import streamlit as st
import time

# Numpy
from numpy import nan as np_nan
from numpy import inf as np_inf

# Pandas
from pandas import DataFrame
from pandas import concat as pd_concat
from pandas import options as pd_options
from pandas import errors as pd_errors
from pandas import options as poptions
from pandas import set_option

# Time
from time import sleep as time_sleep
from datetime import datetime, date
from pytz import timezone as pytz_timezone

# Database and configuration - imported globally
from database import *

# Streamlit UI Configuration
st.set_page_config(
    page_title="Paydirt Model Updates",
    page_icon="🏆",
    layout="wide"
)

# Custom CSS for consistent styling
st.markdown("""
<style>
    /* Tab styling */
    .stElementContainer [data-baseweb="button-group"] {
        gap: 2.000rem;
        padding: 4px;
    }
    .stElementContainer [kind="segmented_control"] {
        height: 2.000rem;
        white-space: pre-wrap;
        background-color: #DAA520;
        color: white;
        border-radius: 20px;
        gap: 1px;
        padding: 10px 20px;
        font-weight: bold;
        transition: all 0.3s ease;
    }
    .stElementContainer [kind="segmented_controlActive"] {
        height: 3.000rem;
        background-color: #DAA520;
        border: 3px solid #FFD700;
        border-radius: 10px;
        color: black;
    }
    .stElementContainer [kind="segmented_control"]:hover {
        background-color: #FFD700;
        cursor: pointer;
    }
    div[data-baseweb="select"] > div {
        background-color: #DAA520;
        color: white;
    }
</style>""", unsafe_allow_html=True)

# Header
st.title("🏆 Paydirt Model Updates")
st.markdown("### Update models and generate seed frames")
st.markdown("---")

# Sport Selection
sport_icons = {
    "NHL": "🏒",
    "NFL": "🏈",
    "NBA": "🏀",
    "MLB": "⚾",
    "PGA": "⛳",
    "MMA": "🥊",
    "NASCAR": "🏎",
}

selected_tab = st.segmented_control(
    "Select Tab",
    options=["NHL Updates", "NBA Updates", 'MLB Updates', 'NFL Updates', 'PGA Updates', 'MMA Updates', 'NASCAR Updates'],
    selection_mode='single',
    default='NHL Updates',
    width='stretch',
    label_visibility='collapsed',
    key='tab_selector'
)

# ============================================================================
# NHL UPDATES TAB
# ============================================================================
if selected_tab == "NHL Updates":
    from sports.nhl_functions import *
    
    st.header("🏒 NHL Model Updates")
    
    if st.button("🔄 Run NHL Updates", type="primary", use_container_width=True):
        with st.spinner("Updating NHL models..."):
            st.write("Starting prop betting table generation...")
            upload_betting_data(nhl_db)
            try:
                discord.post(content="NHL Prop Betting Table refreshed")
            except:
                pass

            st.write("Starting DraftKings player level basic outcomes generation...")
            roo_file, own_dicts = build_dk_player_level_basic_outcomes(slate_info, dk_player_hold, fd_player_hold, nhl_db)

            if roo_file is not None and len(roo_file) > 0:
                st.write("NHL Draftkings Player Level ROO refreshed")
                st.table(roo_file.head(10))
            else:
                st.error("NHL Draftkings Player Level ROO generation failed")
                st.stop()

            st.write("Starting DraftKings stack matrix basic outcomes generation...")
            dk_stacks_outcomes = build_dk_stack_matrix_basic_outcomes(slate_info, dk_stacks_hold, own_dicts)

            st.write("Starting Fanduel stack matrix basic outcomes generation...")
            fd_stacks_outcomes = build_fd_stack_matrix_basic_outcomes(slate_info, fd_stacks_hold)

            st.write("Starting DraftKings pp stack matrix basic outcomes generation...")
            dk_pp_outcomes = build_dk_pp_stack_matrix_basic_outcomes(slate_info, dk_pp_stacks_hold, own_dicts)

            st.write("Starting Fanduel pp stack matrix basic outcomes generation...")
            fd_pp_outcomes = build_fd_pp_stack_matrix_basic_outcomes(slate_info, fd_pp_stacks_hold)

            final_stacks_Proj = pd_concat([dk_stacks_outcomes, fd_stacks_outcomes])
            final_stacks_Proj.replace([np_nan, np_inf, -np_inf], 0, inplace=True)

