app.py

import gradio as gr
from pdf2image import convert_from_path
from transformers import AutoModel, AutoTokenizer
from PIL import Image
import numpy as np
import os
import base64
import io
import uuid
import tempfile
import time
import shutil
from pathlib import Path
import json

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, device_map='cuda', use_safetensors=True)
model = model.eval().cuda()

UPLOAD_FOLDER = "./uploads"
RESULTS_FOLDER = "./results"

# Ensure directories exist
for folder in [UPLOAD_FOLDER, RESULTS_FOLDER]:
    if not os.path.exists(folder):
        os.makedirs(folder)

def image_to_base64(image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    return base64.b64encode(buffered.getvalue()).decode()

def convert_pdf_to_images(pdf_path, output_folder):
    # Ensure the output folder exists
    if not os.path.exists(output_folder):
        os.makedirs(output_folder)

    # Convert PDF to images
    images = convert_from_path(pdf_path)

    # Save each image to the output folder
    image_paths = []
    for i, image in enumerate(images):
        image_path = os.path.join(output_folder, f"page_{i + 1}.png")
        image.save(image_path, 'JPEG')
        image_paths.append(image_path)
        print(f"Saved {image_path}")
    return image_paths

def run_GOT(pdf_file):
    unique_id = str(uuid.uuid4())
    pdf_path = os.path.join(UPLOAD_FOLDER, f"{unique_id}.pdf")
    shutil.copy(pdf_file, pdf_path)
    
    images = convert_pdf_to_images(pdf_path, UPLOAD_FOLDER)
    results = []
    
    try:
        for i, image_path in enumerate(images):
            result_path = os.path.join(RESULTS_FOLDER, f"{unique_id}_page_{i+1}.html")
            
            res = model.chat_crop(tokenizer, image_path, ocr_type='format', render=True, save_render_file=result_path)
            
            # Read the rendered HTML content
            with open(result_path, 'r') as f:
                html_content = f.read()
            
            results.append({
                "page_number": i + 1,
                "text": res,
                "html": html_content
            })
            
            if os.path.exists(image_path):
                os.remove(image_path)
            if os.path.exists(result_path):
                os.remove(result_path)
    except Exception as e:
        return f"Error: {str(e)}", None
    finally:
        if os.path.exists(pdf_path):
            os.remove(pdf_path)
    
    html_output = "".join([result["html"] for result in results])
    print("HTML Output:", html_output)  # Debugging print statement
    return json.dumps(results, indent=4), html_output

def cleanup_old_files():
    current_time = time.time()
    for folder in [UPLOAD_FOLDER, RESULTS_FOLDER]:
        for file_path in Path(folder).glob('*'):
            if current_time - file_path.stat().st_mtime > 3600:  # 1 hour
                file_path.unlink()

with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            pdf_input = gr.File(type="filepath", label="Upload your PDF")
            submit_button = gr.Button("Submit")
        
        with gr.Column():
            ocr_result = gr.JSON(label="GOT output")
            html_output = gr.HTML(label="Rendered HTML")

    submit_button.click(
        run_GOT,
        inputs=[pdf_input],
        outputs=[ocr_result, html_output]
    )

if __name__ == "__main__":
    cleanup_old_files()
    demo.launch()