demo/demo_gradio.py

"""
Layout Inference Web Application with Gradio

A Gradio-based layout inference tool that supports image uploads and multiple backend inference engines.
It adopts a reference-style interface design while preserving the original inference logic.
"""

import gradio as gr
import json
import os
import io
import tempfile
import base64
import zipfile
import uuid
import re
from pathlib import Path
from PIL import Image
import requests
import shutil # Import shutil for cleanup

# Local tool imports
from dots_ocr.utils import dict_promptmode_to_prompt
from dots_ocr.utils.consts import MIN_PIXELS, MAX_PIXELS
from dots_ocr.utils.demo_utils.display import read_image
from dots_ocr.utils.doc_utils import load_images_from_pdf

# Add DotsOCRParser import
from dots_ocr.parser import DotsOCRParser


# ==================== Configuration ====================
DEFAULT_CONFIG = {
    'ip': "127.0.0.1",
    'port_vllm': 8000,
    'min_pixels': MIN_PIXELS,
    'max_pixels': MAX_PIXELS,
    'test_images_dir': "./assets/showcase_origin",
}

# ==================== Global Variables ====================
# Store current configuration
current_config = DEFAULT_CONFIG.copy()

# Create DotsOCRParser instance
dots_parser = DotsOCRParser(
    ip=DEFAULT_CONFIG['ip'],
    port=DEFAULT_CONFIG['port_vllm'],
    dpi=200,
    min_pixels=DEFAULT_CONFIG['min_pixels'],
    max_pixels=DEFAULT_CONFIG['max_pixels']
)

def get_initial_session_state():
    return {
        'processing_results': {
            'original_image': None,
            'processed_image': None,
            'layout_result': None,
            'markdown_content': None,
            'cells_data': None,
            'temp_dir': None,
            'session_id': None,
            'result_paths': None,
            'pdf_results': None
        },
        'pdf_cache': {
            "images": [],
            "current_page": 0,
            "total_pages": 0,
            "file_type": None,
            "is_parsed": False,
            "results": []
        }
    }

def read_image_v2(img):
    """Reads an image, supports URLs and local paths"""
    if isinstance(img, str) and img.startswith(("http://", "https://")):
        with requests.get(img, stream=True) as response:
            response.raise_for_status()
            img = Image.open(io.BytesIO(response.content))
    elif isinstance(img, str):
        img, _, _ = read_image(img, use_native=True)
    elif isinstance(img, Image.Image):
        pass
    else:
        raise ValueError(f"Invalid image type: {type(img)}")
    return img

def load_file_for_preview(file_path, session_state):
    """Loads a file for preview, supports PDF and image files"""
    pdf_cache = session_state['pdf_cache']
    
    if not file_path or not os.path.exists(file_path):
        return None, "<div id='page_info_box'>0 / 0</div>", session_state
    
    file_ext = os.path.splitext(file_path)[1].lower()
    
    try:
        if file_ext == '.pdf':
            pages = load_images_from_pdf(file_path)
            pdf_cache["file_type"] = "pdf"
        elif file_ext in ['.jpg', '.jpeg', '.png']:
            image = Image.open(file_path)
            pages = [image]
            pdf_cache["file_type"] = "image"
        else:
            return None, "<div id='page_info_box'>Unsupported file format</div>", session_state
    except Exception as e:
        return None, f"<div id='page_info_box'>PDF loading failed: {str(e)}</div>", session_state
    
    pdf_cache["images"] = pages
    pdf_cache["current_page"] = 0
    pdf_cache["total_pages"] = len(pages)
    pdf_cache["is_parsed"] = False
    pdf_cache["results"] = []
    
    return pages[0], f"<div id='page_info_box'>1 / {len(pages)}</div>", session_state

def turn_page(direction, session_state):
    """Page turning function"""
    pdf_cache = session_state['pdf_cache']
    
    if not pdf_cache["images"]:
        return None, "<div id='page_info_box'>0 / 0</div>", "", session_state

    if direction == "prev":
        pdf_cache["current_page"] = max(0, pdf_cache["current_page"] - 1)
    elif direction == "next":
        pdf_cache["current_page"] = min(pdf_cache["total_pages"] - 1, pdf_cache["current_page"] + 1)

    index = pdf_cache["current_page"]
    current_image = pdf_cache["images"][index]  # Use the original image by default
    page_info = f"<div id='page_info_box'>{index + 1} / {pdf_cache['total_pages']}</div>"
    
