import streamlit as st
st.set_page_config(layout="wide")
import numpy as np
import pandas as pd
from rapidfuzz import process, fuzz
from collections import Counter
from pymongo.mongo_client import MongoClient
from pymongo.server_api import ServerApi
from datetime import datetime

def init_conn():
        
        uri = st.secrets['mongo_uri']
        client = MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
        db = client['Contest_Information']

        return db
    
def grab_contest_names(db, sport, type):
    if type == 'Classic':
        db_type = 'reg'
    elif type == 'Showdown':
        db_type = 'sd'
    collection = db[f'{sport}_{db_type}_contest_info']
    cursor = collection.find()

    curr_info = pd.DataFrame(list(cursor)).drop('_id', axis=1)
    curr_info['Date'] = pd.to_datetime(curr_info['Contest Date'].sort_values(ascending = False))
    curr_info['Date'] = curr_info['Date'].dt.strftime('%Y-%m-%d')
    contest_names = curr_info['Contest Name']
    contest_id_map = dict(zip(curr_info['Contest Name'], curr_info['Contest ID']))
    
    return contest_names, contest_id_map, curr_info

def grab_contest_player_info(db, sport, type, contest_date, contest_name, contest_id_map):
    if type == 'Classic':
        db_type = 'reg'
    elif type == 'Showdown':
        db_type = 'showdown'
    collection = db[f'{sport}_{db_type}_player_info']
    cursor = collection.find()

    player_info = pd.DataFrame(list(cursor)).drop('_id', axis=1)
    player_info = player_info[player_info['Contest Date'] == contest_date]
    try:
        player_info = player_info[player_info['Contest ID'] == contest_id_map[contest_name]]
    except:
        pass
    player_info = player_info.rename(columns={'Display Name': 'Player'})
    player_info = player_info.sort_values(by='Salary', ascending=True).drop_duplicates(subset='Player', keep='first')

    info_maps = {
        'position_dict': dict(zip(player_info['Player'], player_info['Position'])),
        'salary_dict': dict(zip(player_info['Player'], player_info['Salary'])),
        'team_dict': dict(zip(player_info['Player'], player_info['Team'])),
        'opp_dict': dict(zip(player_info['Player'], player_info['Opp'])),
        'fpts_avg_dict': dict(zip(player_info['Player'], player_info['Avg FPTS']))
    }
    
    return player_info, info_maps

db = init_conn()

## import global functions
from global_func.load_contest_file import load_contest_file
from global_func.create_player_exposures import create_player_exposures
from global_func.create_stack_exposures import create_stack_exposures
from global_func.create_stack_size_exposures import create_stack_size_exposures
from global_func.create_general_exposures import create_general_exposures
from global_func.grab_contest_data import grab_contest_data

def is_valid_input(file):
    if isinstance(file, pd.DataFrame):
        return not file.empty
    else:
        return file is not None  # For Streamlit uploader objects

player_exposure_format = {'Exposure Overall': '{:.2%}', 'Exposure Top 1%': '{:.2%}', 'Exposure Top 5%': '{:.2%}', 'Exposure Top 10%': '{:.2%}', 'Exposure Top 20%': '{:.2%}'}

tab1, tab2 = st.tabs(["Data Load", "Contest Analysis"])
with tab1:
    col1, col2 = st.columns(2)

    with col1:
        if st.button('Clear data', key='reset1'):
            st.session_state.clear()
        search_options, sport_options, date_options = st.columns(3)
        with search_options:
            parse_type = st.selectbox("Manual upload or DB search?", ['DB Search', 'Manual'], key='parse_type')
        with sport_options:
            sport_select = st.selectbox("Select Sport", ['MLB', 'MMA', 'GOLF'], key='sport_select')
            type_var = st.selectbox("Select Game Type", ['Classic', 'Showdown'], key='type_var')
            contest_names, contest_id_map, curr_info = grab_contest_names(db, sport_select, type_var)
            