            sh = gc.open_by_url(NHL_Master_hold)
            worksheet = sh.worksheet('Player_Lines_ROO')
            worksheet.batch_clear(['A:Z'])
            worksheet.update([final_stacks_Proj.columns.values.tolist()] + final_stacks_Proj.values.tolist())

            collection = nhl_db['Player_Lines_ROO']
            final_stacks_Proj.reset_index(inplace=True)
            chunk_size = 100000
            collection.drop()
            for i in range(0, len(final_stacks_Proj), chunk_size):
                for _ in range(5):
                    try:
                        df_chunk = final_stacks_Proj.iloc[i:i + chunk_size]
                        collection.insert_many(df_chunk.to_dict('records'), ordered=False)
                        break
                    except Exception as e:
                        st.write(f"Retry due to error: {e}")
                        time_sleep(1)

            st.write("NHL Stack Matrix refreshed")

            final_pp_Proj = pd_concat([dk_pp_outcomes, fd_pp_outcomes])
            final_pp_Proj.replace([np_nan, np_inf, -np_inf], 0, inplace=True)

            worksheet = sh.worksheet('Player_PowerPlay_ROO')
            worksheet.batch_clear(['A:Z'])
            worksheet.update([final_pp_Proj.columns.values.tolist()] + final_pp_Proj.values.tolist())

            collection = nhl_db['Player_Powerplay_ROO']
            final_pp_Proj.reset_index(inplace=True)
            chunk_size = 100000
            collection.drop()
            for i in range(0, len(final_pp_Proj), chunk_size):
                for _ in range(5):
                    try:
                        df_chunk = final_pp_Proj.iloc[i:i + chunk_size]
                        collection.insert_many(df_chunk.to_dict('records'), ordered=False)
                        break
                    except Exception as e:
                        st.write(f"Retry due to error: {e}")
                        time_sleep(1)

            st.write("NHL Powerplay Stack Matrix refreshed")

            now = datetime.now()
            current_time = now.strftime("%H:%M:%S")
            sh = gc.open_by_url(NHL_Master_hold)
            worksheet = sh.worksheet('Timestamp')
            worksheet.batch_clear(['A:z'])
            worksheet.update_cell(1, 1, current_time)

            try:
                sh = gc.open_by_url(NHL_Master_hold)
                worksheet = sh.worksheet('Prop_Frame')
                prop_frame = DataFrame(worksheet.get_values())
            except:
                sh = gc2.open_by_url(NHL_Master_hold)
                worksheet = sh.worksheet('Prop_Frame')
                prop_frame = DataFrame(worksheet.get_values())

            prop_frame.columns = prop_frame.iloc[0]
            prop_frame = prop_frame[1:]
            prop_frame = prop_frame.reset_index(drop=True)
            prop_frame = prop_frame.replace('', np_nan)
            prop_frame = prop_frame.dropna(subset=['Salary'])

            collection = nhl_db['Player_Level_ROO']
            roo_file.reset_index(inplace=True)
            chunk_size = 100000
            collection.drop()
            for i in range(0, len(roo_file), chunk_size):
                for _ in range(5):
                    try:
                        df_chunk = roo_file.iloc[i:i + chunk_size]
                        collection.insert_many(df_chunk.to_dict('records'), ordered=False)
                        break
                    except Exception as e:
                        st.write(f"Retry due to error: {e}")
                        time_sleep(1)

            st.write("Starting DraftKings NHL seed frame generation...")
            DK_NHL_seed_frame(nhl_db, roo_file)
            st.write("NHL Draftkings Seed Frames refreshed")

            st.write("Starting Fanduel NHL seed frame generation...")
            FD_NHL_seed_frame(nhl_db, roo_file)
            st.write("NHL Fanduel Seed Frames refreshed")

        st.success("✅ NHL updates completed successfully!")
        st.balloons()

# ============================================================================
# NBA UPDATES TAB
# ============================================================================
elif selected_tab == "NBA Updates":
    from sports.nba_functions import *
    
    st.header("🏀 NBA Model Updates")
    
    if st.button("🔄 Run NBA Updates", type="primary", use_container_width=True):
        with st.spinner("Updating NBA models..."):
            x = 0
            high_end = 1

            while x < high_end:
                try:
                    sh = gc.open_by_url(NBA_Master_hold)
                    worksheet = sh.worksheet('DK_Projections')
                    dk_projects_raw = DataFrame(worksheet.get_values())
                except:
                    sh = gc2.open_by_url(NBA_Master_hold)
                    worksheet = sh.worksheet('DK_Projections')
                    dk_projects_raw = DataFrame(worksheet.get_values())