    current_json = ""
    if pdf_cache["is_parsed"] and index < len(pdf_cache["results"]):
        result = pdf_cache["results"][index]
        if 'cells_data' in result and result['cells_data']:
            try:
                current_json = json.dumps(result['cells_data'], ensure_ascii=False, indent=2)
            except:
                current_json = str(result.get('cells_data', ''))
        if 'layout_image' in result and result['layout_image']:
            current_image = result['layout_image']
    
    return current_image, page_info, current_json, session_state

def get_test_images():
    """Gets the list of test images"""
    test_images = []
    test_dir = current_config['test_images_dir']
    if os.path.exists(test_dir):
        test_images = [os.path.join(test_dir, name) for name in os.listdir(test_dir) 
                      if name.lower().endswith(('.png', '.jpg', '.jpeg', '.pdf'))]
    return test_images

def create_temp_session_dir():
    """Creates a unique temporary directory for each processing request"""
    session_id = uuid.uuid4().hex[:8]
    temp_dir = os.path.join(tempfile.gettempdir(), f"dots_ocr_demo_{session_id}")
    os.makedirs(temp_dir, exist_ok=True)
    return temp_dir, session_id

def parse_image_with_high_level_api(parser, image, prompt_mode, fitz_preprocess=False):
    """
    Processes using the high-level API parse_image from DotsOCRParser
    """
    # Create a temporary session directory
    temp_dir, session_id = create_temp_session_dir()
    
    try:
        # Save the PIL Image as a temporary file
        temp_image_path = os.path.join(temp_dir, f"input_{session_id}.png")
        image.save(temp_image_path, "PNG")
        
        # Use the high-level API parse_image
        filename = f"demo_{session_id}"
        results = parser.parse_image(
            input_path=image,
            filename=filename, 
            prompt_mode=prompt_mode,
            save_dir=temp_dir,
            fitz_preprocess=fitz_preprocess
        )
        
        # Parse the results
        if not results:
            raise ValueError("No results returned from parser")
        
        result = results[0]  # parse_image returns a list with a single result
        
        layout_image = None
        if 'layout_image_path' in result and os.path.exists(result['layout_image_path']):
            layout_image = Image.open(result['layout_image_path'])
        
        cells_data = None
        if 'layout_info_path' in result and os.path.exists(result['layout_info_path']):
            with open(result['layout_info_path'], 'r', encoding='utf-8') as f:
                cells_data = json.load(f)
        
        md_content = None
        if 'md_content_path' in result and os.path.exists(result['md_content_path']):
            with open(result['md_content_path'], 'r', encoding='utf-8') as f:
                md_content = f.read()
        
        return {
            'layout_image': layout_image,
            'cells_data': cells_data,
            'md_content': md_content,
            'filtered': result.get('filtered', False),
            'temp_dir': temp_dir,
            'session_id': session_id,
            'result_paths': result,
            'input_width': result.get('input_width', 0),
            'input_height': result.get('input_height', 0),
        }
    except Exception as e:
        if os.path.exists(temp_dir):
            shutil.rmtree(temp_dir, ignore_errors=True)
        raise e

def parse_pdf_with_high_level_api(parser, pdf_path, prompt_mode):
    """
    Processes using the high-level API parse_pdf from DotsOCRParser
    """
    # Create a temporary session directory
    temp_dir, session_id = create_temp_session_dir()
    
    try:
        # Use the high-level API parse_pdf
        filename = f"demo_{session_id}"
        results = parser.parse_pdf(
            input_path=pdf_path,
            filename=filename,
            prompt_mode=prompt_mode,
            save_dir=temp_dir
        )
        
        # Parse the results
        if not results:
            raise ValueError("No results returned from parser")
        
        # Handle multi-page results
        parsed_results = []
        all_md_content = []
        all_cells_data = []
        
        for i, result in enumerate(results):
            page_result = {
                'page_no': result.get('page_no', i),
                'layout_image': None,
                'cells_data': None,
                'md_content': None,
                'filtered': False
            }
            
            # Read the layout image
            if 'layout_image_path' in result and os.path.exists(result['layout_image_path']):
                page_result['layout_image'] = Image.open(result['layout_image_path'])
            
