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

st.set_page_config(layout="wide")

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

        return db
    
db = init_conn()

game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%}',
              'Fifth Inning Lead Percentage': '{:.2%}', '8+ runs': '{:.2%}', 'DK LevX': '{:.2%}', 'FD LevX': '{:.2%}'}

team_roo_format = {'Top Score%': '{:.2%}','0 Runs': '{:.2%}', '1 Run': '{:.2%}', '2 Runs': '{:.2%}', '3 Runs': '{:.2%}', '4 Runs': '{:.2%}',
                   '5 Runs': '{:.2%}','6 Runs': '{:.2%}', '7 Runs': '{:.2%}', '8 Runs': '{:.2%}', '9 Runs': '{:.2%}', '10 Runs': '{:.2%}'}

wrong_acro = ['WSH', 'AZ', 'CHW']
right_acro = ['WAS', 'ARI', 'CWS']

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: #FFD700;
        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;
        color: white;
    }
    .stTabs [data-baseweb="tab"]:hover {
        background-color: #DAA520;
        cursor: pointer;
    }
</style>""", unsafe_allow_html=True)

@st.cache_resource(ttl = 60)
def init_stat_load():

    collection = db["Player_Range_Of_Outcomes"] 
    cursor = collection.find()

    raw_display = pd.DataFrame(list(cursor))
    raw_display = raw_display[['Player', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Own%', 'Site', 'Slate']]
    raw_display = raw_display.rename(columns={'Own%': 'Own'})
    initial_concat = raw_display.sort_values(by='Own', ascending=False)

    return initial_concat

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

proj_raw = init_stat_load()

col1, col2 = st.columns([1, 5])

with col1:
    with st.container():
        if st.button("Load/Reset Data", key='reset1'):
            st.cache_data.clear()
            proj_raw = init_stat_load()
            for key in st.session_state.keys():
                del st.session_state[key]
        site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'), key='site_var1')
        slate_var1 = st.radio("What slate are you working with?", ('Main Slate', 'Secondary Slate'), key='slate_var1')
        if site_var1 == 'Draftkings':
            raw_baselines = proj_raw[proj_raw['Site'] == 'Draftkings']
            if slate_var1 == 'Main Slate':
                raw_baselines = raw_baselines[raw_baselines['Slate'] == 'main_slate']
            elif slate_var1 == 'Secondary Slate':
                raw_baselines = raw_baselines[raw_baselines['Slate'] == 'secondary_slate']
            raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
        elif site_var1 == 'Fanduel':
            raw_baselines = proj_raw[proj_raw['Site'] == 'Fanduel']
            if slate_var1 == 'Main Slate':
                raw_baselines = raw_baselines[raw_baselines['Slate'] == 'main_slate']
            elif slate_var1 == 'Secondary Slate':
                raw_baselines = raw_baselines[raw_baselines['Slate'] == 'secondary_slate']
            raw_baselines = raw_baselines.sort_values(by='Own', ascending=False)
        split_var2 = st.radio("Would you like to run stack analysis for the full slate or individual teams?", ('Full Slate Run', 'Specific Teams'), key='split_var2')
        if split_var2 == 'Specific Teams':
            team_var2 = st.multiselect('Which teams would you like to include in the analysis?', options = raw_baselines['Team'].unique(), key='team_var2')
        elif split_var2 == 'Full Slate Run':
            team_var2 = raw_baselines.Team.unique().tolist()
        pos_split2 = st.radio("Are you viewing all positions, specific groups, or specific positions?", ('All Positions', 'Specific Positions'), key='pos_split2')
        if pos_split2 == 'Specific Positions':
            pos_var2 = st.multiselect('What Positions would you like to view?', options = ['SP', 'P', 'C', '1B', '2B', '3B', 'SS', 'OF'])
        elif pos_split2 == 'All Positions':
            pos_var2 = 'All'
        if site_var1 == 'Draftkings':
            max_sal2 = st.number_input('Max Salary', min_value = 5000, max_value = 50000, value = 35000, step = 100, key='max_sal2')
        elif site_var1 == 'Fanduel':
            max_sal2 = st.number_input('Max Salary', min_value = 5000, max_value = 35000, value = 25000, step = 100, key='max_sal2')
        size_var2 = st.selectbox('What size of stacks are you analyzing?', options = ['3-man', '4-man', '5-man'])
        if size_var2 == '3-man':
            stack_size = 3
        if size_var2 == '4-man':
            stack_size = 4
        if size_var2 == '5-man':
            stack_size = 5

        team_dict = dict(zip(raw_baselines.Player, raw_baselines.Team))
        proj_dict = dict(zip(raw_baselines.Player, raw_baselines.Median))
        own_dict = dict(zip(raw_baselines.Player, raw_baselines.Own))
        cost_dict = dict(zip(raw_baselines.Player, raw_baselines.Salary))

with col2:
    stack_hold_container = st.empty()
    comb_list = []
    if pos_split2 == 'All Positions':
        raw_baselines = raw_baselines
    elif pos_split2 != 'All Positions':
        raw_baselines = raw_baselines[raw_baselines['Position'].str.contains('|'.join(pos_var2))]

