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

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

import pulp
import numpy as np
import pandas as pd
import streamlit as st
import gspread
import pymongo
from itertools import combinations

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

        return db
    
db = init_conn()

player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
                   '4x%': '{:.2%}'}

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: #DAA520;
        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;
        border: 3px solid #FFD700;
        color: white;
    }

    .stTabs [data-baseweb="tab"]:hover {
        background-color: #FFD700;
        cursor: pointer;
    }
</style>""", unsafe_allow_html=True)

@st.cache_resource(ttl=200)
def player_stat_table():
    collection = db["Player_Level_ROO"] 
    cursor = collection.find()
    player_frame = pd.DataFrame(cursor)
    player_frame = player_frame[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%', 'Own',
                                 'Small Field Own%', 'Large Field Own%', 'Cash Own%', 'CPT_Own', 'Site', 'Type', 'Slate', 'player_id', 'timestamp']]

    collection = db["Player_Lines_ROO"] 
    cursor = collection.find()
    line_frame = pd.DataFrame(cursor)
    line_frame = line_frame[['Player', 'SK1', 'SK2', 'SK3', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '50+%', '2x%', '3x%', '4x%', 'Own', 'Site', 'Type', 'Slate']]

    collection = db["Player_Powerplay_ROO"] 
    cursor = collection.find()
    pp_frame = pd.DataFrame(cursor)
    pp_frame = pp_frame[['Player', 'SK1', 'SK2', 'SK3', 'SK4', 'SK5', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '75+%', '2x%', '3x%', '4x%', 'Own', 'Site', 'Type', 'Slate']]

    timestamp = player_frame['timestamp'].values[0]

    return player_frame, line_frame, pp_frame, timestamp

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

player_frame, line_frame, pp_frame, timestamp = player_stat_table()
t_stamp = f"Last Update: " + str(timestamp) + f" CST"

view_var1 = st.radio("View Type", ("Simple", "Advanced"), key='view_var1')

tab1, tab2, tab3 = st.tabs(["Player Range of Outcomes", "Line Combo Range of Outcomes", "Power Play Range of Outcomes"])

with tab1:
    with st.expander("Info and Filters"):
        with st.container():
            st.info("Advanced view includes all stats and thresholds, simple includes just basic columns for ease of use on mobile")
        st.info(t_stamp)
        if st.button("Load/Reset Data", key='reset1'):
            st.cache_data.clear()
            player_frame, line_frame, pp_frame, timestamp = player_stat_table()
            t_stamp = f"Last Update: " + str(timestamp) + f" CST"
        site_var1 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var1')
        main_var1 = st.radio("Main slate or secondary slate?", ('Main Slate', 'Secondary Slate'), key='main_var1')
        split_var1 = st.radio("Would you like to view the whole slate or just specific games?", ('Full Slate Run', 'Specific Games'), key='split_var1')
        if split_var1 == 'Specific Games':
            team_var1 = st.multiselect('Which teams would you like to include in the ROO?', options = player_frame['Team'].unique(), key='team_var1')
        elif split_var1 == 'Full Slate Run':
            team_var1 = player_frame.Team.values.tolist()
        pos_split1 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split1')
        if pos_split1 == 'Specific Positions':
            pos_var1 = st.multiselect('What Positions would you like to view?', options = ['C', 'W', 'D', 'G'])
        elif pos_split1 == 'All Positions':
            pos_var1 = 'All'
        sal_var1 = st.slider("Is there a certain price range you want to view?", 2000, 10000, (2000, 20000), key='sal_var1')

    final_Proj = player_frame[player_frame['Site'] == str(site_var1)]
    final_Proj = final_Proj[final_Proj['Type'] == 'Basic']
    final_Proj = final_Proj[final_Proj['Slate'] == main_var1]
    final_Proj = final_Proj[player_frame['Team'].isin(team_var1)]
    final_Proj = final_Proj[final_Proj['Salary'] >= sal_var1[0]]
    final_Proj = final_Proj[final_Proj['Salary'] <= sal_var1[1]]
    if pos_var1 != 'All':
            final_Proj = final_Proj[final_Proj['Position'].str.contains('|'.join(pos_var1))]
            final_Proj = final_Proj.sort_values(by='Median', ascending=False)
    if pos_var1 == 'All':
            final_Proj = final_Proj.sort_values(by='Median', ascending=False)
    
