import streamlit as st
st.set_page_config(layout="wide")

for name in dir():
    if not name.startswith('_'):
        del globals()[name]

import numpy as np
import pandas as pd
import streamlit as st
import os
import requests

NBA_DATA = os.getenv('NBA_DATA')

percentages_format = {'Pts% Boost': '{:.2%}', 'Reb% Boost': '{:.2%}', 'Ast% Boost': '{:.2%}', '3p% Boost': '{:.2%}',
                      'Stl Boost%': '{:.2%}', 'Blk Boost%': '{:.2%}', 'TOV Boost%': '{:.2%}', 'FPPM Boost': '{:.2%}',
                      'Team FPPM Boost': '{:.2%}'}

team_macro_format = {'FGM%': '{:.2%}', 'FG3M%': '{:.2%}', 'FTM%': '{:.2%}'}

macro_minutes_cols = ['Team', 'Games', 'MIN', 'FGA/m', 'FG3A/m', 'FTA/m', 'PTS/m', 'REB/m', 'AST/m', 'STL/m', 'BLK/m', 'Fantasy/m', 'FD_Fantasy/m']
remove_minutes_cols = ['MIN', 'FGA/m', 'FG3A/m', 'FTA/m', 'PTS/m', 'REB/m', 'AST/m', 'STL/m', 'BLK/m', 'Fantasy/m', 'FD_Fantasy/m']

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)

@st.cache_resource(ttl = 600)
def init_DEM():
    
    json_matchups = requests.get(NBA_DATA + '?sheet=DEM%20Matchups').json()
    raw_display = pd.DataFrame(json_matchups)
    raw_display = raw_display.reset_index(drop=True)
    matchups = raw_display[raw_display['Var'] != ""]
    matchups_dict = dict(zip(matchups['Team'], matchups['Opp']))
    
    master_dem_json = requests.get(NBA_DATA + '?sheet=Master_DEM_Calc').json()
    master_dem_frame = pd.DataFrame(master_dem_json)

    raw_display = master_dem_frame[master_dem_frame['Position'] == 'PG']
    raw_display = raw_display.reset_index(drop=True)
    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
    raw_display['position'] = 'Point Guard'
    pg_dem = raw_display[raw_display['Acro'] != ""]

    raw_display = master_dem_frame[master_dem_frame['Position'] == 'SG']
    raw_display = raw_display.reset_index(drop=True)
    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
    raw_display['position'] = 'Shooting Guard'
    sg_dem = raw_display[raw_display['Acro'] != ""]
    
    raw_display = master_dem_frame[master_dem_frame['Position'] == 'SF']
    raw_display = raw_display.reset_index(drop=True)
    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
    raw_display['position'] = 'Small Forward'
    sf_dem = raw_display[raw_display['Acro'] != ""]
    
    raw_display = master_dem_frame[master_dem_frame['Position'] == 'PF']
    raw_display = raw_display.reset_index(drop=True)
    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
    raw_display['position'] = 'Power Forward'
    pf_dem = raw_display[raw_display['Acro'] != ""]
    
    raw_display = master_dem_frame[master_dem_frame['Position'] == 'C']
    raw_display = raw_display.reset_index(drop=True)
    cols_to_check = ['Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost', 'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM Boost']
    raw_display.loc[:, cols_to_check] = raw_display.loc[:, cols_to_check].replace({'%': ''}, regex=True).astype(float) / 100
    raw_display = raw_display.apply(pd.to_numeric, errors='coerce').fillna(raw_display)
    raw_display['position'] = 'Center'
    c_dem = raw_display[raw_display['Acro'] != ""]
    
    overall_dem = pd.concat([pg_dem, sg_dem, sf_dem, pf_dem, c_dem])
    overall_dem = overall_dem[['Acro', 'G', 'Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost',
                               'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM', 'FPPM Boost', 'position']]
    overall_dem['Team'] = overall_dem['Acro'] + '-' + overall_dem['position']
    overall_dem['Team FPPM Boost'] = overall_dem.groupby('Acro', sort=False)['FPPM Boost'].transform('mean')
    overall_dem = overall_dem.reset_index()
    
    
    export_dem = overall_dem[['Team', 'Acro', 'G', 'Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost',
                               'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM', 'FPPM Boost', 'Team FPPM Boost', 'position']]
    

    return export_dem, matchups, matchups_dict

@st.cache_resource(ttl = 600)
def init_macro_tables():

    json_matchups = requests.get(NBA_DATA + '?sheet=Site_Info').json()
    raw_display = pd.DataFrame(json_matchups)
    raw_display = raw_display.reset_index(drop=True)
    dk_main_slate = raw_display['DK Main'].values.tolist()
    fd_main_slate = raw_display['FD Main'].values.tolist()

    team_macro_json = requests.get(NBA_DATA + '?sheet=Team_Macro').json()
    team_macro_frame = pd.DataFrame(team_macro_json)
    team_macro_frame = team_macro_frame.reset_index(drop=True)
    team_macro_frame = team_macro_frame[team_macro_frame['Team'] != ""]

