from io import BytesIO
import os
from typing import List, Tuple
from openpyxl import Workbook
from openpyxl.styles import Font
from openpyxl.styles import Alignment
from openpyxl.styles import numbers
from openpyxl.styles.borders import Border, Side
import streamlit as st
import pandas as pd
import numpy as np
from data.cli_dropbox import dropbox_download_file, dropbox_upload_bytefile

from .dataframes import complete_affaires, complete_arrets, complete_intervenants, complete_supplements, complete_vehicules, merge_clients, merge_affaires, merge_intervenants_affaires,cast_specifics_to_str

def check_paths(paths: List[str]) -> bool:
    files = []
    for path in paths:
        if not os.path.exists(path) or not os.path.isfile(path):
            files.append(path)
    return files
    
def load_excels(datapath: str, excel_sources: List[dict])  -> pd.DataFrame:
    data = {}
    for key, value in excel_sources.items():
        data[key] = pd.read_excel(os.path.join(datapath, excel_sources[key]['path']),
                           sheet_name=excel_sources[key]['sheet_name'],
                            usecols=excel_sources[key]['columns'],
                            nrows=excel_sources[key]['rows'],
                            skiprows=excel_sources[key]['skiprows'],
                        )
    return data

@st.cache_data
def load_transform_data(datapath: str, excel_sources: List[dict]) -> pd.DataFrame:
    not_files = check_paths([os.path.join(datapath, excel['path']) for excel in excel_sources.values()])
    if len(not_files):
        st.error(f'Erreur: une partie de la base de données n\'est pas accessible. {not_files}')
        return

    data = load_excels(datapath, excel_sources)
    merge_clients(data)
    merge_affaires(data)
    merge_intervenants_affaires(data)

    complete_vehicules(data)
    complete_supplements(data)
    complete_intervenants(data)
    complete_affaires(data)
    complete_arrets(data)
    cast_specifics_to_str(data)
    return data


def filter_multiple_conditions_data(df, filters):
    filtered_df = df[df[filters.keys()].isin(filters.values()).all(axis=1)]
    return filtered_df

def draw_border(sheet, start_cell: Tuple[int, int], end_cell: Tuple[int, int]):
    # Define the border style
    border_style = Border(left=Side(style='thin'),
                      right=Side(style='thin'),
                      top=Side(style='thin'),
                      bottom=Side(style='thin'))

    # Define the range of cells to apply borders to
    start_cell = sheet.cell(row=start_cell[0], column=start_cell[1])
    end_cell = sheet.cell(row=end_cell[0] , column=end_cell[1])
    cell_range = '{}:{}'.format(start_cell.coordinate, end_cell.coordinate)

    # Apply borders to the range of cells
    for row in sheet[cell_range]:
        for cell in row:
            cell.border = border_style

def get_fit_totals(dataframe):
    column_sums = {}
    for column in dataframe.columns:
        if dataframe[column].dtype in [int, float] and not np.isnan(dataframe[column]).all():
            column_sums[column] = dataframe[column].sum()
    column_sums['rows'] = dataframe.shape[0]
    column_sums['worked_hours'] = column_sums['H.\njour'] + column_sums['H.\nnuit (1)']
    return column_sums

def load_fit(datapath: str, intervenant: str, year: str, month: str, week: str):
    filename = f'{intervenant}_{year}_{month}_{week}_FIT.xlsx'
    if dropbox_download_file(f'/SEC_IND_GTP2023_OUTPUT/FIT/{intervenant}/{year}/{month}/{week}/{filename}', os.path.join(datapath, filename)):
        #TODO get file from dropbox
        if os.path.exists(os.path.join(datapath, filename)) and os.path.isfile(os.path.join(datapath, filename)):
            data = pd.read_excel(os.path.join(datapath, filename), sheet_name='Sheet', skiprows=6, nrows=10)
            data.dropna(axis=0, how='all', inplace=True)
            totals = get_fit_totals(data)

            if 'fit' not in st.session_state.keys():
                st.session_state['fit'] = {}
            
            if intervenant not in st.session_state['fit'].keys():
                st.session_state['fit'][intervenant] = {}
            if year not in st.session_state['fit'][intervenant].keys():
                st.session_state['fit'][intervenant][year] = {}
            if month not in st.session_state['fit'][intervenant][year].keys():
                st.session_state['fit'][intervenant][year][month] = {}
            if week not in st.session_state['fit'][intervenant][year][month].keys():
                st.session_state['fit'][intervenant][year][month][week] = {}
            st.session_state['fit'][intervenant][year][month][week] = {
                'data': data,
                'totals': totals
            }

            return data
    print('error loading fit')
    return None

async def update_society_fit(dropbox_datapath: str, form: dict):
    society = form['prestataire']
    year = form['year']
    month = form['month']
    week = form['week']
    dropbox_path = f'{dropbox_datapath}/SOCIETE/{society}/{year}/{month}/{week}'

    filename = f'{society}_{year}_{month}_{week}_FIT.csv'
    fit_df = pd.DataFrame([form])

