import typing
import pandas as pd
from banking_breakdown import types
import json
import re
import numpy as np


def _read_regex_dict(regex_file: str = "res/category_regexes.json"):
    with open(regex_file, 'r') as f:
        return json.load(f)


def _tag_with_category(df: pd.DataFrame) -> pd.DataFrame:
    regex_dict = _read_regex_dict()

    return df


def _compute_total_balance(df: pd.DataFrame) -> pd.DataFrame:
    stripped_df = pd.DataFrame(
        {'t': df["Buchungstag"], 'value': df["Saldo nach Buchung"]})

    stripped_df.index = stripped_df['t']
    gb = stripped_df.groupby(pd.Grouper(freq='M'))

    result = gb.tail(1)['value'].reset_index()
    #result['t'] = result['t'].apply(lambda dt: dt.replace(day=1))

    return result


def _compute_net_income(df: pd.DataFrame) -> pd.DataFrame:
    stripped_df = pd.DataFrame({'t': df["Buchungstag"], 'value': df["Betrag"]})

    result = stripped_df.resample(rule='M', on="t").sum().reset_index()
    #result['t'] = result['t'].apply(lambda dt: dt.replace(day=1))

    return result


def _compute_category_overview(df: pd.DataFrame) -> pd.DataFrame:
    categories = ["A", "B", "C", "D", "E", "F", "G"]
    values = np.array([10, 12, 53, 12, 90, 23, 32])
    values = values / values.sum() * 100
    values = np.round(values, decimals=1)
    values[-1] += 100 - np.sum(values)

    category_overview_df = pd.DataFrame(
        {"category": categories, "value": values})

    return category_overview_df


def _compute_detailed_balance(df: pd.DataFrame) -> pd.DataFrame:
    return pd.DataFrame({'t': df["Buchungstag"],
                         'value': df["Saldo nach Buchung"]})


def parse_statement(filename: str) -> types.ReportData:
    df = pd.read_csv(filename, delimiter=';', decimal=",")
    df["Buchungstag"] = pd.to_datetime(df["Buchungstag"], format='%d.%m.%Y')

    category_overview_df = _compute_category_overview(df)
    total_balance_df = _compute_total_balance(df)
    net_income_df = _compute_net_income(df)
    detailed_balance_df = _compute_detailed_balance(df)

    return types.ReportData(category_overview_df,
                            net_income_df,
                            total_balance_df,
                            detailed_balance_df)


def main():
    report_data = parse_statement("../res/banking_statement_2023.csv")


if __name__ == "__main__":
    main()