import pulp
import numpy as np
import pandas as pd
import streamlit as st
from database import gspreadcon

st.set_page_config(layout="wide")

roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}',
              '120+%': '{:.2%}','10x%': '{:.2%}','11x%': '{:.2%}','12x%': '{:.2%}','Own': '{:.2%}','LevX': '{:.2%}', 'CPT_Own': '{.2%}'}

odds_format = {'Odds': '{:.2%}'}

stat_format = {'Odds%': '{:.2%}'}

map_proj_format = {'Win%': '{:.2%}'}

master_hold = 'https://docs.google.com/spreadsheets/d/1dOXsbeWbvWjRyohsEEDXOiWji4-1R1J6E-Lu2CSM9AM/edit#gid=928272897'

@st.cache_resource(ttl=600)
def pull_baselines():
    sh = gspreadcon.open_by_url(master_hold)
    
    worksheet = sh.worksheet('Overall_Vegas')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.loc[raw_display['Team'] != ""]
    odds_table = raw_display[['Team', 'Vegas', 'Odds', 'Games']]
    
    worksheet = sh.worksheet('Overall_ROO')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    overall_roo = raw_display.loc[raw_display['Player'] != ""]
    
    worksheet = sh.worksheet('Win_ROO')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    win_roo = raw_display.loc[raw_display['Player'] != ""]
    
    worksheet = sh.worksheet('Loss_ROO')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    loss_roo = raw_display.loc[raw_display['Player'] != ""]
    
    worksheet = sh.worksheet('3_map_Proj')
    raw_display = pd.DataFrame(worksheet.get_all_records())
    raw_display = raw_display.loc[raw_display['Player'] != ""]
    map_proj_3 = raw_display[['Player', 'Team', 'Opponent', 'Odds', 'Win%', 'Avg Kills', 'Avg Deaths', 'Proj_Kills', 'Proj_Deaths']]
    data_cols = map_proj_3.columns.drop(['Player', 'Team', 'Opponent', 'Win%'])
    map_proj_3[data_cols] = map_proj_3[data_cols].apply(pd.to_numeric, errors='coerce')
    
    worksheet = sh.worksheet('Timestamp')
    timestamp = worksheet.acell('A1').value

    return odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3

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

odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 = pull_baselines()
t_stamp = f"Last Update: " + str(timestamp) + f" CST"

tab1, tab2, tab3 = st.tabs(["COD Odds Tables", "COD Range of Outcomes", "COD 3-map projections"])

with tab1:
    st.info(t_stamp)
    if st.button("Reset Data", key='reset1'):
              st.cache_data.clear()
              odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 = pull_baselines()
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    odds_display = odds_table
    st.dataframe(odds_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(odds_format, precision=2), use_container_width = True)
    st.download_button(
        label="Export Tables",
        data=convert_df_to_csv(odds_display),
        file_name='COD_Odds_Tables_export.csv',
        mime='text/csv',
    )

with tab2:
    st.info(t_stamp)
    if st.button("Reset Data", key='reset2'):
             st.cache_data.clear()
             odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 = pull_baselines()
             t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    model_choice = st.radio("What table would you like to display?", ('Overall', 'Wins', 'Losses'), key='roo_table')
    team_var1 = st.multiselect('View specific team?', options = overall_roo['Team'].unique(), key = 'roo_teamvar')
    if model_choice == 'Overall':
      hold_display = overall_roo
    elif model_choice == 'Wins':
      hold_display = win_roo
    elif model_choice == 'Losses':
      hold_display = loss_roo
    hold_display['Cpt_Own'] = (hold_display['Own']) * ((100 - (100-hold_display['Own'])))
    cpt_own_norm = 100 / hold_display['Cpt_Own'].sum()
    hold_display['Cpt_Own'] = (hold_display['Cpt_Own'] * cpt_own_norm)
    display = hold_display.set_index('Player')
    export_display = display
    export_display['Position'] = "FLEX"
    if team_var1:
              display = display[display['Team'].isin(team_var1)]
    st.dataframe(display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(roo_format, precision=2), use_container_width = True)
    st.download_button(
        label="Export Range of Outcomes",
        data=convert_df_to_csv(export_display),
        file_name='CSGO_ROO_export.csv',
        mime='text/csv',
    )

with tab3:
    st.info(t_stamp)
    if st.button("Reset Data", key='reset3'):
              st.cache_data.clear()
              odds_table, overall_roo, win_roo, timestamp, loss_roo, map_proj_3 = pull_baselines()
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
    team_var2 = st.multiselect('View specific team?', options = map_proj_3['Team'].unique(), key = 'stat_teamvar')
    map_stat_display = map_proj_3
    if team_var2:
          map_stat_display = map_stat_display[display['Team'].isin(team_var2)]
    st.dataframe(map_stat_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(map_proj_format, precision=2), use_container_width = True)
    st.download_button(
        label="Export Projections",
        data=convert_df_to_csv(map_stat_display),
        file_name='COD_Projections_export.csv',
        mime='text/csv',
    )