                dk_projects_raw.columns = dk_projects_raw.iloc[0]
                dk_projects_raw = dk_projects_raw[1:]
                dk_projects_raw = dk_projects_raw.reset_index(drop=True)
                dk_projects_raw = dk_projects_raw.replace('', np_nan)
                dk_projects_raw = dk_projects_raw.dropna(subset=['Salary'])

                try:
                    sh = gc.open_by_url(NBA_Master_hold)
                    worksheet = sh.worksheet('FD_Projections')
                    fd_projects_raw = DataFrame(worksheet.get_values())
                except:
                    sh = gc2.open_by_url(NBA_Master_hold)
                    worksheet = sh.worksheet('FD_Projections')
                    fd_projects_raw = DataFrame(worksheet.get_values())

                fd_projects_raw.columns = fd_projects_raw.iloc[0]
                fd_projects_raw = fd_projects_raw[1:]
                fd_projects_raw = fd_projects_raw.reset_index(drop=True)
                fd_projects_raw = fd_projects_raw.replace('', np_nan)
                fd_projects_raw = fd_projects_raw.dropna(subset=['Salary'])

                st.write("Starting DraftKings player level basic outcomes generation...")
                dk_roo_result = DK_ROO_Structure_Creation(dk_projects_raw, short_team_acro, long_team_acro, team_only_acro)
                st.write("NBA Draftkings ROO structure refreshed")

                st.write("Starting Fanduel player level basic outcomes generation...")
                fd_roo_result = FD_ROO_Structure_Creation(fd_projects_raw, short_team_acro, long_team_acro, team_only_acro)
                st.write("NBA Fanduel ROO structure refreshed")

                st.write("Starting DraftKings NBA seed frame generation...")
                DK_NBA_seed_frame(dk_roo_result, client)
                st.write("NBA Draftkings Seed Frames refreshed")

                st.write("Starting Fanduel NBA seed frame generation...")
                FD_NBA_seed_frame(fd_roo_result, client)
                st.write("NBA Fanduel Seed Frames refreshed")

                try:
                    sh = gc.open_by_url(NBA_Master_hold)
                    worksheet = sh.worksheet('DK_SD_Projections')
                    dk_sd_projections = DataFrame(worksheet.get_values())
                except:
                    sh = gc2.open_by_url(NBA_Master_hold)
                    worksheet = sh.worksheet('DK_SD_Projections')
                    dk_sd_projections = DataFrame(worksheet.get_values())

                dk_sd_projections.columns = dk_sd_projections.iloc[0]
                dk_sd_projections = dk_sd_projections[1:]
                dk_sd_projections = dk_sd_projections.reset_index(drop=True)
                dk_sd_projections = dk_sd_projections.replace('', np_nan)
                dk_sd_projections = dk_sd_projections.dropna(subset=['Salary'])

                try:
                    sh = gc.open_by_url(NBA_Master_hold)
                    worksheet = sh.worksheet('FD_SD_Projections')
                    fd_sd_projections = DataFrame(worksheet.get_values())
                except:
                    sh = gc2.open_by_url(NBA_Master_hold)
                    worksheet = sh.worksheet('FD_SD_Projections')
                    fd_sd_projections = DataFrame(worksheet.get_values())

                fd_sd_projections.columns = fd_sd_projections.iloc[0]
                fd_sd_projections = fd_sd_projections[1:]
                fd_sd_projections = fd_sd_projections.reset_index(drop=True)
                fd_sd_projections = fd_sd_projections.replace('', np_nan)
                fd_sd_projections = fd_sd_projections.dropna(subset=['Salary'])

                dk_showdown_options = list(dk_sd_projections['slate'].unique())
                fd_showdown_options = list(fd_sd_projections['slate'].unique())

                st.write("Starting DraftKings SD ROO structure generation...")
                dk_sd_roo_result = DK_SD_ROO(dk_sd_projections, dk_showdown_options)
                st.write("NBA Draftkings SD ROO structure refreshed")

                st.write("Starting Fanduel SD ROO structure generation...")
                fd_sd_roo_result = FD_SD_ROO(fd_sd_projections, fd_showdown_options)
                st.write("NBA Fanduel SD ROO structure refreshed")

                sd_roo_final = pd_concat([dk_sd_roo_result, fd_sd_roo_result])

                st.write("Starting DraftKings SD seed frame generation...")
                DK_NBA_SD_seed_frame(dk_showdown_options, dk_sd_projections)
                st.write("NBA Draftkings SD Seed Frames refreshed")

                st.write("Starting Fanduel SD seed frame generation...")
                FD_NBA_SD_seed_frame(fd_showdown_options, fd_sd_projections)
                st.write("NBA Fanduel SD Seed Frames refreshed")

                upload_sd_dfs_data(client, sd_roo_final)
                st.write("NBA SD DFS database refreshed")

                x += 1

                if high_end > 1:
                    time_sleep(600)

        st.success("✅ NBA updates completed successfully!")
        st.balloons()