    if view_var1 == 'Advanced':
        display_proj = final_Proj[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
                                    'Own', 'Small Field Own%', 'Large Field Own%', 'Cash Own%', 'CPT_Own']]
    elif view_var1 == 'Simple':
        display_proj = final_Proj[['Player', 'Position', 'Salary', 'Median', '3x%', 'Own']]
    st.dataframe(display_proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True, hide_index=True)
    st.download_button(
            label="Export Tables",
            data=convert_df_to_csv(display_proj),
            file_name='NHL_player_export.csv',
            mime='text/csv',
    )

with tab2:
    with st.expander("Info and Filters"):
        with st.container():
            st.info("Advanced view includes all stats and thresholds, simple includes just basic columns for ease of use on mobile")
        st.info(t_stamp)
        if st.button("Load/Reset Data", key='reset2'):
              st.cache_data.clear()
              player_frame, line_frame, pp_frame, timestamp = player_stat_table()
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
        site_var2 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var2')
        main_var2 = st.radio("Main slate or secondary slate?", ('Main Slate', 'Secondary Slate'), key='main_var2')
        sal_var2 = st.slider("Is there a certain price range you want to view?", 5000, 40000, (5000, 40000), key='sal_var2')

    final_line_combos = line_frame[line_frame['Site'] == str(site_var2)]
    final_line_combos = final_line_combos[final_line_combos['Type'] == 'Basic']
    final_line_combos = final_line_combos[final_line_combos['Slate'] == main_var2]
    final_line_combos = final_line_combos[final_line_combos['Salary'] >= sal_var2[0]]
    final_line_combos = final_line_combos[final_line_combos['Salary'] <= sal_var2[1]]
    final_line_combos = final_line_combos.drop_duplicates(subset=['Player'])
    final_line_combos = final_line_combos.sort_values(by='Median', ascending=False)

    if view_var1 == 'Advanced':
        display_proj_lines = final_line_combos[['Player', 'SK1', 'SK2', 'SK3', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '50+%', '2x%', '3x%', '4x%',
                                    'Own']]
    elif view_var1 == 'Simple':
        display_proj_lines = final_line_combos[['SK1', 'SK2', 'SK3', 'Salary', 'Median', '3x%', 'Own']]
    st.dataframe(display_proj_lines.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(player_roo_format, precision=2), use_container_width = True, hide_index=True)
    st.download_button(
            label="Export Tables",
            data=convert_df_to_csv(display_proj_lines),
            file_name='NHL_linecombos_export.csv',
            mime='text/csv',
    )

with tab3:
    with st.expander("Info and Filters"):
        with st.container():
            st.info("Advanced view includes all stats and thresholds, simple includes just basic columns for ease of use on mobile")
        st.info(t_stamp)
        if st.button("Load/Reset Data", key='reset3'):
              st.cache_data.clear()
              player_frame, line_frame, pp_frame, timestamp = player_stat_table()
              t_stamp = f"Last Update: " + str(timestamp) + f" CST"
        site_var3 = st.radio("What table would you like to display?", ('Draftkings', 'Fanduel'), key='site_var3')
        main_var3 = st.radio("Main slate or secondary slate?", ('Main Slate', 'Secondary Slate'), key='main_var3')
        sal_var3 = st.slider("Is there a certain price range you want to view?", 5000, 40000, (5000, 40000), key='sal_var3')
    
    final_pp_combos = pp_frame[pp_frame['Site'] == str(site_var3)]
    final_pp_combos = final_pp_combos[final_pp_combos['Type'] == 'Basic']
    final_pp_combos = final_pp_combos[final_pp_combos['Slate'] == main_var3]
    final_pp_combos = final_pp_combos[final_pp_combos['Salary'] >= sal_var3[0]]
    final_pp_combos = final_pp_combos[final_pp_combos['Salary'] <= sal_var3[1]]
    final_pp_combos = final_pp_combos.drop_duplicates(subset=['Player'])
    final_pp_combos = final_pp_combos.sort_values(by='Median', ascending=False)

    if view_var1 == 'Advanced':
        display_proj_pp = final_pp_combos[['Player', 'SK1', 'SK2', 'SK3', 'SK4', 'SK5', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '75+%', '2x%', '3x%', '4x%',
                                    'Own']]
    elif view_var1 == 'Simple':
        display_proj_pp = final_pp_combos[['SK1', 'SK2', 'SK3', 'SK4', 'SK5', 'Salary', 'Median', '3x%', 'Own']]
    st.dataframe(display_proj_pp.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True, hide_index=True)
    st.download_button(
            label="Export Tables",
            data=convert_df_to_csv(display_proj_pp),
            file_name='NHL_powerplay_export.csv',
            mime='text/csv',
    )