            # Read the JSON data
            if 'layout_info_path' in result and os.path.exists(result['layout_info_path']):
                with open(result['layout_info_path'], 'r', encoding='utf-8') as f:
                    page_result['cells_data'] = json.load(f)
                    all_cells_data.extend(page_result['cells_data'])
            
            # Read the Markdown content
            if 'md_content_path' in result and os.path.exists(result['md_content_path']):
                with open(result['md_content_path'], 'r', encoding='utf-8') as f:
                    page_content = f.read()
                    page_result['md_content'] = page_content
                    all_md_content.append(page_content)
            page_result['filtered'] = result.get('filtered', False)
            parsed_results.append(page_result)
        
        combined_md = "\n\n---\n\n".join(all_md_content) if all_md_content else ""
        return {
            'parsed_results': parsed_results,
            'combined_md_content': combined_md,
            'combined_cells_data': all_cells_data,
            'temp_dir': temp_dir,
            'session_id': session_id,
            'total_pages': len(results)
        }
        
    except Exception as e:
        if os.path.exists(temp_dir):
            shutil.rmtree(temp_dir, ignore_errors=True)
        raise e

# ==================== Core Processing Function ====================
def process_image_inference(session_state, test_image_input, file_input,
                          prompt_mode, server_ip, server_port, min_pixels, max_pixels,
                          fitz_preprocess=False
                          ):
    """Core function to handle image/PDF inference"""
    # Use session_state instead of global variables
    processing_results = session_state['processing_results']
    pdf_cache = session_state['pdf_cache']
    
    if processing_results.get('temp_dir') and os.path.exists(processing_results['temp_dir']):
        try:
            shutil.rmtree(processing_results['temp_dir'], ignore_errors=True)
        except Exception as e:
            print(f"Failed to clean up previous temporary directory: {e}")
    
    # Reset processing results for the current session
    session_state['processing_results'] = get_initial_session_state()['processing_results']
    processing_results = session_state['processing_results']
    
    current_config.update({
        'ip': server_ip,
        'port_vllm': server_port,
        'min_pixels': min_pixels,
        'max_pixels': max_pixels
    })
    
    # Update parser configuration
    dots_parser.ip = server_ip
    dots_parser.port = server_port
    dots_parser.min_pixels = min_pixels
    dots_parser.max_pixels = max_pixels
    
    input_file_path = file_input if file_input else test_image_input
    
    if not input_file_path:
        return None, "Please upload image/PDF file or select test image", "", "", gr.update(value=None), None, "", session_state
    
    file_ext = os.path.splitext(input_file_path)[1].lower()
    
    try:
        if file_ext == '.pdf':
            # MINIMAL CHANGE: The `process_pdf_file` function is now inlined and uses session_state.
            preview_image, page_info, session_state = load_file_for_preview(input_file_path, session_state)
            pdf_result = parse_pdf_with_high_level_api(dots_parser, input_file_path, prompt_mode)
            
            session_state['pdf_cache']["is_parsed"] = True
            session_state['pdf_cache']["results"] = pdf_result['parsed_results']
            
            processing_results.update({
                'markdown_content': pdf_result['combined_md_content'],
                'cells_data': pdf_result['combined_cells_data'],
                'temp_dir': pdf_result['temp_dir'],
                'session_id': pdf_result['session_id'],
                'pdf_results': pdf_result['parsed_results']
            })
            
            total_elements = len(pdf_result['combined_cells_data'])
            info_text = f"**PDF Information:**\n- Total Pages: {pdf_result['total_pages']}\n- Server: {current_config['ip']}:{current_config['port_vllm']}\n- Total Detected Elements: {total_elements}\n- Session ID: {pdf_result['session_id']}"
            
            current_page_layout_image = preview_image
            current_page_json = ""
            if session_state['pdf_cache']["results"]:
                first_result = session_state['pdf_cache']["results"][0]
                if 'layout_image' in first_result and first_result['layout_image']:
                    current_page_layout_image = first_result['layout_image']
                if first_result.get('cells_data'):
                    try:
                        current_page_json = json.dumps(first_result['cells_data'], ensure_ascii=False, indent=2)
                    except:
                        current_page_json = str(first_result['cells_data'])

