import gradio as gr
import fitz  # PyMuPDF
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, low_cpu_mem_usage=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 pdf_to_images(pdf_path):
    images = []
    pdf_document = fitz.open(pdf_path)
    for page_num in range(len(pdf_document)):
        page = pdf_document.load_page(page_num)
        pix = page.get_pixmap()
        img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
        images.append(img)
    return images

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 = pdf_to_images(pdf_path)
    results = []
    
    try:
        for i, image in enumerate(images):
            image_path = os.path.join(UPLOAD_FOLDER, f"{unique_id}_page_{i+1}.png")
            image.save(image_path)
            
            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)
    
    return json.dumps(results, indent=4)

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")

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

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