    team_off_frame = team_macro_frame[team_macro_frame['Data'] == 'Off']
    team_off_frame = team_off_frame.reset_index(drop=True)
    team_off_frame = team_off_frame.drop(columns=['PLUS_MINUS', 'PF', 'Data'], axis=1)
    team_off_frame = team_off_frame.apply(pd.to_numeric, errors='coerce').fillna(team_off_frame)
    team_off_frame['PTS/m'] = team_off_frame['PTS'] / team_off_frame['MIN']
    team_off_frame['REB/m'] = team_off_frame['REB'] / team_off_frame['MIN']
    team_off_frame['AST/m'] = team_off_frame['AST'] / team_off_frame['MIN']
    team_off_frame['STL/m'] = team_off_frame['STL'] / team_off_frame['MIN']
    team_off_frame['BLK/m'] = team_off_frame['BLK'] / team_off_frame['MIN']

    team_def_frame = team_macro_frame[team_macro_frame['Data'] == 'Def']
    team_def_frame = team_def_frame.reset_index(drop=True)
    team_def_frame = team_def_frame.drop(columns=['PLUS_MINUS', 'PF', 'Data'], axis=1)
    team_def_frame = team_def_frame.apply(pd.to_numeric, errors='coerce').fillna(team_def_frame)
    team_def_frame['PTS/m'] = team_def_frame['PTS'] / team_def_frame['MIN']
    team_def_frame['REB/m'] = team_def_frame['REB'] / team_def_frame['MIN']
    team_def_frame['AST/m'] = team_def_frame['AST'] / team_def_frame['MIN']
    team_def_frame['STL/m'] = team_def_frame['STL'] / team_def_frame['MIN']
    team_def_frame['BLK/m'] = team_def_frame['BLK'] / team_def_frame['MIN']

    team_combo_json = requests.get(NBA_DATA + '?sheet=Team%20Combo%20Data').json()
    team_combo_frame = pd.DataFrame(team_combo_json)
    team_combo_frame = team_combo_frame.reset_index(drop=True)
    team_combo_frame = team_combo_frame.apply(pd.to_numeric, errors='coerce').fillna(team_combo_frame)
    try:
        team_matchup_json = requests.get(NBA_DATA + '?sheet=Team%20Matchups').json()
        team_matchup_frame = pd.DataFrame(team_matchup_json)
        team_matchup_frame = team_matchup_frame[team_matchup_frame['t_Pts'] != ""]
        team_matchup_frame = team_matchup_frame.reset_index(drop=True)
        team_matchup_frame = team_matchup_frame.apply(pd.to_numeric, errors='coerce').fillna(team_matchup_frame)
    except:
        team_matchup_frame = pd.DataFrame()

    return team_off_frame, team_def_frame, team_combo_frame, team_matchup_frame, dk_main_slate, fd_main_slate

def convert_df_to_csv(df):
    return df.to_csv().encode('utf-8')

selected_tab = st.segmented_control(
    "Select Tab",
    options=["DEM Matchups", "Team Macro Tables"],
    selection_mode='single',
    default='DEM Matchups',
    width='stretch',
    label_visibility='collapsed',
    key='tab_selector'
)

if selected_tab == 'DEM Matchups':

    overall_dem, matchups, matchups_dict = init_DEM()

    col1, col2 = st.columns([1, 9])
    with col1:
        if st.button("Reset Data", key='reset1'):
                st.cache_data.clear()
                overall_dem, matchups, matchups_dict = init_DEM()
        split_var1 = st.radio("View all teams or just this main slate's matchups?", ('Slate Matchups', 'All'), key='split_var1')
        if split_var1 == 'Slate Matchups':
            view_var1 = matchups.Opp.values.tolist()
            split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
            if split_var2 == 'Specific Teams':
                team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = view_var1, key='team_var1')
            elif split_var2 == 'All':
                team_var1 = view_var1
            split_var3 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var3')
            if split_var3 == 'Specific Positions':
                pos_var1 = st.multiselect('Which teams would you like to include in the tables?', options = overall_dem['position'].unique(), key='pos_var1')
            elif split_var3 == 'All':
                pos_var1 = overall_dem.position.values.tolist()
        if split_var1 == 'All':
            split_var2 = st.radio("Would you like to view all teams or specific ones?", ('All', 'Specific Teams'), key='split_var2')
            if split_var2 == 'Specific Teams':
                team_var1 = st.multiselect('Which teams would you like to include in the tables?', options = overall_dem['Acro'].unique(), key='team_var1')
            elif split_var2 == 'All':
                team_var1 = overall_dem.Acro.values.tolist()
            split_var3 = st.radio("Would you like to view all positions or specific ones?", ('All', 'Specific Positions'), key='split_var3')
            if split_var3 == 'Specific Positions':
                pos_var1 = st.multiselect('Which teams would you like to include in the tables?', options = overall_dem['position'].unique(), key='pos_var1')
            elif split_var3 == 'All':
                pos_var1 = overall_dem.position.values.tolist()
    with col2:
        if split_var1 == 'Slate Matchups':
            dem_display = overall_dem[overall_dem['Acro'].isin(view_var1)]
            dem_display['Team (Getting Boost)'] = dem_display['Acro'].map(matchups_dict)
            dem_display.rename(columns={"Acro": "Opp (Giving Boost)"}, inplace = True)
            dem_display = dem_display[['Team (Getting Boost)', 'Opp (Giving Boost)', 'G', 'Pts% Boost', 'Reb% Boost', 'Ast% Boost', '3p% Boost',
                                    'Stl Boost%', 'Blk Boost%', 'TOV Boost%', 'FPPM', 'FPPM Boost', 'Team FPPM Boost', 'position']]
            dem_display = dem_display[dem_display['Team (Getting Boost)'].isin(team_var1)]
            dem_display = dem_display[dem_display['position'].isin(pos_var1)]
            dem_display = dem_display.sort_values(by='FPPM Boost', ascending=False)
        elif split_var1 == 'All':
            dem_display = overall_dem[overall_dem['Acro'].isin(team_var1)]
            dem_display = dem_display[dem_display['position'].isin(pos_var1)]
            dem_display = dem_display.sort_values(by='FPPM Boost', ascending=False)
            dem_display.rename(columns={"Team": "Team (Giving Boost)"}, inplace = True)
            dem_display = dem_display.set_index('Team (Giving Boost)')
        st.dataframe(dem_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
        st.download_button(
            label="Export DEM Numbers",
            data=convert_df_to_csv(overall_dem),
            file_name='DEM_export.csv',
            mime='text/csv',
        )

