#initial imports
import streamlit as st
st.set_page_config(layout="wide")
import numpy as np
import pandas as pd
import time
from fuzzywuzzy import process

# Bring in numpy reqs
from numpy import nan as np_nan
from numpy import where as np_where
from numpy import random as np_random
from numpy import zeros as np_zeros
from numpy import array as np_array
from pandas import concat as pd_concat
from pandas import merge as pd_merge
from pandas import DataFrame

#bring in functions
from function_hold.NBA_functions import DK_NBA_ROO_Build, FD_NBA_ROO_Build

def load_file(upload):
    if upload is not None:
        try:
            if upload.name.endswith('.csv'):
                df = pd.read_csv(upload)
            elif upload.name.endswith(('.xls', '.xlsx')):
                df = pd.read_excel(upload)
            else:
                st.error('Please upload either a CSV or Excel file')
                return None
            
            export_df = df.copy()
            
            return export_df, df
        except Exception as e:
            st.error(f'Error loading file: {str(e)}')
            return None
    return None

tab1, tab2 = st.tabs(["Data Load", "Manage Portfolio"])
with tab1:
    if st.button('Clear data', key='reset1'):
        st.session_state.clear()
    sport_var = st.selectbox("Select Sport", ["NBA", "NFL", "MLB"])
    st.subheader("Projections File")
    if sport_var == "NBA":
        st.info("upload a projections file that has Data oriented in the following format: 'Player', 'Team', 'Opp', 'Position', 'Salary', 'Median', 'Minutes', 'Own'")
    elif sport_var == "NFL":
        st.info("upload a projections file that has Data oriented in the following format: 'Player', 'Team', 'Opp', 'Position', 'Salary', 'Median', 'Minutes', 'Own'")
    elif sport_var == "MLB":
        st.info("upload a projections file that has Data oriented in the following format: 'Player', 'Team', 'Opp', 'Position', 'Salary', 'Median', 'Minutes', 'Own'")
    
    # Create two columns for the uploader and template button
    upload_col, template_col = st.columns([3, 1])
    
    with upload_col:
        projections_file = st.file_uploader("Upload Projections File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
    
    with template_col:
        if sport_var == "NBA":
            template_df = pd.DataFrame(columns=['Player', 'Team', 'Opp', 'Position', 'Salary', 'Median', 'Minutes', 'Own'])
        elif sport_var == "NFL":
            template_df = pd.DataFrame(columns=['Player', 'Team', 'Opp', 'Position', 'Salary', 'Median', 'Minutes', 'Own'])
        elif sport_var == "MLB":
            template_df = pd.DataFrame(columns=['Player', 'Team', 'Opp', 'Position', 'Salary', 'Median', 'Minutes', 'Own'])
        # Add download button for template
        st.download_button(
            label="Template",
            data=template_df.to_csv(index=False),
            file_name="projections_template.csv",
            mime="text/csv"
        )
        
    if projections_file:
        export_projections, projections = load_file(projections_file)
        if projections is not None:
            st.success('Projections file loaded successfully!')
            st.dataframe(projections)

with tab2:
    if st.button('Clear data', key='reset2'):
        st.session_state.clear()

    with st.sidebar:
        site_var_sb = st.selectbox("Select Site", ["Draftkings", "Fanduel"])
        distribution_type_sb = st.selectbox("Select Distribution Type", ['normal', 'poisson', 'bimodal'])
        floor_var_sb = st.number_input("Floor (low end multiplier)", min_value=0.00, max_value=.50, value=.25, step=.01)
        ceiling_var_sb = st.number_input("Ceiling (high end multiplier)", min_value=1.50, max_value=3.00, value=2.00, step=.01)
        std_var_sb = st.number_input("Standard Deviation (variance within distribution)", min_value=1.00, max_value=5.00, value=4.00, step=.01)

    if projections_file:
        if st.button('Build ROO'):
            if sport_var == "NBA":
                if site_var_sb == "Draftkings":
                    disp_file = DK_NBA_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)
                elif site_var_sb == "Fanduel":
                    disp_file = FD_NBA_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)
            elif sport_var == "NFL":
                if site_var_sb == "Draftkings":
                    disp_file = DK_NFL_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)
                elif site_var_sb == "Fanduel":
                    disp_file = FD_NFL_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)
            elif sport_var == "MLB":
                if site_var_sb == "Draftkings":
                    disp_file = DK_MLB_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)
                elif site_var_sb == "Fanduel":
                    disp_file = FD_MLB_ROO_Build(projections, floor_var_sb, ceiling_var_sb, std_var_sb, distribution_type_sb)

    if disp_file is not None:
        st.dataframe(disp_file.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=1000, use_container_width = True)