            download_zip_path = None
            if pdf_result['temp_dir']:
                download_zip_path = os.path.join(pdf_result['temp_dir'], f"layout_results_{pdf_result['session_id']}.zip")
                with zipfile.ZipFile(download_zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
                    for root, _, files in os.walk(pdf_result['temp_dir']):
                        for file in files:
                            if not file.endswith('.zip'): zipf.write(os.path.join(root, file), os.path.relpath(os.path.join(root, file), pdf_result['temp_dir']))

            return (
                current_page_layout_image, info_text, pdf_result['combined_md_content'] or "No markdown content generated",
                pdf_result['combined_md_content'] or "No markdown content generated",
                gr.update(value=download_zip_path, visible=bool(download_zip_path)), page_info, current_page_json, session_state
            )
        
        else: # Image processing
            image = read_image_v2(input_file_path)
            session_state['pdf_cache'] = get_initial_session_state()['pdf_cache']
            
            original_image = image
            parse_result = parse_image_with_high_level_api(dots_parser, image, prompt_mode, fitz_preprocess)
            
            if parse_result['filtered']:
                 info_text = f"**Image Information:**\n- Original Size: {original_image.width} x {original_image.height}\n- Processing: JSON parsing failed, using cleaned text output\n- Server: {current_config['ip']}:{current_config['port_vllm']}\n- Session ID: {parse_result['session_id']}"
                 processing_results.update({
                     'original_image': original_image, 'markdown_content': parse_result['md_content'],
                     'temp_dir': parse_result['temp_dir'], 'session_id': parse_result['session_id'],
                     'result_paths': parse_result['result_paths']
                 })
                 return original_image, info_text, parse_result['md_content'], parse_result['md_content'], gr.update(visible=False), None, "", session_state
            
            md_content_raw = parse_result['md_content'] or "No markdown content generated"
            processing_results.update({
                'original_image': original_image, 'layout_result': parse_result['layout_image'],
                'markdown_content': parse_result['md_content'], 'cells_data': parse_result['cells_data'],
                'temp_dir': parse_result['temp_dir'], 'session_id': parse_result['session_id'],
                'result_paths': parse_result['result_paths']
            })
            
            num_elements = len(parse_result['cells_data']) if parse_result['cells_data'] else 0
            info_text = f"**Image Information:**\n- Original Size: {original_image.width} x {original_image.height}\n- Model Input Size: {parse_result['input_width']} x {parse_result['input_height']}\n- Server: {current_config['ip']}:{current_config['port_vllm']}\n- Detected {num_elements} layout elements\n- Session ID: {parse_result['session_id']}"
            
            current_json = json.dumps(parse_result['cells_data'], ensure_ascii=False, indent=2) if parse_result['cells_data'] else ""
            
            download_zip_path = None
            if parse_result['temp_dir']:
                download_zip_path = os.path.join(parse_result['temp_dir'], f"layout_results_{parse_result['session_id']}.zip")
                with zipfile.ZipFile(download_zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
                    for root, _, files in os.walk(parse_result['temp_dir']):
                        for file in files:
                            if not file.endswith('.zip'): zipf.write(os.path.join(root, file), os.path.relpath(os.path.join(root, file), parse_result['temp_dir']))
            
            return (
                parse_result['layout_image'], info_text, parse_result['md_content'] or "No markdown content generated",
                md_content_raw, gr.update(value=download_zip_path, visible=bool(download_zip_path)),
                None, current_json, session_state
            )
    except Exception as e:
        import traceback
        traceback.print_exc()
        return None, f"Error during processing: {e}", "", "", gr.update(value=None), None, "", session_state

# MINIMAL CHANGE: Functions now take `session_state` as an argument.
def clear_all_data(session_state):
    """Clears all data"""
    processing_results = session_state['processing_results']
    
    if processing_results.get('temp_dir') and os.path.exists(processing_results['temp_dir']):
        try:
            shutil.rmtree(processing_results['temp_dir'], ignore_errors=True)
        except Exception as e:
            print(f"Failed to clean up temporary directory: {e}")
    
    # Reset the session state by returning a new initial state
    new_session_state = get_initial_session_state()
    
    return (
        None,  # Clear file input
        "",    # Clear test image selection
        None,  # Clear result image
        "Waiting for processing results...",  # Reset info display
        "## Waiting for processing results...",  # Reset Markdown display
        "🕐 Waiting for parsing result...",    # Clear raw Markdown text
        gr.update(visible=False),  # Hide download button
        "<div id='page_info_box'>0 / 0</div>",  # Reset page info
        "🕐 Waiting for parsing result...",     # Clear current page JSON
        new_session_state
    )

def update_prompt_display(prompt_mode):
    """Updates the prompt display content"""
    return dict_promptmode_to_prompt[prompt_mode]