    fit = dropbox_download_file(os.path.join(dropbox_path, filename), '', False)
    if fit:
        fit = pd.read_csv(BytesIO(fit), index_col=0)
        fit_df = pd.concat([fit, fit_df], ignore_index=True)

    csv_data = BytesIO()
    fit_df.to_csv(csv_data, index = False)
    dropbox_upload_bytefile(dropbox_data_path=dropbox_path, dropbox_file_name=filename, bytes=csv_data)
    return fit_df


async def update_society_payroll(dropbox_datapath: str, form: dict):
    prestataire = form['Prestataire']
    fournisseur = form['Fournisseur']
    year = form['year']
    month = form['month']
    dropbox_path = f'{dropbox_datapath}/PRESTATIONS/{prestataire}/{year}/{month}'

    filename = f'{prestataire}_{fournisseur}_{year}_{month}_PRESTATIONS_CROISEES.xlsx'
    payroll_df = pd.DataFrame([form])

    payroll = dropbox_download_file(os.path.join(dropbox_path, filename), '', False)
    if payroll:
        payroll = pd.read_excel(BytesIO(payroll))
        payroll_df = pd.concat([payroll, payroll_df], ignore_index=True)

    excel_data = BytesIO()
    payroll_df.to_excel(excel_data, index = False)
    dropbox_upload_bytefile(dropbox_data_path=dropbox_path, dropbox_file_name=filename, bytes=excel_data)
    return payroll_df

async def update_historical_week(dropbox_datapath: str, form: dict):
    intervenant = form['intervenant']
    year = form['year']
    month = form['month']
    week = form['week']
    dropbox_path = f'{dropbox_datapath}/FIT/{intervenant}/{year}/{month}/{week}'

    historic_df = pd.DataFrame([form])

    historic = dropbox_download_file(dropbox_path + '/historique.xlsx', '', False)
    if historic:
        historic = pd.read_excel(historic)
        historic_df = pd.concat([historic, historic_df], ignore_index=True)

    excel_data = BytesIO()
    historic_df.to_excel(excel_data, index = False)
    dropbox_upload_bytefile(dropbox_data_path=dropbox_path, dropbox_file_name='historique.xlsx', bytes=excel_data)
    return historic_df

async def update_monthly_payroll(dropbox_datapath: str, payroll_dict: dict, year: str, month: str, week: str):

    dropbox_path = f'{dropbox_datapath}/PAYES'
    # dropbox_path = dropbox_datapath
    nom = payroll_dict['Nom']
    prenom = payroll_dict['Prénom']
    payroll_df = pd.DataFrame([payroll_dict])

    payroll = dropbox_download_file(dropbox_path + f'/tableau_prepaye_{year}_{month}.xlsx', '', False)
    if payroll:
        payroll = pd.read_excel(payroll)
        intervenant_rows = payroll[(payroll['Nom'] == nom) & (payroll['Prénom'] == prenom)]
        # print(len(intervenant_rows))
        if len(intervenant_rows):
            current_week_row = payroll[(payroll['Nom'] == nom) & (payroll_df['Prénom'] == prenom) & (payroll['Semaine'] == f'{year}-s{week}')]
            # print(len(current_week_row))
            if len(current_week_row):
                payroll.iloc[current_week_row.index] = payroll_df.loc[0]
                payroll_df = payroll
            else:
                payroll_df = pd.concat([payroll, payroll_df], ignore_index=True)
        else:
            payroll_df = pd.concat([payroll, payroll_df], ignore_index=True)
    rows_for_total = payroll_df[(payroll_df['Nom'] == nom) & (payroll_df['Prénom'] == prenom) & (payroll_df['Semaine'].str.contains(f'{year}-s'))]
    total = pd.DataFrame([rows_for_total.drop(columns=['Nom', 'Prénom', 'Semaine']).sum()])
    total['Nom'] = nom
    total['Prénom'] = prenom
    total['Semaine'] = 'TOTAL'
    all_but_total = payroll_df[~((payroll_df['Nom'] == nom) & (payroll_df['Prénom'] == prenom) & (payroll_df['Semaine'].str.contains('TOTAL')))]
    # print(all_but_total)
    payroll_df = pd.concat([total, all_but_total], ignore_index=True, axis = 0)
    # print(payroll_df)
    payroll_df = payroll_df.sort_values(by=['Nom', 'Prénom', 'Semaine'])
    column_order = payroll_df.columns[-3:].tolist() + payroll_df.columns[:-3].tolist()
    payroll_df = payroll_df[column_order]   
    excel_data = BytesIO()
    payroll_df.to_excel(excel_data, index = False)
    dropbox_upload_bytefile(dropbox_data_path=dropbox_path, dropbox_file_name=f'tableau_prepaye_{year}_{month}.xlsx', bytes=excel_data)
    return payroll_df

def write_excel_fit(datapath: str, filename: str, data, starting_row = 7):
    workbook = Workbook()
    sheet = workbook.active