if selected_tab == 'Team Macro Tables':
    team_off_frame, team_def_frame, team_combo_frame, team_matchup_frame, dk_main_slate, fd_main_slate = init_macro_tables()
    col1, col2 = st.columns([1, 9])
    with col1:
        if st.button("Reset Data", key='reset2'):
                st.cache_data.clear()
                team_off_frame, team_def_frame, team_combo_frame, team_matchup_frame, dk_main_slate, fd_main_slate = init_macro_tables()
        macro_split_var = st.radio("View all teams or just this main slate's matchups?", ('DK Main Slate', 'FD Main Slate', 'All'), key='macro_split_var')
        if macro_split_var == 'DK Main Slate':
            macro_view_var = dk_main_slate
        if macro_split_var == 'FD Main Slate':
            macro_view_var = fd_main_slate
        if macro_split_var == 'All':
            macro_view_var = team_off_frame.Team.values.tolist()
        table_var2 = st.selectbox("Select Table", options=['Team Offense', 'Team Defense', 'Team Combo', 'Team Matchups'], key='table_var2')
        display_type2 = st.selectbox("Select Display Type", options=['Minutes', 'Total'], key='display_type2')
    with col2:
        if table_var2 == 'Team Offense':
            if display_type2 == 'Minutes':
                team_macro_display = team_off_frame.loc[:, team_off_frame.columns.isin(macro_minutes_cols)]
                team_macro_display = team_macro_display[team_macro_display['Team'].isin(macro_view_var)]
                team_macro_display = team_macro_display[['Team', 'Games', 'MIN', 'FGA/m', 'FG3A/m', 'FTA/m', 'PTS/m', 'REB/m', 'AST/m', 'STL/m', 'BLK/m', 'Fantasy/m', 'FD_Fantasy/m']]
            elif display_type2 == 'Total':
                team_macro_display = team_off_frame.loc[:, ~team_off_frame.columns.isin(remove_minutes_cols)]
                team_macro_display = team_macro_display[team_macro_display['Team'].isin(macro_view_var)]
        elif table_var2 == 'Team Defense':
            if display_type2 == 'Minutes':
                team_macro_display = team_def_frame.loc[:, team_def_frame.columns.isin(macro_minutes_cols)]
                team_macro_display = team_macro_display[team_macro_display['Team'].isin(macro_view_var)]
                team_macro_display = team_macro_display[['Team', 'Games', 'MIN', 'FGA/m', 'FG3A/m', 'FTA/m', 'PTS/m', 'REB/m', 'AST/m', 'STL/m', 'BLK/m', 'Fantasy/m', 'FD_Fantasy/m']]
            elif display_type2 == 'Total':
                team_macro_display = team_def_frame.loc[:, ~team_def_frame.columns.isin(remove_minutes_cols)]
                team_macro_display = team_macro_display[team_macro_display['Team'].isin(macro_view_var)]
        elif table_var2 == 'Team Combo':
            team_macro_display = team_combo_frame
            team_macro_display = team_macro_display[team_macro_display['Team'].isin(macro_view_var)]
        elif table_var2 == 'Team Matchups':
            team_macro_display = team_matchup_frame
            team_macro_display = team_macro_display[team_macro_display['Team'].isin(macro_view_var)]
        st.dataframe(team_macro_display.set_index('Team').style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(team_macro_format, precision=2), use_container_width = True)
        st.download_button(
            label="Export Table Data",
            data=convert_df_to_csv(team_macro_display),
            file_name='Team_Macro_export.csv',
            mime='text/csv',
        )