# ============================================================================
# NFL UPDATES TAB
# ============================================================================
elif selected_tab == "NFL Updates":
    from sports.nfl_functions import *
    
    st.header("🏈 NFL Model Updates")
    
    if st.button("🔄 Run NFL Updates", type="primary", use_container_width=True):
        with st.spinner("Updating NFL models..."):
            try:
                sh = gc.open_by_url(NFL_Master_hold)
                worksheet = sh.worksheet('DK_Projections')
                dk_projects_raw = DataFrame(worksheet.get_values())
            except:
                sh = gc2.open_by_url(NFL_Master_hold)
                worksheet = sh.worksheet('DK_Projections')
                dk_projects_raw = DataFrame(worksheet.get_values())

            dk_projects_raw.columns = dk_projects_raw.iloc[0]
            dk_projects_raw = dk_projects_raw[1:]
            dk_projects_raw = dk_projects_raw.reset_index(drop=True)
            dk_projects_raw = dk_projects_raw.replace('', np_nan)
            dk_projects_raw = dk_projects_raw.dropna(subset=['Salary'])

            try:
                sh = gc.open_by_url(NFL_Master_hold)
                worksheet = sh.worksheet('FD_Projections')
                fd_projects_raw = DataFrame(worksheet.get_values())
            except:
                sh = gc2.open_by_url(NFL_Master_hold)
                worksheet = sh.worksheet('FD_Projections')
                fd_projects_raw = DataFrame(worksheet.get_values())

            fd_projects_raw.columns = fd_projects_raw.iloc[0]
            fd_projects_raw = fd_projects_raw[1:]
            fd_projects_raw = fd_projects_raw.reset_index(drop=True)
            fd_projects_raw = fd_projects_raw.replace('', np_nan)
            fd_projects_raw = fd_projects_raw.dropna(subset=['Salary'])

            st.write("Starting DraftKings team level stacks generation...")
            dk_player_hold = DK_Team_Level_Stacks(dk_projects_raw, team_only_acro, long_team_acro, short_team_acro, client)
            st.write("NFL Draftkings Team Level Stacks refreshed")

            st.write("Starting Fanduel team level stacks generation...")
            fd_player_hold = FD_Team_Level_Stacks(fd_projects_raw, team_only_acro, long_team_acro, short_team_acro, client)
            st.write("NFL Fanduel Team Level Stacks refreshed")

            st.write("Starting DraftKings ROO structure generation...")
            dk_roo_result = DK_ROO_Structure_Creation(dk_player_hold, short_team_acro, long_team_acro, team_only_acro)
            st.write("NFL Draftkings ROO structure refreshed")

            st.write("Starting Fanduel ROO structure generation...")
            fd_roo_result = FD_ROO_Structure_Creation(fd_player_hold, short_team_acro, long_team_acro, team_only_acro)
            st.write("NFL Fanduel ROO structure refreshed")

            st.write("Starting DraftKings seed frame generation...")
            DK_seed_frame(dk_roo_result, seed_team_acro, short_team_acro, client)
            st.write("NFL Draftkings Seed Frames refreshed")

            st.write("Starting Fanduel seed frame generation...")
            FD_seed_frame(fd_roo_result, seed_team_acro, short_team_acro, client)
            st.write("NFL Fanduel Seed Frames refreshed")

            upload_betting_data(client)
            st.write("NFL Player Baselines refreshed")

            try:
                sh = gc.open_by_url(NFL_Master_hold)
                worksheet = sh.worksheet('DK_SD_Projections')
                dk_sd_projections = DataFrame(worksheet.get_values())
            except:
                sh = gc2.open_by_url(NFL_Master_hold)
                worksheet = sh.worksheet('DK_SD_Projections')
                dk_sd_projections = DataFrame(worksheet.get_values())

            dk_sd_projections.columns = dk_sd_projections.iloc[0]
            dk_sd_projections = dk_sd_projections[1:]
            dk_sd_projections = dk_sd_projections.reset_index(drop=True)
            dk_sd_projections = dk_sd_projections.replace('', np_nan)
            dk_sd_projections = dk_sd_projections.dropna(subset=['Salary'])