        with date_options:
            date_list = curr_info['Date'].sort_values(ascending=False).unique()
            date_list = date_list[date_list != pd.Timestamp.today().strftime('%Y-%m-%d')]
            date_select = st.selectbox("Select Date", date_list, key='date_select')
            date_select2 = (pd.to_datetime(date_select) + pd.Timedelta(days=1)).strftime('%Y-%m-%d')
            
            name_parse = curr_info[curr_info['Date'] == date_select]['Contest Name'].reset_index(drop=True)
            date_select = date_select.replace('-', '')
            date_select2 = date_select2.replace('-', '')

        contest_name_var = st.selectbox("Select Contest to load", name_parse)
        if parse_type == 'DB Search':
            if 'Contest_file_helper' in st.session_state:
                del st.session_state['Contest_file_helper']
            if 'Contest_file' in st.session_state:
                del st.session_state['Contest_file']
            if 'Contest_file' not in st.session_state:
                if st.button('Load Contest Data', key='load_contest_data'):
                    st.session_state['player_info'], st.session_state['info_maps'] = grab_contest_player_info(db, sport_select, type_var, date_select, contest_name_var, contest_id_map)
                    st.session_state['Contest_file'] = grab_contest_data(sport_select, contest_name_var, contest_id_map, date_select, date_select2)
            else:
                pass
    with col2:
        st.info(f"If you are manually loading and do not have the results CSV for the contest you selected, you can find it here: https://www.draftkings.com/contest/gamecenter/{contest_id_map[contest_name_var]}#/")
        if parse_type == 'Manual':
            if 'Contest_file_helper' in st.session_state:
                del st.session_state['Contest_file_helper']
            if 'Contest_file' in st.session_state:
                del st.session_state['Contest_file']
            if 'Contest_file' not in st.session_state:
                st.session_state['Contest_upload'] = st.file_uploader("Upload Contest File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
                st.session_state['player_info'], st.session_state['info_maps'] = grab_contest_player_info(db, sport_select, type_var, date_select, contest_name_var, contest_id_map)
                try:
                    st.session_state['Contest_file'] = pd.read_csv(st.session_state['Contest_upload'])
                except:
                    st.warning('Please upload a Contest CSV')
            else:
                pass

    if 'Contest_file' in st.session_state:
        st.session_state['Contest'], st.session_state['ownership_df'], st.session_state['actual_df'], st.session_state['entry_list'], check_lineups = load_contest_file(st.session_state['Contest_file'], type_var, st.session_state['player_info'], sport_select)
        st.session_state['Contest'] = st.session_state['Contest'].dropna(how='all')
        st.session_state['Contest'] = st.session_state['Contest'].reset_index(drop=True)
        if st.session_state['Contest'] is not None:
            st.success('Contest file loaded successfully!')
            st.dataframe(st.session_state['Contest'].head(100))
        
    if 'Contest_file' in st.session_state:
        st.session_state['ownership_dict'] = dict(zip(st.session_state['ownership_df']['Player'], st.session_state['ownership_df']['Own']))
        st.session_state['actual_dict'] = dict(zip(st.session_state['actual_df']['Player'], st.session_state['actual_df']['FPTS']))
        if 'Contest_file_helper' not in st.session_state:
            st.session_state['salary_dict'] = st.session_state['info_maps']['salary_dict']
            st.session_state['team_dict'] = st.session_state['info_maps']['team_dict']
            st.session_state['pos_dict'] = st.session_state['info_maps']['position_dict']
        else:
            st.session_state['salary_dict'] = dict(zip(st.session_state['salary_df']['Player'], st.session_state['salary_df']['Salary']))
            st.session_state['team_dict'] = dict(zip(st.session_state['team_df']['Player'], st.session_state['team_df']['Team']))
            st.session_state['pos_dict'] = dict(zip(st.session_state['pos_df']['Player'], st.session_state['pos_df']['Pos']))

with tab2:
    excluded_cols = ['BaseName', 'EntryCount']
    if 'Contest' in st.session_state:
        player_columns = [col for col in st.session_state['Contest'].columns if col not in excluded_cols]
        for col in player_columns:
            st.session_state['Contest'][col] = st.session_state['Contest'][col].astype(str)
    