# ==================== Gradio Interface ====================
def create_gradio_interface():
    """Creates the Gradio interface"""
    
    # CSS styles, matching the reference style
    css = """

    #parse_button {
        background: #FF576D !important; /* !important 确保覆盖主题默认样式 */
        border-color: #FF576D !important;
    }
    /* 鼠标悬停时的颜色 */
    #parse_button:hover {
        background: #F72C49 !important;
        border-color: #F72C49 !important;
    }
    
    #page_info_html {
        display: flex;
        align-items: center;
        justify-content: center;
        height: 100%;
        margin: 0 12px;
    }

    #page_info_box {
        padding: 8px 20px;
        font-size: 16px;
        border: 1px solid #bbb;
        border-radius: 8px;
        background-color: #f8f8f8;
        text-align: center;
        min-width: 80px;
        box-shadow: 0 1px 3px rgba(0,0,0,0.1);
    }

    #markdown_output {
        min-height: 800px;
        overflow: auto;
    }

    footer {
        visibility: hidden;
    }
    
    #info_box {
        padding: 10px;
        background-color: #f8f9fa;
        border-radius: 8px;
        border: 1px solid #dee2e6;
        margin: 10px 0;
        font-size: 14px;
    }
    
    #result_image {
        border-radius: 8px;
    }
    
    #markdown_tabs {
        height: 100%;
    }
    """
    
    with gr.Blocks(theme="ocean", css=css, title='dots.ocr') as demo:
        session_state = gr.State(value=get_initial_session_state())
        
        # Title
        gr.HTML("""
            <div style="display: flex; align-items: center; justify-content: center; margin-bottom: 20px;">
                <h1 style="margin: 0; font-size: 2em;">🔍 dots.ocr</h1>
            </div>
            <div style="text-align: center; margin-bottom: 10px;">
                <em>Supports image/PDF layout analysis and structured output</em>
            </div>
        """)
        
        with gr.Row():
            # Left side: Input and Configuration
            with gr.Column(scale=1, elem_id="left-panel"):
                gr.Markdown("### 📥 Upload & Select")
                file_input = gr.File(
                    label="Upload PDF/Image", 
                    type="filepath", 
                    file_types=[".pdf", ".jpg", ".jpeg", ".png"],
                )
                
                test_images = get_test_images()
                test_image_input = gr.Dropdown(
                    label="Or Select an Example",
                    choices=[""] + test_images,
                    value="",
                )

                gr.Markdown("### ⚙️ Prompt & Actions")
                prompt_mode = gr.Dropdown(
                    label="Select Prompt",
                    choices=["prompt_layout_all_en", "prompt_layout_only_en", "prompt_ocr"],
                    value="prompt_layout_all_en",
                )
                
                # Display current prompt content
                prompt_display = gr.Textbox(
                    label="Current Prompt Content",
                    value=dict_promptmode_to_prompt[list(dict_promptmode_to_prompt.keys())[0]],
                    lines=4,
                    max_lines=8,
                    interactive=False,
                    show_copy_button=True
                )
                
                with gr.Row():
                    process_btn = gr.Button("🔍 Parse", variant="primary", scale=2, elem_id="parse_button")
                    clear_btn = gr.Button("🗑️ Clear", variant="secondary", scale=1)
                
                with gr.Accordion("🛠️ Advanced Configuration", open=False):
                    fitz_preprocess = gr.Checkbox(
                        label="Enable fitz_preprocess for images", 
                        value=True,
                        info="Processes image via a PDF-like pipeline (image->pdf->200dpi image). Recommended if your image DPI is low."
                    )
                    with gr.Row():
                        server_ip = gr.Textbox(label="Server IP", value=DEFAULT_CONFIG['ip'])
                        server_port = gr.Number(label="Port", value=DEFAULT_CONFIG['port_vllm'], precision=0)
                    with gr.Row():
                        min_pixels = gr.Number(label="Min Pixels", value=DEFAULT_CONFIG['min_pixels'], precision=0)
                        max_pixels = gr.Number(label="Max Pixels", value=DEFAULT_CONFIG['max_pixels'], precision=0)
            # Right side: Result Display
            with gr.Column(scale=6, variant="compact"):
                with gr.Row():
                    # Result Image
                    with gr.Column(scale=3):
                        gr.Markdown("### 👁️ File Preview")
                        result_image = gr.Image(
                            label="Layout Preview",
                            visible=True,
                            height=800,
                            show_label=False
                        )
                        