            try:
                sh = gc.open_by_url(NFL_Master_hold)
                worksheet = sh.worksheet('FD_SD_Projections')
                fd_sd_projections = DataFrame(worksheet.get_values())
            except:
                sh = gc2.open_by_url(NFL_Master_hold)
                worksheet = sh.worksheet('FD_SD_Projections')
                fd_sd_projections = DataFrame(worksheet.get_values())

            fd_sd_projections.columns = fd_sd_projections.iloc[0]
            fd_sd_projections = fd_sd_projections[1:]
            fd_sd_projections = fd_sd_projections.reset_index(drop=True)
            fd_sd_projections = fd_sd_projections.replace('', np_nan)
            fd_sd_projections = fd_sd_projections.dropna(subset=['Salary'])

            dk_showdown_options = list(dk_sd_projections['slate'].unique())
            fd_showdown_options = list(fd_sd_projections['slate'].unique())

            st.write("Starting DraftKings SD ROO structure generation...")
            dk_sd_roo_result = DK_SD_ROO(dk_sd_projections, team_only_acro, long_team_acro, dk_showdown_options)
            st.write("NFL Draftkings SD ROO structure refreshed")

            st.write("Starting Fanduel SD ROO structure generation...")
            fd_sd_roo_result = FD_SD_ROO(fd_sd_projections, team_only_acro, long_team_acro, fd_showdown_options)
            st.write("NFL Fanduel SD ROO structure refreshed")

            for slate in dk_showdown_options:
                st.write(f"Processing DraftKings Showdown slate: {slate}")
                DK_SD_seed_frame(dk_sd_roo_result, team_only_acro, long_team_acro, client, dk_showdown_options, dk_sd_projections)

            for slate in fd_showdown_options:
                st.write(f"Processing Fanduel Showdown slate: {slate}")
                FD_SD_seed_frame(fd_sd_roo_result, team_only_acro, long_team_acro, client, fd_showdown_options, fd_sd_projections)

        st.success("✅ NFL updates completed successfully!")
        st.balloons()

# ============================================================================
# PGA UPDATES TAB
# ============================================================================
elif selected_tab == "PGA Updates":
    from sports.pga_functions import *
    
    st.header("⛳ PGA Model Updates")
    
    # Column definitions
    str_columns = ['Name', 'Roster Position', 'Site', 'Type']
    float_columns = ['Cut%', 'Win Odds', 'T5 Odds', 'T10 Odds', 'T20 Odds', 'CeilingVar', 'Fantasy', 'Own%']
    int_columns = ['Salary', 'ID']
    stat_columns = float_columns + int_columns
    
    if st.button("🔄 Run PGA Updates", type="primary", use_container_width=True):
        with st.spinner("Updating PGA models..."):
            st.write("Loading PGA data from Google Sheets...")
            try:
                sh = gc.open_by_url(PGA_Master_hold)
                worksheet = sh.worksheet('Export')
                projects_raw = DataFrame(worksheet.get_values())
            except:
                sh = gc2.open_by_url(PGA_Master_hold)
                worksheet = sh.worksheet('Export')
                projects_raw = DataFrame(worksheet.get_values())
            
            projects_raw.columns = projects_raw.iloc[0]
            projects_raw = projects_raw[1:]
            projects_raw = projects_raw.reset_index(drop=True)
            projects_raw = projects_raw.replace('', np_nan)
            projects_raw = projects_raw.dropna(subset=['Salary'])
            projects_raw['Roster Position'] = 'FLEX'
            
            for col in str_columns:
                projects_raw[col] = projects_raw[col].astype(str)
            for col in float_columns:
                projects_raw[col] = projects_raw[col].str.replace('%', '').astype(float) / 100
            for col in int_columns:
                projects_raw[col] = projects_raw[col].str.replace('%', '').astype(int)
            
            projects_raw = projects_raw[projects_raw['Fantasy'] > 0]
            projects_raw['Fantasy'] = projects_raw['Fantasy'] * 100
            projects_raw['Own%'] = projects_raw['Own%'] * 100
            projects_raw['Cut%'] = projects_raw['Cut%'] * 100
            
            classic_proj = projects_raw[projects_raw['Type'] == 'Classic']
            showdown_proj = projects_raw[projects_raw['Type'] == 'Showdown']
            
            classic_stat_dicts = {}
            showdown_stat_dicts = {}
            
            for col in stat_columns:
                classic_stat_dicts[col] = dict(zip(classic_proj['Name'], classic_proj[col]))
                showdown_stat_dicts[col] = dict(zip(showdown_proj['Name'], showdown_proj[col]))
            