        # Create mapping dictionaries
        map_dict = {
            'pos_map': st.session_state['pos_dict'],
            'team_map': st.session_state['team_dict'],
            'salary_map': st.session_state['salary_dict'],
            'own_map': st.session_state['ownership_dict'],
            'own_percent_rank': dict(zip(st.session_state['ownership_df']['Player'], st.session_state['ownership_df']['Own'].rank(pct=True)))
        }
        # Create a copy of the dataframe for calculations
        working_df = st.session_state['Contest'].copy() 

        if type_var == 'Classic':
            working_df['stack'] = working_df.apply(
                lambda row: Counter(
                    map_dict['team_map'].get(player, '') for player in row[4:]
                    if map_dict['team_map'].get(player, '') != ''
                ).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row[4:]) else '',
                axis=1
            )
            working_df['stack_size'] = working_df.apply(
                lambda row: Counter(
                    map_dict['team_map'].get(player, '') for player in row[4:]
                    if map_dict['team_map'].get(player, '') != ''
                ).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row[4:]) else '',
                axis=1
            )
            working_df['salary'] = working_df.apply(lambda row: sum(map_dict['salary_map'].get(player, 0) for player in row), axis=1)
            working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
            working_df['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row), axis=1)
            working_df['sorted'] = working_df[player_columns].apply(
                lambda row: ','.join(sorted(row.values)),
                axis=1
            )
            working_df['dupes'] = working_df.groupby('sorted').transform('size')
            working_df = working_df.reset_index()
            working_df['percentile_finish'] = working_df['index'].rank(pct=True)
            working_df['finish'] = working_df['index']
            working_df = working_df.drop(['sorted', 'index'], axis=1)
        
        elif type_var == 'Showdown':
            working_df['stack'] = working_df.apply(
                lambda row: Counter(
                    map_dict['team_map'].get(player, '') for player in row[2:]
                    if map_dict['team_map'].get(player, '') != ''
                ).most_common(1)[0][0] if any(map_dict['team_map'].get(player, '') for player in row[2:]) else '',
                axis=1
            )
            working_df['stack_size'] = working_df.apply(
                lambda row: Counter(
                    map_dict['team_map'].get(player, '') for player in row[2:]
                    if map_dict['team_map'].get(player, '') != ''
                ).most_common(1)[0][1] if any(map_dict['team_map'].get(player, '') for player in row[2:]) else '',
                axis=1
            )
            # Modified salary calculation with 1.5x multiplier for first player
            working_df['salary'] = working_df.apply(
                lambda row: (map_dict['salary_map'].get(row[2], 0) * 1.5) + 
                           sum(map_dict['salary_map'].get(player, 0) for player in row[3:]),
                axis=1
            )
            # Modified actual_fpts calculation with 1.5x multiplier for first player
            working_df['actual_fpts'] = working_df.apply(
                lambda row: (st.session_state['actual_dict'].get(row[2], 0) * 1.5) + 
                           sum(st.session_state['actual_dict'].get(player, 0) for player in row[3:]),
                axis=1
            )
            working_df['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row), axis=1)
            working_df['sorted'] = working_df[player_columns].apply(
                lambda row: ','.join(sorted(row.values)),
                axis=1
            )
            working_df['dupes'] = working_df.groupby('sorted').transform('size')
            working_df = working_df.reset_index()
            working_df['percentile_finish'] = working_df['index'].rank(pct=True)
            working_df['finish'] = working_df['index']
            working_df = working_df.drop(['sorted', 'index'], axis=1)
        st.session_state['field_player_frame'] = create_player_exposures(working_df, player_columns)
        st.session_state['field_stack_frame'] = create_stack_exposures(working_df)

