Hyperautomation vs. Python Scripts: A Practical Showdown for 2025

import os
import re
import time
from openai import OpenAI
from dotenv import load_dotenv

# --- Configuration ---
load_dotenv()
API_KEY = os.getenv("OPENAI_API_KEY")
if not API_KEY:
    raise ValueError("OpenAI API key not found. Please create a .env file.")
client = OpenAI(api_key=API_KEY)
MODEL = "gpt-4o"

# --- Method 1: The Traditional, Hand-Written Script ---
def traditional_invoice_parser(text):
    """
    A rigid, hand-written parser using regular expressions.
    It expects a specific format.
    """
    patterns = {
        'invoice_number': r"Invoice Number: (INV-\d+)",
        'date': r"Date: (\d{4}-\d{2}-\d{2})",
        'total_amount': r"Total Amount: \$([\d,]+\.\d{2})"
    }
    
    extracted_data = {}
    for key, pattern in patterns.items():
        match = re.search(pattern, text)
        extracted_data[key] = match.group(1) if match else None
        
    return extracted_data

# --- Method 2: The Hyperautomation / Agentic AI Approach ---
def ai_workflow_generator(text, goal):
    """
    Simulates a Hyperautomation platform by using an LLM to generate
    the parsing logic based on a natural language goal.
    """
    system_message = (
        "You are an expert data extraction AI. Your task is to analyze the user's text "
        "and extract the specific information requested in their goal. "
        "Return ONLY a JSON object with the extracted data. The keys should be 'invoice_number', 'date', and 'total_amount'."
    )
    
    prompt = f"Goal: {goal}\n\nText to analyze:\n---\n{text}\n---"
    
    response_str = client.chat.completions.create(
        model=MODEL,
        response_format={"type": "json_object"},
        messages=[
            {"role": "system", "content": system_message},
            {"role": "user", "content": prompt},
        ],
        temperature=0.2,
    ).choices[0].message.content
    
    # The LLM should return a valid JSON string, which we can parse
    try:
        return eval(response_str) # Using eval is simple for this demo, json.loads is safer in production
    except:
        return {"error": "Failed to parse LLM response"}

# --- The Showdown Orchestrator ---
def main():
    print("\n--- Hyperautomation vs. Python Scripts: A Showdown ---")
    
    invoice_folder = "invoices"
    if not os.path.isdir(invoice_folder):
        print(f"\n\033[91mError:\033[0m The '{invoice_folder}' folder was not found.")
        print("Please create it and add sample .txt files as per the instructions.")
        return

    files_to_process = [f for f in os.listdir(invoice_folder) if f.endswith('.txt')]
    if not files_to_process:
        print(f"\n\033[91mError:\033[0m No .txt files found in the '{invoice_folder}' folder.")
        return

    natural_language_goal = "Extract the invoice number, date, and total amount from the text."
    
    for filename in files_to_process:
        print(f"\n\n\033[1;96m--- Processing: {filename} ---\033[0m")
        filepath = os.path.join(invoice_folder, filename)
        with open(filepath, 'r') as f:
            content = f.read()

        # --- Run Traditional Script ---
        print("\n\033[1;93m1. Running Traditional Hand-Written Script...\033[0m")
        start_time = time.time()
        traditional_result = traditional_invoice_parser(content)
        end_time = time.time()
        print(f"  Result: {traditional_result}")
        print(f"  Time Taken: {end_time - start_time:.4f} seconds")

        # --- Run AI-Generated Workflow ---
        print("\n\033[1;95m2. Running AI-Generated Hyperautomation Workflow...\033[0m")
        start_time = time.time()
        ai_result = ai_workflow_generator(content, natural_language_goal)
        end_time = time.time()
        print(f"  Result: {ai_result}")
        print(f"  Time Taken: {end_time - start_time:.4f} seconds")

    print("\n\n\033[1;96m--- Showdown Complete ---\033[0m")
    print("Notice how the traditional script is faster but fails on unstructured formats,")
    print("while the AI workflow is slower but more flexible and resilient to change.")

if __name__ == "__main__":
    main()