    
    sheet.column_dimensions['A'].width = 60
    sheet.column_dimensions['B'].width = 40
    sheet.column_dimensions['C'].width = 80
    sheet.column_dimensions['D'].width = 40
    sheet.column_dimensions['E'].width = 20
    sheet.column_dimensions['K'].width = 60
    sheet.column_dimensions['L'].width = 40
    sheet.column_dimensions['M'].width = 40
    sheet.column_dimensions['O'].width = 20
    sheet.column_dimensions['P'].width = 40
    sheet.column_dimensions['Q'].width = 40
    sheet.column_dimensions['R'].width = 40
    sheet.column_dimensions['S'].width = 20
    sheet.row_dimensions[29].height = 30
    sheet.row_dimensions[31].height = 40

    sheet['A1'] = 'SECMI'

    sheet['D1'] = 'FICHE D\'INTERVENTION ET DE TEMPS (FIT)'

    sheet['A3'] = f'Intervenant: {data["intervenant"]}'
    draw_border(sheet, (3, 1), (3, 1))

    sheet.merge_cells('M1:N1')
    sheet['M1'] = f'Année: {data["year"]}'
    draw_border(sheet, (1, 13), (1, 13))
    
    sheet.merge_cells('M2:N2')
    sheet['M2'] = f'Semaine: {data["week"]}'
    draw_border(sheet, (2, 13), (2, 13))


    sheet['A18'] = '(1)  travail effectué entre 21h00 et 06h00'

    sheet.merge_cells('A19:S22')
    sheet['A19'] = 'Commentaires SECMI:'
    draw_border(sheet, (19, 1), (22, 19))

    sheet.merge_cells('A23:S26')
    sheet['A23'] = 'Commentaires Client:'
    draw_border(sheet, (23, 1), (26, 19))

    sheet.merge_cells('A30:D30')
    sheet['A30'] = 'Signature client:'
    draw_border(sheet, (30, 1), (30, 4))
    sheet.merge_cells('A31:D32')
    draw_border(sheet, (31, 1), (32, 4))

    
    sheet['E30'] = 'Note\n(de 0 à 10)'
    draw_border(sheet, (30, 5), (30, 5))
    sheet.merge_cells('E31:E32')
    draw_border(sheet, (31, 5), (32, 5))

    sheet['L30'] = 'Signature chargé d\'affaire:'
    draw_border(sheet, (30, 12), (30, 12))
    sheet.merge_cells('L31:L32')
    draw_border(sheet, (31, 12), (32, 12))

    sheet['M30'] = 'Signature intervenant:'
    draw_border(sheet, (30, 13), (30, 13))
    sheet.merge_cells('M31:M32')
    draw_border(sheet, (31, 13), (32, 13))
    

    sheet.merge_cells('A33:T33')
    sheet.merge_cells('A34:T34')
    draw_border(sheet, (33, 1), (34, 19))
    sheet['A33'] = 'Service Administratif'
    sheet['A34'] = 'Tel: +33 6 02 14 55 16  - Email : compta.grh@secmi-addax.com'
    # Define the starting row for writing the dictionary
    
    draw_border(sheet, (starting_row, 1), (starting_row + 10, 19))

    draw_border(sheet, (starting_row - 2, 4), (starting_row -2, 19))


    sheet.cell(row=starting_row - 2, column=4, value='TOTAUX (en heure)')
    # Write the dictionary to the sheet starting from the specified row
    header = list(data['data'].keys())
    for col_i, key in enumerate(header):
        sheet.cell(row=starting_row, column=col_i + 1, value=key)
        if key in data['totals'].keys():
            sheet.cell(row=starting_row - 2, column=col_i + 1, value=data['totals'][key])

    for cell in sheet[starting_row - 2]:
        cell.font = Font(bold=True)
    for cell in sheet[starting_row]:
        cell.font = Font(bold=True)

    starting_row += 1
    for col_i, key in enumerate(data['data'].keys()):
            values = data['data'][key]
            for j, value in enumerate(values):

                sheet.cell(row=starting_row + j, column=col_i + 1, value=value)

    alignment = Alignment(horizontal='center', vertical='center')
    # Set the decimal format
    decimal_format = numbers.FORMAT_NUMBER_00

    for row in sheet.iter_rows():
        for cell in row:
            cell.alignment = alignment
            cell.number_format = decimal_format
   
    sheet['A1'].font = Font(bold=True, underline='single', size=11)
    sheet['D1'].font = Font(bold=True, underline='single', size=11)
    sheet['A19'].font = Font(bold=True, underline='single')
    sheet['A19'].alignment = Alignment(horizontal='left', vertical='top')
    sheet['A23'].font = Font(bold=True, underline='single')
    sheet['A23'].alignment = Alignment(horizontal='left', vertical='top')
    sheet['A33'].font = Font(bold=True)
    sheet['A33'].alignment = Alignment(horizontal='center')
    sheet['A34'].font = Font(bold=True)
    sheet['A34'].alignment = Alignment(horizontal='center')

    workbook.save(os.path.join(datapath, filename))