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["Valutadatum"], '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()
#
#     return result
#
#
# def _compute_net_income(df: pd.DataFrame) -> pd.DataFrame:
#     stripped_df = pd.DataFrame({'t': df["Valutadatum"], 'value': df["Betrag"]})
#
#     stripped_df.index = stripped_df['t']
#     gb = stripped_df.groupby(pd.Grouper(freq='M'))
#
#     result = gb["value"].sum().reset_index()
#     return result
#
#
# def _compute_category_overview(df: pd.DataFrame) -> pd.DataFrame:
#     categories = ["Social life", "Other", "Food", "Hobbies",
#                   "Rent \\& Utilities", "Education", "Transportation"]
#     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["Valutadatum"],
#                          'value': df["Saldo nach Buchung"]})
#
#
# def parse_statement(filename: str) -> types.ReportData:
#     df = pd.read_csv(filename, delimiter=';', decimal=",")
#     df["Valutadatum"] = pd.to_datetime(df["Valutadatum"], 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()


def get_stripped_statement(filename: str) -> pd.DataFrame:
    df = pd.read_csv(filename, delimiter=';', decimal=",")
    df["Valutadatum"] = pd.to_datetime(df["Valutadatum"], format='%d.%m.%Y')

    result = pd.DataFrame({'t': df["Valutadatum"],
                           'other party': df["Name Zahlungsbeteiligter"],
                           'value': df["Betrag"],
                           'balance afterwards': df["Saldo nach Buchung"],
                           'description': df["Buchungstext"],
                           'purpose': df["Verwendungszweck"]
                           })
    result['category'] = [''] * len(result.index)

    return result