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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)
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