            classic_working_proj = classic_proj[['Name', 'Roster Position', 'Site', 'Type', 'Salary', 'ID', 'Cut%', 'Win Odds', 'T5 Odds', 'T10 Odds', 'T20 Odds', 'CeilingVar', 'Fantasy', 'Own%']]
            classic_working_proj = classic_working_proj.rename(columns={"Name": "Player", "Roster Position": "Position", "Fantasy": "Median", "Own%": "Own", "Cut%": "Cut_Odds", "Win Odds": "Win_Odds", "T5 Odds": "T5_Odds", "T10 Odds": "T10_Odds", "T20 Odds": "T20_Odds", "CeilingVar": "ceiling_avg"})
            showdown_working_proj = showdown_proj[['Name', 'Roster Position', 'Site', 'Type', 'Salary', 'ID', 'Cut%', 'Win Odds', 'T5 Odds', 'T10 Odds', 'T20 Odds', 'CeilingVar', 'Fantasy', 'Own%']]
            showdown_working_proj = showdown_working_proj.rename(columns={"Name": "Player", "Roster Position": "Position", "Fantasy": "Median", "Own%": "Own", "Cut%": "Cut_Odds", "Win Odds": "Win_Odds", "T5 Odds": "T5_Odds", "T10 Odds": "T10_Odds", "T20 Odds": "T20_Odds", "CeilingVar": "ceiling_avg"})
            
            st.write("PGA data loaded successfully!")
        
            st.write("Generating Classic PGA ROO structure...")
            classic_roo = player_level_classic_roo(classic_working_proj, classic_stat_dicts, client)
            st.write("PGA Draftkings Classic ROO structure refreshed")
        
            st.write("Generating Classic PGA seed frames...")
            DK_seed_frame(classic_roo, classic_stat_dicts, client, "Classic")
            st.write("PGA Draftkings Classic Seed Frames refreshed")
        
            st.write("Generating Showdown PGA ROO structure...")
            showdown_roo = player_level_showdown_roo(showdown_working_proj, showdown_stat_dicts, client)
            st.write("PGA Draftkings Showdown ROO structure refreshed")
        
            st.write("Generating Showdown PGA seed frames...")
            DK_seed_frame(showdown_roo, showdown_stat_dicts, client, "Showdown")
            st.write("PGA Draftkings Showdown Seed Frames refreshed")
        
        st.success("✅ PGA updates completed successfully!")
        st.balloons()

# ============================================================================
# MLB UPDATES TAB
# ============================================================================
elif selected_tab == "MLB Updates":
    from sports.mlb_functions import *
    
    st.header("⚾ MLB Model Updates")
    st.info("MLB updates coming soon!")
    st.write("MLB functionality will be added in a future update.")

# ============================================================================
# MMA UPDATES TAB
# ============================================================================
elif selected_tab == "MMA Updates":
    from sports.mma_functions import *
    
    st.header("🥊 MMA Model Updates")
    
    # Column definitions
    str_columns = ['Fighter1', 'Opp']
    float_columns = ['Avg Win', 'Avg Loss', 'Avg Draw', 'ToWin', 'To Win By KO/TKO/DQ', 'To Win By Submission', 'To Win By Decision', 'Draw', 'Round 1', 'Round 2', 'Round 3', 'Round 4', 'Round 5',
                    'By Decision', 'Average', 'OddsProj', 'SalaryProj', 'DeviationVar', 'FloorProj', 'AggProj', 'CeilingProj', 'OwnRaw', 'OwnAdj', 'Own']
    int_columns = ['Salary', 'player_id']
    
    if st.button("🔄 Run MMA Updates", type="primary", use_container_width=True):
        with st.spinner("Updating MMA models..."):
            st.write("Loading MMA data from Google Sheets...")
            try:
                sh = gc.open_by_url(MMA_Master_hold)
                worksheet = sh.worksheet('Export')
                projects_raw = DataFrame(worksheet.get_values())
            except:
                sh = gc2.open_by_url(MMA_Master_hold)
                worksheet = sh.worksheet('Export')
                projects_raw = DataFrame(worksheet.get_values())
            
            projects_raw.columns = projects_raw.iloc[0]
            projects_raw = projects_raw[1:]
            projects_raw = projects_raw.reset_index(drop=True)
            projects_raw = projects_raw.replace('', np_nan)
            projects_raw = projects_raw.dropna(subset=['Salary'])
            projects_raw['Position'] = 'FLEX'
            
