from datetime import datetime
from collections import Counter

import pandas as pd
from sqlalchemy import select

from data.database import Base, dal, project_variables
import util.drawing as drawutil
import util.environment as envutil

project_variables = envutil.load_environment_variables()


def load_dataframe(
        table_name: str, start_date: datetime | None = None, end_date: datetime | None = None
    ) -> pd.DataFrame:
    """connect to the database and load game data into a pandas dataframe"""

    dal.connect()
    session = dal.Session()

    game_dict = drawutil.check_table_name(table_name=table_name)
    if game_dict:
        target_table = Base.metadata.tables.get(game_dict['db_table_name'])
        if start_date is not None and end_date is not None:
            sql_statement = (select(target_table)
                .where(target_table.columns.draw_date >= start_date)
                .where(target_table.columns.draw_date <= end_date))
        elif start_date is not None and end_date is None:
            sql_statement = (select(target_table).where(target_table.columns.draw_date >= start_date))
        elif start_date is None and end_date is not None:
            sql_statement = (select(target_table).where(target_table.columns.draw_date <= end_date))
        else:
            sql_statement = select(target_table)

        return pd.read_sql(sql_statement, session.bind)
    else:
        raise ValueError('An invalid table name was provided')


def get_most_common_number(
        data_frame: pd.DataFrame,
        columns: list | None = None,
        top: int = 1
    ) -> list[int]:
    if columns is None:
        columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
    flat_numbers = data_frame[columns].values.flatten()
    counts = Counter(flat_numbers)
    return [int(num) for num, _ in counts.most_common(top)]


def get_least_common_number(
        data_frame: pd.DataFrame,
        columns: list | None = None,
        bottom: int = 1
) -> list[int]:
    if columns is None:
        columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
    flat_numbers = data_frame[columns].values.flatten()
    counts = Counter(flat_numbers)
    return list(reversed([int(num) for num, _ in counts.most_common()[-bottom:]]))