                        # Page navigation (shown during PDF preview)
                        with gr.Row():
                            prev_btn = gr.Button("⬅ Previous", size="sm")
                            page_info = gr.HTML(
                                value="<div id='page_info_box'>0 / 0</div>", 
                                elem_id="page_info_html"
                            )
                            next_btn = gr.Button("Next ➡", size="sm")
                        
                        # Info Display
                        info_display = gr.Markdown(
                            "Waiting for processing results...",
                            elem_id="info_box"
                        )
                    
                    # Markdown Result
                    with gr.Column(scale=3):
                        gr.Markdown("### ✔️ Result Display")
                        
                        with gr.Tabs(elem_id="markdown_tabs"):
                            with gr.TabItem("Markdown Render Preview"):
                                md_output = gr.Markdown(
                                    "## Please click the parse button to parse or select for single-task recognition...",
                                    max_height=600,
                                    latex_delimiters=[
                                        {"left": "$$", "right": "$$", "display": True},
                                        {"left": "$", "right": "$", "display": False}
                                    ],
                                    show_copy_button=False,
                                    elem_id="markdown_output"
                                )
                            
                            with gr.TabItem("Markdown Raw Text"):
                                md_raw_output = gr.Textbox(
                                    value="🕐 Waiting for parsing result...",
                                    label="Markdown Raw Text",
                                    max_lines=100,
                                    lines=38,
                                    show_copy_button=True,
                                    elem_id="markdown_output",
                                    show_label=False
                                )
                            
                            with gr.TabItem("Current Page JSON"):
                                current_page_json = gr.Textbox(
                                    value="🕐 Waiting for parsing result...",
                                    label="Current Page JSON",
                                    max_lines=100,
                                    lines=38,
                                    show_copy_button=True,
                                    elem_id="markdown_output",
                                    show_label=False
                                )
                
                # Download Button
                with gr.Row():
                    download_btn = gr.DownloadButton(
                        "⬇️ Download Results",
                        visible=False
                    )
        
        # When the prompt mode changes, update the display content
        prompt_mode.change(
            fn=update_prompt_display,
            inputs=prompt_mode,
            outputs=prompt_display,
        )
        
        # Show preview on file upload
        file_input.upload(
            # fn=lambda file_data, state: load_file_for_preview(file_data, state),
            fn=load_file_for_preview,
            inputs=[file_input, session_state],
            outputs=[result_image, page_info, session_state]
        )
        
        # Also handle test image selection
        test_image_input.change(
            # fn=lambda path, state: load_file_for_preview(path, state),
            fn=load_file_for_preview,
            inputs=[test_image_input, session_state],
            outputs=[result_image, page_info, session_state]
        )

        prev_btn.click(
            fn=lambda s: turn_page("prev", s),
            inputs=[session_state], 
            outputs=[result_image, page_info, current_page_json, session_state]
        )
        
        next_btn.click(
            fn=lambda s: turn_page("next", s),
            inputs=[session_state], 
            outputs=[result_image, page_info, current_page_json, session_state]
        )
        
        process_btn.click(
            fn=process_image_inference,
            inputs=[
                session_state, test_image_input, file_input,
                prompt_mode, server_ip, server_port, min_pixels, max_pixels, 
                fitz_preprocess
            ],
            outputs=[
                result_image, info_display, md_output, md_raw_output,
                download_btn, page_info, current_page_json, session_state
            ]
        )
        
        clear_btn.click(
            fn=clear_all_data,
            inputs=[session_state],
            outputs=[
                file_input, test_image_input,
                result_image, info_display, md_output, md_raw_output,
                download_btn, page_info, current_page_json, session_state
            ]
        )
    
    return demo

# ==================== Main Program ====================
if __name__ == "__main__":
    import sys
    port = int(sys.argv[1])
    demo = create_gradio_interface()
    demo.queue().launch(
        server_name="0.0.0.0", 
        server_port=port, 
        debug=True
    )