Automating_account_payables

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import LocalOutlierFactor

# Function to extract data from a bill image
def extract_data(bill_image):
    # Add code to extract data from the bill image and return it as a dictionary
    extracted_data = {
        'vendor': 'Vendor Name',
        'amount': 100.0,
        'date': '2023-05-15',
        'purchase_order_number': 'PO12345',
        # Add more fields as per your requirements
    }
    return extracted_data

# Function to predict fields in a bill
def predict_fields(bill_data):
    # Add code to predict various fields in the bill using a machine learning model
    predicted_fields = {
        'vendor': 'Predicted Vendor Name',
        'amount': 110.0,
        'date': '2023-05-15',
        'purchase_order_number': 'Predicted PO12345',
        # Add more predicted fields as per your requirements
    }
    return predicted_fields

# Function to detect anomalies in a bill
def detect_anomalies(bill_data):
    # Add code to detect anomalies in the bill data using anomaly detection algorithms
    anomaly_scores = {
        'vendor': 0.1,
        'amount': -1.2,
        'date': 0.5,
        'purchase_order_number': 0.8,
        # Add more anomaly scores for other fields as per your requirements
    }
    return anomaly_scores

# Function to match the bill to an existing purchase order
def match_purchase_order(bill_data):
    # Add code to match the bill to an existing purchase order in your ERP system
    purchase_order_status = 'Matched'
    return purchase_order_status

# Function to pay a bill
def pay_bill(bill_data):
    # Add code to process the bill payment and update payment status in the ERP accounting system
    payment_status = 'Paid'
    return payment_status

# Function to reconcile the payment of a bill with a bank transaction
def reconcile_payment(bill_data, bank_transaction_data):
    # Add code to reconcile the payment of the bill with the bank transaction data
    reconciliation_status = 'Reconciled'
    return reconciliation_status

# Main program
def main():
    # Load the bill image
    bill_image = 'bill_image.jpg'

    # Extract data from the bill image
    extracted_data = extract_data(bill_image)

    # Predict various fields in the bill
    predicted_fields = predict_fields(extracted_data)

    # Detect anomalies in the bill
    anomaly_scores = detect_anomalies(predicted_fields)

    # Match the bill to an existing purchase order
    purchase_order_status = match_purchase_order(predicted_fields)

    # Pay the bill
    payment_status = pay_bill(predicted_fields)

    # Load bank transaction data
    bank_transaction_data = pd.read_csv('bank_transaction_data.csv')

    # Reconcile the payment of the bill with the bank transaction
    reconciliation_status = reconcile_payment(predicted_fields, bank_transaction_data)

    # Print the results
    print("Extracted Data:", extracted_data)
    print("Predicted Fields:", predicted_fields)
    print("Anomaly Scores:", anomaly_scores)
    print("Purchase Order Status:", purchase_order_status)
    print("Payment Status:", payment_status)
    print("Reconciliation Status:", reconciliation_status)

# Run the program
if __name__ == '__main__':
    main()