# Python code for bank transaction processing
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from bank_api_connector import BankConnector

def process_transactions(bank_id, start_date, end_date):
    # Connect to bank API and fetch transactions
    connector = BankConnector(bank_id)
    transactions = connector.get_transactions(start_date, end_date)
    
    # Convert to DataFrame for processing
    df = pd.DataFrame(transactions)
    
    # Apply ML categorization model
    categories = transaction_classifier.predict(df['description'])
    df['category'] = categories
    
    # Match with existing vendors
    df['vendor_id'] = df['description'].apply(match_vendor)
    
    # Prepare for accounting system import
    accounting_entries = prepare_journal_entries(df)
    
    # Push to accounting system
    return accounting_system.batch_create_entries(accounting_entries)

# Python code for OCR invoice processing
import cv2
import pytesseract
import re
from invoice_processor import InvoiceParser

def process_invoice(file_path):
    # Read the invoice image/PDF
    image = cv2.imread(file_path)
    
    # Preprocess the image for better OCR
    processed_img = preprocess_image(image)
    
    # Extract text using OCR
    text = pytesseract.image_to_string(processed_img)
    
    # Parse invoice details using specialized parser
    parser = InvoiceParser()
    invoice_data = parser.parse(text)
    
    # Validate extracted data
    validation_errors = validate_invoice_data(invoice_data)
    
    if validation_errors:
        return {'status': 'review', 'errors': validation_errors}
    
    # Create invoice in accounting system
    return accounting_system.create_invoice(invoice_data)