        with st.expander("Info and filters"):
            if st.button('Clear data', key='reset3'):
                st.session_state.clear()
            with st.form(key='filter_form'):
                entry_parse_var = st.selectbox("Do you want to view a specific player(s) or a group of players?", ['All', 'Specific'])
                entry_names = st.multiselect("Select players", options=st.session_state['entry_list'], default=[])
                submitted = st.form_submit_button("Submit")
                if submitted:
                    if 'player_frame' in st.session_state:
                        del st.session_state['player_frame']
                    if 'stack_frame' in st.session_state:
                        del st.session_state['stack_frame']
                    # Apply entry name filter if specific entries are selected
                    if entry_parse_var == 'Specific' and entry_names:
                        working_df = working_df[working_df['BaseName'].isin(entry_names)]
            
        # Initialize pagination in session state if not exists
        if 'current_page' not in st.session_state:
            st.session_state.current_page = 1

        # Calculate total pages
        rows_per_page = 500
        total_rows = len(working_df)
        total_pages = (total_rows + rows_per_page - 1) // rows_per_page

        # Create pagination controls in a single row
        pagination_cols = st.columns([4, 1, 1, 1, 4])
        with pagination_cols[1]:
            if st.button(f"Previous Page"):
                if st.session_state['current_page'] > 1:
                    st.session_state.current_page -= 1
                else:
                    st.session_state.current_page = 1
                    if 'player_frame' in st.session_state:
                        del st.session_state['player_frame']
                    if 'stack_frame' in st.session_state:
                        del st.session_state['stack_frame']

        with pagination_cols[3]:
            if st.button(f"Next Page"):
                st.session_state.current_page += 1
                if 'player_frame' in st.session_state:
                    del st.session_state['player_frame']
                if 'stack_frame' in st.session_state:
                    del st.session_state['stack_frame']

        # Calculate start and end indices for current page
        start_idx = (st.session_state.current_page - 1) * rows_per_page
        end_idx = min((st.session_state.current_page) * rows_per_page, total_rows)
        st.dataframe(
            working_df.iloc[start_idx:end_idx].style
            .background_gradient(axis=0)
            .background_gradient(cmap='RdYlGn')
            .format(precision=2), 
            height=500,
            use_container_width=True,
            hide_index=True
        )

        with st.container():
            tab1, tab2, tab3, tab4 = st.tabs(['Player Used Info', 'Stack Used Info', 'Stack Size Info', 'General Info'])
            with tab1:
                col1, col2 = st.columns(2)
                with col1:
                    pos_var = st.selectbox("Which position(s) would you like to view?",  ['All', 'Specific'], key='pos_var')
                with col2:
                    if pos_var == 'Specific':
                        pos_select = st.multiselect("Select your position(s)", ['P', 'C', '1B', '2B', '3B', 'SS', 'OF'], key='pos_select')
                    else:
                        pos_select = None

                if entry_parse_var == 'All':

                    st.session_state['player_frame'] = create_player_exposures(working_df, player_columns)
                    hold_frame = st.session_state['player_frame'].copy()
                    if sport_select == 'GOLF':
                        hold_frame['Pos'] = 'G'
                    else:
                        hold_frame['Pos'] = hold_frame['Player'].map(map_dict['pos_map'])
                    st.session_state['player_frame'].insert(1, 'Pos', hold_frame['Pos'])
                    st.session_state['player_frame'] = st.session_state['player_frame'].dropna(subset=['Pos'])
                    if pos_select:
                        position_mask = st.session_state['player_frame']['Pos'].apply(lambda x: any(pos in x for pos in pos_select))
                        st.session_state['player_frame'] = st.session_state['player_frame'][position_mask]
                    st.dataframe(st.session_state['player_frame'].
                        sort_values(by='Exposure Overall', ascending=False).
                        style.background_gradient(cmap='RdYlGn').
                        format(formatter='{:.2%}', subset=st.session_state['player_frame'].iloc[:, 2:].select_dtypes(include=['number']).columns),
                        hide_index=True)
                else:

                    st.session_state['player_frame'] = create_player_exposures(working_df, player_columns, entry_names)
                    hold_frame = st.session_state['player_frame'].copy()
                    if sport_select == 'GOLF':
                        hold_frame['Pos'] = 'G'
                    else:
                        hold_frame['Pos'] = hold_frame['Player'].map(map_dict['pos_map'])
                    st.session_state['player_frame'].insert(1, 'Pos', hold_frame['Pos'])
                    st.session_state['player_frame'] = st.session_state['player_frame'].dropna(subset=['Pos'])
                    if pos_select:
                        position_mask = st.session_state['player_frame']['Pos'].apply(lambda x: any(pos in x for pos in pos_select))
                        st.session_state['player_frame'] = st.session_state['player_frame'][position_mask]
                    st.dataframe(st.session_state['player_frame'].
                        sort_values(by='Exposure Overall', ascending=False).
                        style.background_gradient(cmap='RdYlGn').
                        format(formatter='{:.2%}', subset=st.session_state['player_frame'].iloc[:, 2:].select_dtypes(include=['number']).columns),
                        hide_index=True)
            with tab2:

                if entry_parse_var == 'All':
                    st.session_state['stack_frame'] = create_stack_exposures(working_df)
                    st.dataframe(st.session_state['stack_frame'].
                        sort_values(by='Exposure Overall', ascending=False).
                        style.background_gradient(cmap='RdYlGn').
                        format(formatter='{:.2%}', subset=st.session_state['stack_frame'].iloc[:, 1:].select_dtypes(include=['number']).columns),
                        hide_index=True)
                else:
                    st.session_state['stack_frame'] = create_stack_exposures(working_df, entry_names)
                    st.dataframe(st.session_state['stack_frame'].
                        sort_values(by='Exposure Overall', ascending=False).
                        style.background_gradient(cmap='RdYlGn').
                        format(formatter='{:.2%}', subset=st.session_state['stack_frame'].iloc[:, 1:].select_dtypes(include=['number']).columns),
                        hide_index=True)
            with tab3:
                
                if entry_parse_var == 'All':
                    st.session_state['stack_size_frame'] = create_stack_size_exposures(working_df)
                    st.dataframe(st.session_state['stack_size_frame'].
                        sort_values(by='Exposure Overall', ascending=False).
                        style.background_gradient(cmap='RdYlGn').
                        format(formatter='{:.2%}', subset=st.session_state['stack_size_frame'].iloc[:, 1:].select_dtypes(include=['number']).columns),
                        hide_index=True)
                else:
                    st.session_state['stack_size_frame'] = create_stack_size_exposures(working_df, entry_names)
                    st.dataframe(st.session_state['stack_size_frame'].
                        sort_values(by='Exposure Overall', ascending=False).
                        style.background_gradient(cmap='RdYlGn').
                        format(formatter='{:.2%}', subset=st.session_state['stack_size_frame'].iloc[:, 1:].select_dtypes(include=['number']).columns),
                        hide_index=True)
            
            with tab4:
                
                if entry_parse_var == 'All':
                    st.session_state['general_frame'] = create_general_exposures(working_df)
                    st.dataframe(st.session_state['general_frame'].style.background_gradient(cmap='RdYlGn', axis=1).format(precision=2), hide_index=True)
                    
                else:
                    st.session_state['general_frame'] = create_general_exposures(working_df, entry_names)
                    st.dataframe(st.session_state['general_frame'].style.background_gradient(cmap='RdYlGn', axis=1).format(precision=2), hide_index=True)