame(list(cursor))

    load_display.replace('', np.nan, inplace=True)
    load_display.rename(columns={"Fantasy": "Median", 'Name': 'Player', 'player_ID': 'player_id'}, 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 validate_lineup_players(df, valid_players, player_columns):
    """
    Validates that all players in specified columns exist in valid_players set
    
    Args:
        df: DataFrame containing lineups
        valid_players: Set of valid player names
        player_columns: List of columns containing player names
    
    Returns:
        DataFrame with only valid lineups
    """
    valid_rows = df[player_columns].apply(lambda x: x.isin(valid_players)).all(axis=1)
    return df[valid_rows]

@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, Contest_Size):
    SimVar = 1
    Sim_Winners = []
    
    # 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 = seed_frame[np.random.choice(seed_frame.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_raw, fd_raw, dk_secondary, fd_secondary = init_baselines()
dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))

tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
            
with tab1:
    with st.expander("Info and Filters"):
        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(10000)
                FD_seed = init_FD_seed_frames(10000)
                DK_secondary = init_DK_secondary_seed_frames(10000)
                FD_secondary = init_FD_secondary_seed_frames(10000)
                dk_raw, fd_raw, dk_secondary, fd_secondary = init_baselines()
                dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
                fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))

        sim_slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='sim_slate_var1')
        sim_site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='sim_site_var1')
            
        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, min_value=100, max_value=100000, step=50)
        strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'))
        if strength_var1 == 'Not Very':
            sharp_split = 5000000
        elif strength_var1 == 'Below Average':
            sharp_split = 2500000
        elif strength_var1 == 'Average':
            sharp_split = 100000
        elif strength_var1 == 'Above Average':
            sharp_split = 50000
        elif strength_var1 == 'Very':
            sharp_split = 10000

    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, 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':
                if sim_slate_var1 == 'Main Slate':
                    st.session_state.working_seed = init_DK_seed_frames(sharp_split)
                    dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
                    raw_baselines = dk_raw
                    column_names = dk_columns
                elif sim_slate_var1 == 'Secondary Slate':
                    st.session_state.working_seed = init_DK_secondary_seed_frames(sharp_split)
                    dk_id_dict = dict(zip(dk_secondary.Player, dk_secondary.player_id))
                    raw_baselines = dk_secondary
                    column_names = dk_columns

            elif sim_site_var1 == 'Fanduel':
                if sim_slate_var1 == 'Main Slate':
                    st.session_state.working_seed = init_FD_seed_frames(sharp_split)
                    fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
                    raw_baselines = fd_raw
                    column_names = fd_columns
                elif sim_slate_var1 == 'Secondary Slate':
                    st.session_state.working_seed = init_FD_secondary_seed_frames(sharp_split)
                    fd_id_dict = dict(zip(fd_secondary.Player, fd_secondary.player_id))
                    raw_baselines = fd_secondary
                    column_names = fd_columns

            st.session_state.maps_dict = {
                    'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
                    'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
                    '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, 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'
                    )

with tab2:
    with st.expander("Info and Filters"):
        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(10000)
                FD_seed = init_FD_seed_frames(10000)
                DK_secondary = init_DK_secondary_seed_frames(10000)
                FD_secondary = init_FD_secondary_seed_frames(10000)
                dk_raw, fd_raw, dk_secondary, fd_secondary = init_baselines()
                dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))
                fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))
                
        slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate'), key='slate_var1')
        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
        sharp_split_var = st.number_input("How many lineups do you want?", value=10000, max_value=500000, min_value=10000, step=10000)
        lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=500, value=10, step=1)

        if site_var1 == 'Draftkings':
            
            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()
                    
            raw_baselines = dk_raw
            column_names = dk_columns
            
        elif site_var1 == 'Fanduel':
            
            player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
            if player_var1 == 'Specific Players':
                    player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique())
            elif player_var1 == 'Full Slate':
                    player_var2 = fd_raw.Player.values.tolist()
            
            raw_baselines = fd_raw
            column_names = fd_columns

        if st.button("Prepare data export", key='data_export'):
            if site_var1 == 'Draftkings':
                if 'working_seed' in st.session_state:
                    st.session_state.working_seed = st.session_state.working_seed
                    if player_var1 == 'Specific Players':
                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
                    st.session_state.data_export_display = st.session_state.working_seed[0:lineup_num_var]
                    export_column_var = 8
                elif 'working_seed' not in st.session_state:
                    if slate_var1 == 'Main Slate':
                        st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
                        dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))

