import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import PatternFill

# Load MomentsPay_Bank_new.xlsx
moments_pay_bank_df = pd.read_excel('hdfccard24-05.xlsx')

# Load Transaction details as on 14.12.2023
transaction_details_df = pd.read_excel('PineLabTransaction24-05.xlsx')

# Convert relevant columns to a common data type (string)
transaction_details_df['Approval Code'] = transaction_details_df['Approval Code'].astype(str).replace(r'\.0$', '', regex=True)
transaction_details_df['Amount'] = transaction_details_df['Amount'].astype(str).replace(r'\.0$', '', regex=True)

moments_pay_bank_df['APPROV CODE'] = moments_pay_bank_df['APPROV CODE'].astype(str).replace(r'\.0$', '', regex=True)
moments_pay_bank_df['INTNL AMT'] = moments_pay_bank_df['INTNL AMT'].astype(str).replace(r'\.0$', '', regex=True)
moments_pay_bank_df['DOMESTIC AMT'] = moments_pay_bank_df['DOMESTIC AMT'].astype(str).replace(r'\.0$', '', regex=True)
moments_pay_bank_df['TRANSACTION AMOUNT'] = moments_pay_bank_df.apply(lambda row: row['DOMESTIC AMT'] if row['INTNL AMT'] == '0' else row['INTNL AMT'], axis=1)

# Specify the mapping between fields excluding 'processing_id' and 'transaction_id'
field_mapping = {'Approval Code': 'APPROV CODE','Amount':'TRANSACTION AMOUNT'}

# Filter only card transactions
transaction_details_df = transaction_details_df[transaction_details_df['Payment Mode'] == 'CARD']
#moments_pay_bank_df = moments_pay_bank_df[moments_pay_bank_df['TRANSACTION TYPE'] == 'CARD']

# Remove duplicates from moments_pay_bank_df based on 'approval_code' and 'total_amount'
#moments_pay_bank_df_dedup = moments_pay_bank_df.drop_duplicates(subset=['APPROV CODE'], keep='first')

# Merge dataframes on the specified fields
merged_df = pd.merge(transaction_details_df, moments_pay_bank_df[list(field_mapping.values())],
                     left_on=list(field_mapping.keys()),
                     right_on=list(field_mapping.values()),
                     how='left')
unmerged_df = pd.merge(transaction_details_df, moments_pay_bank_df[list(field_mapping.values())],
                       left_on=list(field_mapping.keys()),
                       right_on=list(field_mapping.values()),
                       how='outer')

equal_records = merged_df[~merged_df['APPROV CODE'].isnull()]
equal_records.insert(0, 'S.No.', range(1, len(equal_records) + 1))
equal_records['momentpay matched'] = 'YES'

# Filter unmatched card transactions
unequal_records = unmerged_df[unmerged_df['APPROV CODE'].isnull() & (unmerged_df['Payment Mode'] == 'CARD')]
unequal_records.insert(0, 'S.No.', range(1, len(unequal_records) + 1))
unequal_records['momentpay matched'] = 'NO'

# Function to create or overwrite an Excel sheet
def create_or_overwrite_sheet(sheet_name, data):
    with pd.ExcelWriter('Rela-HISBANK24-05.xlsx', mode='a', engine='openpyxl', if_sheet_exists='replace') as writer:
        data.to_excel(writer, sheet_name=sheet_name, index=False)

# Generate sheet names
sheet_name_matched = 'PineBankcard-Matched'
sheet_name_unmatched = 'PineBankcard-Unmatched'
create_or_overwrite_sheet(sheet_name_matched, equal_records)
create_or_overwrite_sheet(sheet_name_unmatched, unequal_records)

wb = load_workbook('Rela-HISBANK24-05.xlsx')
Summary_Sheet = wb['Summary']

transaction_date = moments_pay_bank_df['TRANS DATE'].mode()[0]
print(transaction_date)

# Update summary sheet with total card transaction details
num_rows = len(transaction_details_df)
Summary_Sheet['E17'] = num_rows

total_amount = transaction_details_df['Amount'].astype(float).sum()
Summary_Sheet['F17'] = f"₹{total_amount:,.2f}"

# Update summary sheet with matched card transaction details
HIS_Matched_df = pd.read_excel('Rela-HISBANK24-05.xlsx', sheet_name='PineBankcard-Matched')
num_rows = len(HIS_Matched_df)
Summary_Sheet['G17'] = num_rows

total_amount = HIS_Matched_df['Amount'].astype(float).sum()
Summary_Sheet['H17'] = f"₹{total_amount:,.2f}"

# Update summary sheet with unmatched card transaction details
HIS_UnMatched_df = pd.read_excel('Rela-HISBANK24-05.xlsx', sheet_name='PineBankcard-Unmatched')
num_rows = len(HIS_UnMatched_df)
Summary_Sheet['I17'] = num_rows

total_amount = HIS_UnMatched_df['Amount'].astype(float).sum()
Summary_Sheet['J17'] = f"₹{total_amount:,.2f}"

# Define the color for the header
header_fill = PatternFill(fgColor='1274bd', fill_type='solid')

for sheet_name in ['PineBankcard-Matched', 'PineBankcard-Unmatched']:
    worksheet = wb[sheet_name]

    # Apply style to the header row
    for row in worksheet.iter_rows(min_row=1, max_row=1):
        for cell in row:
            cell.fill = header_fill

# Save the workbook
wb.save('Rela-HISBANK24-05.xlsx')

# Save matched and unmatched records to separate files
#def save_to_new_excel_file(file_name, sheet_name, data):
#    with pd.ExcelWriter(file_name, engine='openpyxl') as writer:
#        data.to_excel(writer, sheet_name=sheet_name, index=False)

#save_to_new_excel_file('PineBankcard_matched.xlsx', 'Matched', equal_records)
#save_to_new_excel_file('PineBankcard_unmatched.xlsx', 'Unmatched', unequal_records)