'''Created 9 Jun 2023 Levi'''

# import re
from datetime import date
import pandas as pd
from jinja2 import (Environment, PackageLoader)
from pymt940.utils import (dig_it, re_check, get_partners, get_last_month)
from . import private

templates = Environment(loader=PackageLoader('pymt940', 'templates'))
template = templates.get_template('body_incomes.txt')

df_dict = get_partners(private.MZK)
fiscal_codes = df_dict['CodFiscal']
mentor_names = df_dict['Denumire']
fiscal_code_dict = {k: v for k, v in zip(mentor_names, fiscal_codes)}
# parts_df = pd.DataFrame(df_dict)
payments_df = pd.read_csv(private.MZKCSV, skiprows=14, keep_default_na=False).astype({'Balance': 'object'})
payments_df['Credit'].replace(' ', '', regex=True, inplace=True)
payments_df['Debit'].replace(' ', '', regex=True, inplace=True)
income_df = payments_df.copy()
# payments_df = payments_df.loc[payments_df['Debit'] != '']
income_df = income_df.loc[income_df['Credit'] != '']

income_df['Amount'] = income_df['Credit'].map(str).apply(dig_it)
income_df['Name'] = income_df['Description'].apply(re_check)
income_df['Transaction details'] = income_df['Description']
income_df['day'] = income_df['Transaction date'].str.slice(0, 2)
# item_count = income_df['Credit'].count()
# incas_list = list(range(item_count))
# names = [name for name in income_df['Name']]
unique_count = income_df['day'].nunique()
doc_list = list(range(1, unique_count + 1))
days = income_df['day'].unique().tolist()
trans = []
counts = []
cl_names = []
obs = []
for day in days:
    values = income_df.loc[income_df['day'] == day, 'Amount'].tolist()
    trans.append(values)
trans_dict = {k: v for k, v in zip(days, trans)}

for day in days:
    values = income_df.loc[income_df['day'] == day, 'Name'].tolist()
    for i in range(len(values)):
        if values[i] not in mentor_names:
            values[i] = ''
    cl_names.append(values)
names_dict = {k: v for k, v in zip(days, cl_names)}

for day in days:
    values = income_df.loc[income_df['day'] == day, 'Transaction details'].tolist()
    obs.append(values)
obs_dict = {k: v for k, v in zip(days, obs)}

for day in days:
    value_counts = income_df['day'].value_counts()
    count_of_value = value_counts[day]
    counts.append(count_of_value)

# payments = income_df['Amount'].tolist()
# obs = income_df['Transaction details'].tolist()
# selected_value = income_df.loc[income_df['Name'] == 'BLOOM CREATIVE ART SRL', 'day'].iloc[0]

data = {'AnLucru': date.today().year,
        'LunaLucru': get_last_month(0),
        'TotalDocumente': unique_count,
        'documents': doc_list,
        'days': days,
        'TotalIncasari': counts,
        'incasari': counts,
        'clients': names_dict,
        'cods': fiscal_code_dict,
        'amount': trans_dict,
        'cont': '411.01',
        'std_client': 'MOZAIK CONSULTING SRL',
        'obs': obs_dict
        }

if __name__ == '__main__':
    # mentor_txt()
    # print(fiscal_code_dict['REMCO SRL'])
    # print(days)
    output = template.render(data)
    print(output)
    output_file = 'mentor_incasari.txt'
    with open(output_file, 'w') as text:
        text.write(output)
    # remove_indentations(output_file)
    # print(item_count)
    # print(income_df['day'])