import os
import pandas as pd
import streamlit as st

from config import DEFAULT_ICON, LEAGUE_NAME, LEAGUE_NUMBER_TEAMS
from streamlit_filter import filter_dataframe


KEEPER_DATA_URL = "../../tests/mocks/2023_keepers.csv"
HEADSHOT_DATA_URL = "../../tests/mocks/2023_player_headshots.csv"


def load_player_ids() -> pd.DataFrame:
    df = pd.read_csv(r"https://raw.githubusercontent.com/dynastyprocess/data/master/files/db_playerids.csv")
    df["merge_id"] = df["yahoo_id"].combine_first(df["stats_id"])
    return df


def load_adp() -> pd.DataFrame:
    df = pd.read_csv(r"https://raw.githubusercontent.com/dynastyprocess/data/master/files/values-players.csv")
    return df


def add_opinionated_keeper_value(df: pd.DataFrame):
    for score_fmt in ["1qb", "2qb"]:
        df[f"value_{score_fmt}"] = (df["keeper_cost"] - 0.5) * LEAGUE_NUMBER_TEAMS - df[f"ecr_{score_fmt}"]


@st.cache_data(ttl=60*60*24)
def load_data():
    data = pd.read_csv(os.path.join(os.path.dirname(__file__), KEEPER_DATA_URL), index_col=0)
    # Hack to get position, replace with better position from yahoo api in future
    data["position"] = data["eligible_positions"].apply(lambda x: eval(x)[0])
    data.columns = data.columns.str.lower()
    teams_list = sorted(list(data["team_name"].unique()))

    # Merge player ids
    df_player_ids = load_player_ids()
    data = data.merge(df_player_ids, how="left", left_on="player_id", right_on="merge_id", suffixes=("", "_ids"))

    # Merge ADP
    df_adp = load_adp()
    data = data.merge(df_adp, how="left", left_on="fantasypros_id", right_on="fp_id", suffixes=("", "_adp"))
    add_opinionated_keeper_value(data)
    return data, teams_list


def filtered_keeper_dataframe(data: pd.DataFrame, teams_list: list[str]):
    teams_selected = st.multiselect("Team:", teams_list)
    teams_filter = data["team_name"].isin(teams_selected) if teams_selected else data["team_name"].isin(teams_list)

    is_advanced = st.checkbox("Show Advanced View")

    id_cols = [
        "team_name",
        "headshot_url",
        "name",
    ]

    id_cols_advanced = [
        "team",
        "position",
    ]

    cost_cols = [
        "keeper_cost",
        "eligible",
    ]

    cost_cols_advanced = [
        "years_eligible",
    ]

    adp_cols: list[str] = []

    adp_cols_advanced = [
        "ecr_1qb",
        "value_1qb",
        "ecr_2qb",
        "value_2qb",
        "ecr_pos",
    ]

    if is_advanced:
        show_columns = id_cols + id_cols_advanced + cost_cols + cost_cols_advanced + adp_cols + adp_cols_advanced
    else:
        show_columns = id_cols + cost_cols + adp_cols

    filtered_data = filter_dataframe(data.loc[teams_filter, show_columns])
    st.dataframe(
        filtered_data,
        hide_index=True,
        height=35 * (len(filtered_data) + 1) + 12,
        use_container_width=True,
        column_config={
            "team_name": st.column_config.TextColumn(label="League Team", help="Name of fantasy League team."),
            "headshot_url": st.column_config.ImageColumn(label="", help="Player image"),
            "name": st.column_config.TextColumn(label="Name", help="Player's name"),
            "team": st.column_config.TextColumn(label="NFL Team"),
            "position": st.column_config.TextColumn(label="Position", help="Player's position"),
            "keeper_cost": st.column_config.NumberColumn(
                label="Keeper Cost", help="Draft Round Cost to keep player.  See Rules for details."
            ),
            "eligible": st.column_config.TextColumn(label="Eligible", help="Is player eligible to be keeper?"),
            "years_eligible": st.column_config.TextColumn(
                label="Years Eligible",
                help="Number of further consecutive seasons player can be kept (subject to maximum of 2)",
            ),
            "ecr_1qb": st.column_config.NumberColumn(
                label="ECR 1QB", help="Player's average Expert Consensus Rank (ECR) for 1 QB League"
            ),
            "value_1qb": st.column_config.NumberColumn(
                label="Value 1QB", help="Approx. number of draft picks of keeper value vs ECR 1QB"
            ),
            "ecr_2qb": st.column_config.NumberColumn(
                label="ECR 2QB", help="Player's average Expert Consensus Rank (ECR) for 2 QB League"
            ),
            "value_2qb": st.column_config.NumberColumn(
                label="Value 2QB", help="Approx. number of draft picks of keeper value vs ECR 2QB"
            ),
            "ecr_pos": st.column_config.NumberColumn(
                label="ECR Position", help="Player's average Expert Consensus Rank (ECR) at the player's position"
            ),
        },
    )


def get_keeper_app():
    keeper_title = f"{LEAGUE_NAME} Keeper Options"
    st.set_page_config(page_title=keeper_title, page_icon=DEFAULT_ICON, layout="wide")
    st.title(keeper_title)
    data, teams_list = load_data()

    with st.container():
        filtered_keeper_dataframe(data, teams_list)


if __name__ == "__main__":
    get_keeper_app()