                        raw_baselines = dk_raw
                        column_names = dk_columns
                    elif slate_var1 == 'Secondary Slate':
                        st.session_state.working_seed = init_DK_secondary_seed_frames(sharp_split_var)
                        dk_id_dict = dict(zip(dk_secondary.Player, dk_secondary.player_id))

                        raw_baselines = dk_secondary
                        column_names = dk_columns
                
                    if player_var1 == 'Specific Players':
                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
                    st.session_state.data_export_display = st.session_state.working_seed[0:lineup_num_var]
                    export_column_var = 8
                data_export = st.session_state.data_export_display.copy()
                for col in range(export_column_var):
                        data_export[:, col] = np.array([dk_id_dict.get(x, x) for x in data_export[:, col]])
                
            elif site_var1 == 'Fanduel':
                if 'working_seed' in st.session_state:
                    st.session_state.working_seed = st.session_state.working_seed
                    if player_var1 == 'Specific Players':
                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
                    st.session_state.data_export_display = st.session_state.working_seed[0:lineup_num_var]
                    export_column_var = 9
                elif 'working_seed' not in st.session_state:
                    if slate_var1 == 'Main Slate':
                        st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
                        fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))

                        raw_baselines = fd_raw
                        column_names = fd_columns
                    elif slate_var1 == 'Secondary Slate':
                        st.session_state.working_seed = init_FD_secondary_seed_frames(sharp_split_var)
                        fd_id_dict = dict(zip(fd_secondary.Player, fd_secondary.player_id))
                    
                        raw_baselines = fd_secondary
                        column_names = fd_columns

                    if player_var1 == 'Specific Players':
                        st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
                    st.session_state.data_export_display = st.session_state.working_seed[0:lineup_num_var]
                    export_column_var = 9
                data_export = st.session_state.data_export_display.copy()
                for col in range(export_column_var):
                        data_export[:, col] = np.array([fd_id_dict.get(x, x) for x in fd_id_dict[:, col]])
            st.download_button(
                label="Export optimals set",
                data=convert_df(data_export),
                file_name='NBA_optimals_export.csv',
                mime='text/csv',
            )
            
    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)]
                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
            elif 'working_seed' not in st.session_state:
                if slate_var1 == 'Main Slate':
                    st.session_state.working_seed = init_DK_seed_frames(sharp_split_var)
                    dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_id))

                    raw_baselines = dk_raw
                    column_names = dk_columns
                elif slate_var1 == 'Secondary Slate':
                    st.session_state.working_seed = init_DK_secondary_seed_frames(sharp_split_var)
                    dk_id_dict = dict(zip(dk_secondary.Player, dk_secondary.player_id))

                    raw_baselines = dk_secondary
                    column_names = dk_columns
            
                if player_var1 == 'Specific Players':
                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
            
        elif site_var1 == 'Fanduel':
            if 'working_seed' in st.session_state:
                st.session_state.working_seed = st.session_state.working_seed
                if player_var1 == 'Specific Players':
                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
            elif 'working_seed' not in st.session_state:
                if slate_var1 == 'Main Slate':
                    st.session_state.working_seed = init_FD_seed_frames(sharp_split_var)
                    fd_id_dict = dict(zip(fd_raw.Player, fd_raw.player_id))

                    raw_baselines = fd_raw
                    column_names = fd_columns
                elif slate_var1 == 'Secondary Slate':
                    st.session_state.working_seed = init_FD_secondary_seed_frames(sharp_split_var)
                    fd_id_dict = dict(zip(fd_secondary.Player, fd_secondary.player_id))
                
                    raw_baselines = fd_secondary
                    column_names = fd_columns

                if player_var1 == 'Specific Players':
                    st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
                st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
            
        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)