from __future__ import print_function
import pandas as pd
from mailmerge import MailMerge
from datetime import date

#get all csv files in the current directory
#import os
#path = os.getcwd()
#extension = '.csv'
#for root, dirs_list, files_list in os.walk(path):
#    for file_name in files_list:
#        if os.path.splitext(file_name)[-1] == extension:
#            file_name_path = os.path.join(root, file_name)
#            print(file_name)
#            print(file_name_path)   # This is the full path of the filter file

# this is the extension you want to detect
#extension = '.csv'
# Define the templates - assumes they are in the same directory as the code
template_1 = "jd_template.docx"

# Show a simple example
csv_path = 'jd.csv'

try:
    # do something
    df=pd.read_csv(csv_path)
    for idx,row in df.iterrows():
        global data
        data={
            '[name_english]': row['name_english'],
            '[name]': row['name'],
            '[present_address]': row['present_address'],
            '[phone]': row['phone'],
            '[id_card_no]': row['id_card_no'],
            '[date_of_birth]': row['date_of_birth'],
            '[permenant_address]': row['permenant_address'],
            '[emergency_contact_name]': row['emergency_contact_name'],
            '[emergency_contact_phone]': row['emergency_contact_phone'],
            '[job_no]': row['job_no'],
            '[job_name_and_rank]': row['job_name_and_rank'],
            '[job_purpose]': row['job_purpose'],
            '[unit]': row['unit'],
            '[jd_date]': row['jd_date'],
            '[basic_salary]': row['basic_salary'],
            '[service_allowance]': row['service_allowance'],
            '[supporting_co_allowance]': row['supporting_co_allowance'],
            '[name_2]': row['name_2'],
            '[job_title]': row['job_title'],
            '[sign_date]': row['sign_date']
        }
        print("Data Row is:" + data['[name]'])
        document_1 = MailMerge(template_1)
        print("Fields included in {}: {}".format(template_1,
                                                document_1.get_merge_fields()))
        # Merge in the values
        document_1.merge(
            name=data['[name]'],
            present_address=data['[present_address]'],
            phone=str(data['[phone]']),
            id_card_no=data['[id_card_no]'],
            date_of_birth=data['[date_of_birth]'],
            permenant_address=data['[permenant_address]'],
            emergency_contact_name=data['[emergency_contact_name]'],
            emergency_contact_phone=str(data['[emergency_contact_phone]']),
            job_no=str(data['[job_no]']),
            job_name_and_rank=data['[job_name_and_rank]'],
            job_purpose=data['[job_purpose]'],
            unit=data['[unit]'],
            jd_date=data['[jd_date]'],
            basic_salary=str(data['[basic_salary]']),
            service_allowance=str(data['[service_allowance]']),
            supporting_co_allowance=str(data['[supporting_co_allowance]']),
            name_2=data['[name_2]'],
            job_title=data['[job_title]'],
            sign_date=data['[sign_date]'],
        )

        csv2_path =  data['[name_english]'] + '.csv'
        df2=pd.read_csv(csv2_path)
        jd_tasks= pd.read_csv(csv2_path).to_dict('records')

        for k in jd_tasks:
            k['job_task_marks'] = str(k['job_task_marks'])

        document_1.merge_rows('job_task_marks', jd_tasks)
        # Save the document as example 1
        document_1.write(data['[name_english]'] + '_jd.docx')

except Exception as e:
    # handle it
    raise Exception("Kommis kamah nibei vege. " + str(e))