app.py

import torch
import pytesseract
import cv2
import json
import xml.etree.ElementTree as ET
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
from layoutparser import Detectron2LayoutModel
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from stable_baselines3 import PPO

# Load OCR model
processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")

def preprocess_image(image_path):
    image = cv2.imread(image_path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return gray

def extract_text(image_path):
    image = preprocess_image(image_path)
    text = pytesseract.image_to_string(image)
    return text

def analyze_layout(image_path):
    model = Detectron2LayoutModel("lp://PubLayNet/mask_rcnn_X_101_32x8d_FPN_3x/config")
    image = cv2.imread(image_path)
    layout = model.detect(image)
    return layout

def generate_machine_readable_format(text, format_type='json'):
    if format_type == 'json':
        return json.dumps({"content": text})
    elif format_type == 'xml':
        root = ET.Element("Document")
        content = ET.SubElement(root, "Content")
        content.text = text
        return ET.tostring(root, encoding='unicode')
    return text

# Generative AI Model
GPT2_model = GPT2LMHeadModel.from_pretrained("gpt2")
GPT2_tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

def generate_structured_output(text):
    inputs = GPT2_tokenizer.encode(text, return_tensors="pt")
    outputs = GPT2_model.generate(inputs, max_length=500)
    return GPT2_tokenizer.decode(outputs[0])

# Reinforcement Learning for Optimization
class DocumentConversionEnv:
    def __init__(self):
        self.state = None
    
    def reset(self):
        self.state = "start"
        return self.state
    
    def step(self, action):
        reward = 1 if action == "optimize" else -1
        self.state = "optimized" if action == "optimize" else "start"
        return self.state, reward, False, {}

env = DocumentConversionEnv()
rl_model = PPO("MlpPolicy", env, verbose=1)
rl_model.learn(total_timesteps=1000)

def convert_document(image_path, output_format='json'):
    text = extract_text(image_path)
    layout = analyze_layout(image_path)
    structured_output = generate_structured_output(text)
    machine_readable_output = generate_machine_readable_format(structured_output, format_type=output_format)
    return machine_readable_output

# Example usage
document_path = "sample_document.png"
converted_document = convert_document(document_path, output_format='json')
print(converted_document)