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

# Load BankTransaction.xls
bank_transaction_df_all = pd.read_excel('Abankstatementphonepe10-06.xlsx')
print(bank_transaction_df_all.columns)
# Load momentsPay.csv
moments_pay_df_all = pd.read_csv('Amomentpay11-06.csv')

# Define column mapping
column_mapping = {
    'AUTH_AMOUNT': ['Transaction Amount','AUTH_AMOUNT', 'TransactionAmount', 'Amount','DOMESTIC AMT'],
    'RRN_NO': ['Txn ref no.(RRN)','PhonePeReferenceId'],
}

# Create copies of the original data frames
bank_transaction_df = bank_transaction_df_all.copy()
moments_pay_df = moments_pay_df_all.copy()

#moments_pay_df['card_num'] = moments_pay_df['card_num'].astype(str).replace('\.0$', '', regex=True)
moments_pay_df['txn_identifier'] = moments_pay_df['email'].astype(str).replace('\.0$', '', regex=True)
moments_pay_df['total_amount'] = moments_pay_df['total_amount'].astype(str).replace('\.0$', '', regex=True) # amount decimal take consider
moments_pay_df['approval_code'] = moments_pay_df['approval_code'].astype(str).replace('\.0$', '', regex=True)

# Map columns based on the configuration
bank_transaction_df_mapped = bank_transaction_df.copy()
for expected_col, possible_cols in column_mapping.items():
    actual_col = next((col for col in possible_cols if col in bank_transaction_df.columns), None)
    if actual_col:
        bank_transaction_df_mapped[expected_col] = bank_transaction_df[actual_col]
        print(bank_transaction_df_mapped[expected_col])

# Convert relevant columns to a common data type (string)
#bank_transaction_df_mapped['CARDNBR'] = bank_transaction_df_mapped['CARDNBR'].astype(str).replace('\.0$', '', regex=True)
#bank_transaction_df_mapped['TERMINAL_NO'] = bank_transaction_df_mapped['TERMINAL_NO'].astype(str).replace('\.0$', '', regex=True)
bank_transaction_df_mapped['AUTH_AMOUNT'] = bank_transaction_df_mapped['AUTH_AMOUNT'].astype(str).replace('\.0$', '', regex=True)
bank_transaction_df_mapped['RRN_NO'] = bank_transaction_df_mapped['RRN_NO'].astype(str).replace('\.0$', '', regex=True)


# Specify the mapping between fields excluding 'processing_id' and 'transaction_id'
field_mapping = {
   # 'CARDNBR': 'card_num',
   # 'TERMINAL_NO': 'terminal_id',
    'AUTH_AMOUNT': 'total_amount',
    'RRN_NO': 'approval_code',
}

print(moments_pay_df[[ 'transaction_id'] + list(field_mapping.values())])
#print(bank_transaction_df_mapped[['CARDNBR','TERMINAL_NO','AUTH_AMOUNT','RRN_NO']])

# Merge dataframes on the specified fields
merged_df = pd.merge(bank_transaction_df_mapped, moments_pay_df[['transaction_id','txn_identifier'] + list(field_mapping.values())], left_on=list(field_mapping.keys()), right_on=list(field_mapping.values()), how='left')

# Create a new column 'Matched' based on the conditions
merged_df['MomentsPay Matched'] = merged_df.apply(lambda row: 'YES' if not pd.isnull(row['transaction_id']) else 'NO', axis=1)

# Drop unnecessary columns
#merged_df = merged_df.dropna(subset=['CARDNBR'])
#merged_df = merged_df.drop(list(field_mapping.values()),axis=1)
print(merged_df)

# Write the modified DataFrame to a new Excel file
merged_df.to_excel('MomentsPayAnderson_Bank_new1.xlsx', index=False)