            st.write("MMA data loaded successfully!")
            st.table(projects_raw.head(10))
            
            for col in str_columns:
                projects_raw[col] = projects_raw[col].astype(str)
            for col in float_columns:
                projects_raw[col] = projects_raw[col].astype(float)
            for col in int_columns:
                projects_raw[col] = projects_raw[col].astype(int)
            
            fighter_dicts = {}
            
            for col in float_columns:
                fighter_dicts[col] = dict(zip(projects_raw['Fighter1'], projects_raw[col]))
            fighter_dicts['Opp'] = dict(zip(projects_raw['Fighter1'], projects_raw['Opp']))
            fighter_dicts['player_id'] = dict(zip(projects_raw['Fighter1'], projects_raw['player_id']))
            
            working_proj = projects_raw[['Fighter1', 'Position', 'Opp', 'Salary', 'FloorProj', 'AggProj', 'CeilingProj', 'Own']]
            working_proj = working_proj.rename(columns={"Fighter1": "Player", "FloorProj": "Floor", "AggProj": "Median", "CeilingProj": "Ceiling", "Own": "Own"})
            
            st.write("Generating MMA ROO structure...")
            Overall_Proj = player_level_roo(working_proj, fighter_dicts, client)
            st.write("MMA Draftkings ROO structure refreshed")
            st.table(Overall_Proj.head(10))
            
            try:
                discord.post(content="MMA Draftkings ROO structure refreshed")
            except:
                pass
            
            # Upload to database
            collection = mma_db['Player_Level_ROO']
            Overall_Proj_copy = Overall_Proj.reset_index(drop=True)
            chunk_size = 100000
            collection.drop()
            for i in range(0, len(Overall_Proj_copy), chunk_size):
                for _ in range(5):
                    try:
                        df_chunk = Overall_Proj_copy.iloc[i:i + chunk_size]
                        collection.insert_many(df_chunk.to_dict('records'), ordered=False)
                        break
                    except Exception as e:
                        st.write(f"Retry due to error: {e}")
                        time_sleep(1)
            
            st.write("Generating MMA seed frames...")
            DK_seed_frame(Overall_Proj, fighter_dicts, client)
            st.write("MMA Draftkings Seed Frames refreshed")
        
        st.success("✅ MMA updates completed successfully!")
        st.balloons()

# ============================================================================
# NASCAR UPDATES TAB
# ============================================================================
elif selected_tab == "NASCAR Updates":
    from sports.nascar_functions import *
    
    st.header("🏎 NASCAR Model Updates")
    
    # Column definitions
    str_columns = ['Name', 'Roster Position', 'Site', 'Type']
    float_columns = ['Grid', 'Laps Led', 'Fast Laps', 'FloorVar', 'CeilingVar', 'Fantasy', 'Own%']
    int_columns = ['Salary', 'ID']
    stat_columns = float_columns + int_columns
    
    if st.button("🔄 Run NASCAR Updates", type="primary", use_container_width=True):
        with st.spinner("Updating NASCAR models..."):
            st.write("Loading NASCAR data from Google Sheets...")
            try:
                sh = gc.open_by_url(NASCAR_Master_Hold)
                worksheet = sh.worksheet('Export')
                # worksheet = sh.worksheet('Test_DK_Projections')
                projects_raw = DataFrame(worksheet.get_values())
            except:
                sh = gc2.open_by_url(NASCAR_Master_Hold)
                worksheet = sh.worksheet('Export')
                # worksheet = sh.worksheet('Test_DK_Projections')
                projects_raw = DataFrame(worksheet.get_values())
            projects_raw.columns = projects_raw.iloc[0]
            projects_raw = projects_raw[1:]
            projects_raw = projects_raw.reset_index(drop=True)
            projects_raw = projects_raw.replace('', np_nan)
            projects_raw = projects_raw.dropna(subset=['Salary'])

            for col in str_columns:
                projects_raw[col] = projects_raw[col].astype(str)
            for col in float_columns:
                projects_raw[col] = projects_raw[col].str.replace('%', '').astype(float)
            for col in int_columns:
                projects_raw[col] = projects_raw[col].str.replace('%', '').astype(int)

            projects_raw = projects_raw[projects_raw['Fantasy'] > 0]
            projects_raw['Fantasy'] = projects_raw['Fantasy']
            projects_raw['Own%'] = projects_raw['Own%']

            classic_proj = projects_raw[projects_raw['Type'] == 'Classic']

