app.py

# RPA Business Analyst AI Agent Implementation
# pip install pm4py neo4j rasa opencv-python pytesseract tensorflow scikit-learn

# imports
import pm4py
import cv2
from mss import mss
import pytesseract
import spacy
from neo4j import GraphDatabase
import numpy as np
import pandas as pd
import json
import tensorflow as tf
from sklearn.ensemble import GradientBoostingRegressor

# Process Mining Module
def discover_process(event_log_path, algorithm="inductive"):
    log = pm4py.read_xes(event_log_path)
    if algorithm == "inductive":
        model = pm4py.discover_petri_net_inductive(log)
    else:
        model = pm4py.discover_petri_net_alpha(log)
    pm4py.view_petri_net(*model)
    return model

# Computer Vision Module
class ScreenCapture:
    def capture_screen(self):
        with mss() as sct:
            monitor = {"top": 0, "left": 0, "width": 1920, "height": 1080}
            img = sct.grab(monitor)
            return cv2.cvtColor(np.array(img), cv2.COLOR_BGRA2BGR)
    
    def extract_text(self, image):
        return pytesseract.image_to_string(image)

# NLP Module
nlp_model = spacy.load("en_core_web_sm")
def parse_workflows(text):
    doc = nlp_model(text)
    return [ent.text for ent in doc.ents if ent.label_ == "WORKFLOW"]

# Requirements Analysis Module
driver = GraphDatabase.driver("bolt://localhost", auth=None)
def create_process_node(process_name, process_description):
    with driver.session() as session:
        session.run(
            "MERGE (p:Process {name: $name}) SET p.description = $description",
            name=process_name,
            description=process_description
        )

# Automation Opportunity Detector
class SuitabilityPredictor:
    def __init__(self, model_path):
        self.model = tf.keras.models.load_model(model_path)
    
    def predict(self, features):
        return self.model.predict(features)[0][0]

def automate_suitability(process_data, model_path):
    predictor = SuitabilityPredictor(model_path)
    return predictor.predict(process_data)

# Main Execution Block
if __name__ == "__main__":
    # Load Configuration
    with open("config.json") as f:
        config = json.load(f)
    
    # Process Mining
    event_log_path = "data/event_logs.xes"
    process_model = discover_process(event_log_path, config["process_mining"]["algorithm"])
    
    # Computer Vision
    screen_cap = ScreenCapture()
    screen_img = screen_cap.capture_screen()
    screen_text = screen_cap.extract_text(screen_img)
    
    # NLP
    workflows = parse_workflows(screen_text)
    
    # Knowledge Graph
    create_process_node("Invoice Processing", "Processes vendor invoices")
    
    # ML Suitability
    model_path = config["ml"]["model_path"]
    suitability_score = automate_suitability(process_data, model_path)
    
    # Recommendation Engine
    print(f"Automation Suitability: {suitability_score:.2f}")