Create app.py

app.py

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+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)