            classic_stat_dicts = {}

            for col in stat_columns:
                classic_stat_dicts[col] = dict(zip(classic_proj['Name'], classic_proj[col]))

            try:
                sh = gc.open_by_url(NASCAR_Master_Hold)
                worksheet = sh.worksheet('Export_2')
                # worksheet = sh.worksheet('Test_DK_Projections')
                projects_raw = DataFrame(worksheet.get_values())
            except:
                sh = gc2.open_by_url(NASCAR_Master_Hold)
                worksheet = sh.worksheet('Export_2')
                # worksheet = sh.worksheet('Test_DK_Projections')
                projects_raw = DataFrame(worksheet.get_values())
            projects_raw.columns = projects_raw.iloc[0]
            projects_raw = projects_raw[1:]
            projects_raw = projects_raw.reset_index(drop=True)
            projects_raw = projects_raw.replace('', np_nan)
            projects_raw = projects_raw.dropna(subset=['Salary'])

            for col in str_columns:
                projects_raw[col] = projects_raw[col].astype(str)
            for col in float_columns:
                projects_raw[col] = projects_raw[col].str.replace('%', '').astype(float)
            for col in int_columns:
                projects_raw[col] = projects_raw[col].str.replace('%', '').astype(int)

            projects_raw = projects_raw[projects_raw['Fantasy'] > 0]
            projects_raw['Fantasy'] = projects_raw['Fantasy']
            projects_raw['Own%'] = projects_raw['Own%']

            classic_proj_2 = projects_raw[projects_raw['Type'] == 'Classic']

            classic_stat_dicts_2 = {}

            for col in stat_columns:
                classic_stat_dicts_2[col] = dict(zip(classic_proj_2['Name'], classic_proj_2[col]))

            classic_working_proj = classic_proj[['Name', 'Roster Position', 'Site', 'Type', 'Salary', 'ID', 'Grid', 'Laps Led', 'Fast Laps', 'FloorVar', 'CeilingVar', 'Fantasy', 'Own%']]
            classic_working_proj_2 = classic_proj_2[['Name', 'Roster Position', 'Site', 'Type', 'Salary', 'ID', 'Grid', 'Laps Led', 'Fast Laps', 'FloorVar', 'CeilingVar', 'Fantasy', 'Own%']]
            classic_working_proj = classic_working_proj.rename(columns={"Name": "Player", "Roster Position": "Position", "Fantasy": "Median", "Own%": "Own", "FloorVar": "floor_avg", "CeilingVar": "ceiling_avg"})
            classic_working_proj_2 = classic_working_proj_2.rename(columns={"Name": "Player", "Roster Position": "Position", "Fantasy": "Median", "Own%": "Own", "FloorVar": "floor_avg", "CeilingVar": "ceiling_avg"})
            
            st.write("Generating NASCAR ROO structure...")
            Overall_Proj = player_level_classic_roo(classic_working_proj, classic_stat_dicts, classic_working_proj_2, classic_stat_dicts_2, client, "Main Slate")
            st.write("NASCAR Draftkings ROO structure refreshed")
            st.table(Overall_Proj.head(10))
            
            try:
                discord.post(content="NASCAR Draftkings ROO structure refreshed")
            except:
                pass
            
            # Upload to database
            collection = nascar_db['Player_Level_ROO']
            Overall_Proj_copy = Overall_Proj.reset_index(drop=True)
            chunk_size = 100000
            collection.drop()
            for i in range(0, len(Overall_Proj_copy), chunk_size):
                for _ in range(5):
                    try:
                        df_chunk = Overall_Proj_copy.iloc[i:i + chunk_size]
                        collection.insert_many(df_chunk.to_dict('records'), ordered=False)
                        break
                    except Exception as e:
                        st.write(f"Retry due to error: {e}")
                        time_sleep(1)
            
            classic_roo = Overall_Proj[Overall_Proj['slate'] == 'Main Slate']
            classic_roo_2 = Overall_Proj[Overall_Proj['slate'] == 'Secondary']
            
            st.write("Generating NASCAR seed frames for Main Slate...")
            DK_seed_frame(classic_roo, classic_stat_dicts, client, "Classic")
            st.write("NASCAR Draftkings Seed Frames refreshed")
        
            st.write("Generating NASCAR seed frames for Secondary Slate...")
            DK_seed_frame(classic_roo_2, classic_stat_dicts_2, client, "Secondary")
            st.write("NASCAR Draftkings Seed Frames refreshed")
        
        st.success("✅ NASCAR updates completed successfully!")
        st.balloons()