app.py

from sepa import parser
import re
import pandas as pd
import gradio as gr
import numpy as np

def full_function(xml_file):

    # Utility function to remove additional namespaces from the XML
    def strip_namespace(xml):
        return re.sub(' xmlns="[^"]+"', '', xml, count=1)

    # Read file
    with open(xml_file, 'r') as f:
        input_data = f.read()

    # Parse the bank statement XML to dictionary
    camt_dict = parser.parse_string(parser.bank_to_customer_statement, bytes(strip_namespace(input_data), 'utf8'))

    statements = pd.DataFrame.from_dict(camt_dict['statements'])
    all_entries = []
    for i,_ in statements.iterrows():
        if 'entries' in camt_dict['statements'][i]:

            #create empty df
            df = pd.DataFrame()
            dd = pd.DataFrame.from_records(camt_dict['statements'][i]['entries'])

            df['reference'] = dd['reference']
            df['credit_debit_indicator'] = dd['credit_debit_indicator']
            df['status'] = dd['status']
            df['account_servicer_reference'] = dd['account_servicer_reference']
            
            iban = camt_dict['statements'][i]['account']['id']['iban']
            name = camt_dict['statements'][i]['account']['name']
            df['iban'] = iban
            df['name'] = name
            df['currency'] = dd['amount'].str['currency']
            df['amount'] = dd['amount'].str['_value']

            df['value_date'] = dd['value_date'].str['date']
            df['value_date'] = pd.to_datetime(df['value_date']).dt.strftime('%Y-%m-%d')
            df['booking_date'] = dd['booking_date'].str['date']
            df['booking_date'] = pd.to_datetime(df['booking_date']).dt.strftime('%Y-%m-%d')

            #bank transaction code
            df['proprietary_code'] = dd['bank_transaction_code'].str['proprietary'].str['code']
            df['proprietary_issuer'] = dd['bank_transaction_code'].str['proprietary'].str['issuer']

            df['domain_code'] = dd['bank_transaction_code'].str['domain'].str['code']
            df['family_code'] = dd['bank_transaction_code'].str['domain'].str['family'].str['code']
            df['sub_family_code'] = dd['bank_transaction_code'].str['domain'].str['family'].str['sub_family_code']

            #transaction details
            df['debtor_name'] = dd['entry_details'].str[0].str['transaction_details'].str[0].str['related_parties'].str['debtor'].str['name']
            df['debtor_iban'] = dd['entry_details'].str[0].str['transaction_details'].str[0].str['related_parties'].str['debtor_account'].str['id'].str['iban']

            df['account_servicer_reference'] = dd['entry_details'].str[0].str['transaction_details'].str[0].str['refs'].str['account_servicer_reference']
            df['end_to_end_id'] = dd['entry_details'].str[0].str['transaction_details'].str[0].str['refs'].str['end_to_end_id']


            all_entries.append(df)

    df_entries = pd.concat(all_entries)
    df_entries.head()

    return df_entries





desc = "Upload XML file, convert to .csv file, and analyze transactions"

with gr.Blocks() as demo: 

    xml_file = gr.File(label = "Upload XML file here")
     
    # input_employees = gr.CheckboxGroup(["Transfer Solutions", "Ordina", "PwC", "Quistor", "Full Orbit", "Accenture", "Atos", "AMIS"], label="Oracle Partners", info="Who to judge?")
    # input_question = gr.Text(label="What activity is the Oracle Partner performing?")
    # additional_info = gr.Text(label="Additional information (optional)")
    output_text = gr.Text(label="R")
    df_entries = gr.DataFrame(label="Output table")


    submit_btn = gr.Button("Run analysis on XML file")


gr.Interface(fn=full_function, inputs=xml_file, outputs=df_entries, title=desc).launch(share=True)