    for cur_team in team_var2:
        working_baselines = raw_baselines
        working_baselines = working_baselines[working_baselines['Team'] == cur_team]
        working_baselines = working_baselines[working_baselines['Position'] != 'SP']
        working_baselines = working_baselines[working_baselines['Position'] != 'P']
        order_list = working_baselines['Player']

        comb = combinations(order_list, stack_size)

        for i in list(comb):
            comb_list.append(i)

    comb_DF = pd.DataFrame(comb_list)

    if stack_size == 3:
        comb_DF['Team'] = comb_DF[0].map(team_dict)

        comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
                comb_DF[1].map(proj_dict),
                comb_DF[2].map(proj_dict)])

        comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
                comb_DF[1].map(cost_dict),
                comb_DF[2].map(cost_dict)])

        comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
                comb_DF[1].map(own_dict),
                comb_DF[2].map(own_dict)])
    elif stack_size == 4:
        comb_DF['Team'] = comb_DF[0].map(team_dict)

        comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
                comb_DF[1].map(proj_dict),
                comb_DF[2].map(proj_dict),
                comb_DF[3].map(proj_dict)])

        comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
                comb_DF[1].map(cost_dict),
                comb_DF[2].map(cost_dict),
                comb_DF[3].map(cost_dict)])

        comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
                comb_DF[1].map(own_dict),
                comb_DF[2].map(own_dict),
                comb_DF[3].map(own_dict)])
    elif stack_size == 5:
        comb_DF['Team'] = comb_DF[0].map(team_dict)

        comb_DF['Proj'] = sum([comb_DF[0].map(proj_dict),
                comb_DF[1].map(proj_dict),
                comb_DF[2].map(proj_dict),
                comb_DF[3].map(proj_dict),
                comb_DF[4].map(proj_dict)])

        comb_DF['Salary'] = sum([comb_DF[0].map(cost_dict),
                comb_DF[1].map(cost_dict),
                comb_DF[2].map(cost_dict),
                comb_DF[3].map(cost_dict),
                comb_DF[4].map(cost_dict)])

        comb_DF['Own%'] = sum([comb_DF[0].map(own_dict),
                comb_DF[1].map(own_dict),
                comb_DF[2].map(own_dict),
                comb_DF[3].map(own_dict),
                comb_DF[4].map(own_dict)])

    comb_DF = comb_DF.sort_values(by='Proj', ascending=False)
    comb_DF = comb_DF.loc[comb_DF['Salary'] <= max_sal2]

    cut_var = 0

    if stack_size == 3:
        while cut_var <= int(len(comb_DF)):
            try:
                if int(cut_var) == 0:
                    cur_proj = float(comb_DF.iat[cut_var,4])
                    cur_own = float(comb_DF.iat[cut_var,6])
                elif int(cut_var) >= 1:
                    check_own = float(comb_DF.iat[cut_var,6])
                    if check_own > cur_own:
                        comb_DF = comb_DF.drop([cut_var])
                        cur_own = cur_own
                        cut_var = cut_var - 1
                        comb_DF = comb_DF.reset_index()
                        comb_DF = comb_DF.drop(['index'], axis=1)
                    elif check_own <= cur_own:
                        cur_own = float(comb_DF.iat[cut_var,6])
                        cut_var = cut_var
                cut_var += 1
            except:
                cut_var += 1
    elif stack_size == 4:
        while cut_var <= int(len(comb_DF)):
            try:
                if int(cut_var) == 0:
                    cur_proj = float(comb_DF.iat[cut_var,5])
                    cur_own = float(comb_DF.iat[cut_var,7])
                elif int(cut_var) >= 1:
                    check_own = float(comb_DF.iat[cut_var,7])
                    if check_own > cur_own:
                        comb_DF = comb_DF.drop([cut_var])
                        cur_own = cur_own
                        cut_var = cut_var - 1
                        comb_DF = comb_DF.reset_index()
                        comb_DF = comb_DF.drop(['index'], axis=1)
                    elif check_own <= cur_own:
                        cur_own = float(comb_DF.iat[cut_var,7])
                        cut_var = cut_var
                cut_var += 1
            except:
                cut_var += 1
    elif stack_size == 5:
        while cut_var <= int(len(comb_DF)):
            try:
                if int(cut_var) == 0:
                    cur_proj = float(comb_DF.iat[cut_var,6])
                    cur_own = float(comb_DF.iat[cut_var,8])
                elif int(cut_var) >= 1:
                    check_own = float(comb_DF.iat[cut_var,8])
                    if check_own > cur_own:
                        comb_DF = comb_DF.drop([cut_var])
                        cur_own = cur_own
                        cut_var = cut_var - 1
                        comb_DF = comb_DF.reset_index()
                        comb_DF = comb_DF.drop(['index'], axis=1)
                    elif check_own <= cur_own:
                        cur_own = float(comb_DF.iat[cut_var,8])
                        cut_var = cut_var
                cut_var += 1
            except:
                cut_var += 1

    with stack_hold_container:
        stack_hold_container = st.empty()
        st.dataframe(comb_DF.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
    st.download_button(
            label="Export Tables",
            data=convert_df_to_csv(comb_DF),
            file_name='MLB_Stack_Options_export.csv',
            mime='text/csv',
    )