app.py

import os
import re
from http import HTTPStatus
from typing import Dict, List, Optional, Tuple
import base64
import mimetypes
import PyPDF2
import docx
import cv2
import numpy as np
from PIL import Image
import pytesseract
import requests
from urllib.parse import urlparse, urljoin
from bs4 import BeautifulSoup
import html2text

import gradio as gr
from huggingface_hub import InferenceClient
from tavily import TavilyClient

# Configuration
SystemPrompt = """You are a helpful coding assistant. You help users create applications by generating code based on their requirements. 
When asked to create an application, you should:
1. Understand the user's requirements
2. Generate clean, working code
3. Provide HTML output when appropriate for web applications
4. Include necessary comments and documentation
5. Ensure the code is functional and follows best practices

For website redesign tasks:
- Analyze the extracted website content to understand the structure and purpose
- Create a modern, responsive design that improves upon the original
- Maintain the core functionality and content while enhancing the user experience
- Use modern CSS frameworks and design patterns
- Ensure accessibility and mobile responsiveness

If an image is provided, analyze it and use the visual information to better understand the user's requirements.

Always respond with code that can be executed or rendered directly.

Always output only the HTML code inside a ```html ... ``` code block, and do not include any explanations or extra text."""

# System prompt with search capability
SystemPromptWithSearch = """You are a helpful coding assistant with access to real-time web search. You help users create applications by generating code based on their requirements. 
When asked to create an application, you should:
1. Understand the user's requirements
2. Use web search when needed to find the latest information, best practices, or specific technologies
3. Generate clean, working code
4. Provide HTML output when appropriate for web applications
5. Include necessary comments and documentation
6. Ensure the code is functional and follows best practices

For website redesign tasks:
- Analyze the extracted website content to understand the structure and purpose
- Use web search to find current design trends and best practices for the specific type of website
- Create a modern, responsive design that improves upon the original
- Maintain the core functionality and content while enhancing the user experience
- Use modern CSS frameworks and design patterns
- Ensure accessibility and mobile responsiveness

If an image is provided, analyze it and use the visual information to better understand the user's requirements.

Always respond with code that can be executed or rendered directly.

Always output only the HTML code inside a ```html ... ``` code block, and do not include any explanations or extra text."""

# Available models
AVAILABLE_MODELS = [
    {
        "name": "DeepSeek V3",
        "id": "deepseek-ai/DeepSeek-V3-0324",
        "description": "DeepSeek V3 model for code generation"
    },
    {
        "name": "DeepSeek R1", 
        "id": "deepseek-ai/DeepSeek-R1-0528",
        "description": "DeepSeek R1 model for code generation"
    },
    {
        "name": "ERNIE-4.5-VL",
        "id": "baidu/ERNIE-4.5-VL-424B-A47B-Base-PT",
        "description": "ERNIE-4.5-VL model for multimodal code generation with image support"
    },
    {
        "name": "MiniMax M1",
        "id": "MiniMaxAI/MiniMax-M1-80k",
        "description": "MiniMax M1 model for code generation and general tasks"
    },
    {
        "name": "Qwen3-235B-A22B",
        "id": "Qwen/Qwen3-235B-A22B",
        "description": "Qwen3-235B-A22B model for code generation and general tasks"
    },
    {
        "name": "SmolLM3-3B",
        "id": "HuggingFaceTB/SmolLM3-3B",
        "description": "SmolLM3-3B model for code generation and general tasks"
    }
]

DEMO_LIST = [
    {
        "title": "Todo App",
        "description": "Create a simple todo application with add, delete, and mark as complete functionality"
    },
    {
        "title": "Calculator",
        "description": "Build a basic calculator with addition, subtraction, multiplication, and division"
    },
    {
        "title": "Weather Dashboard",
        "description": "Create a weather dashboard that displays current weather information"
    },
    {
        "title": "Chat Interface",
        "description": "Build a chat interface with message history and user input"
    },
    {
        "title": "E-commerce Product Card",
        "description": "Create a product card component for an e-commerce website"
    },
    {
        "title": "Login Form",
        "description": "Build a responsive login form with validation"
    },
    {
        "title": "Dashboard Layout",
        "description": "Create a dashboard layout with sidebar navigation and main content area"
    },
    {
        "title": "Data Table",
        "description": "Build a data table with sorting and filtering capabilities"
    },
    {
        "title": "Image Gallery",
        "description": "Create an image gallery with lightbox functionality and responsive grid layout"
    },
    {
        "title": "UI from Image",
        "description": "Upload an image of a UI design and I'll generate the HTML/CSS code for it"
    },
    {
        "title": "Extract Text from Image",
        "description": "Upload an image containing text and I'll extract and process the text content"
    },
    {
        "title": "Website Redesign",
        "description": "Enter a website URL to extract its content and redesign it with a modern, responsive layout"
    }
]

# HF Inference Client
YOUR_API_TOKEN = os.getenv('HF_TOKEN')
client = InferenceClient(
    provider="auto",
    api_key=YOUR_API_TOKEN,
    bill_to="huggingface"
)

# Tavily Search Client
TAVILY_API_KEY = os.getenv('TAVILY_API_KEY')
tavily_client = None
if TAVILY_API_KEY:
    try:
        tavily_client = TavilyClient(api_key=TAVILY_API_KEY)
    except Exception as e:
        print(f"Failed to initialize Tavily client: {e}")
        tavily_client = None

History = List[Tuple[str, str]]
Messages = List[Dict[str, str]]

def history_to_messages(history: History, system: str) -> Messages:
    messages = [{'role': 'system', 'content': system}]
    for h in history:
        # Handle multimodal content in history
        user_content = h[0]
        if isinstance(user_content, list):
            # Extract text from multimodal content
            text_content = ""
            for item in user_content:
                if isinstance(item, dict) and item.get("type") == "text":
                    text_content += item.get("text", "")
            user_content = text_content if text_content else str(user_content)
        
        messages.append({'role': 'user', 'content': user_content})
        messages.append({'role': 'assistant', 'content': h[1]})
    return messages

def messages_to_history(messages: Messages) -> Tuple[str, History]:
    assert messages[0]['role'] == 'system'
    history = []
    for q, r in zip(messages[1::2], messages[2::2]):
        # Extract text content from multimodal messages for history
        user_content = q['content']
        if isinstance(user_content, list):
            text_content = ""
            for item in user_content:
                if isinstance(item, dict) and item.get("type") == "text":
                    text_content += item.get("text", "")
            user_content = text_content if text_content else str(user_content)
        
        history.append([user_content, r['content']])
    return history

def history_to_chatbot_messages(history: History) -> List[Dict[str, str]]:
    """Convert history tuples to chatbot message format"""
    messages = []
    for user_msg, assistant_msg in history:
        # Handle multimodal content
        if isinstance(user_msg, list):
            text_content = ""
            for item in user_msg:
                if isinstance(item, dict) and item.get("type") == "text":
                    text_content += item.get("text", "")
            user_msg = text_content if text_content else str(user_msg)
        
        messages.append({"role": "user", "content": user_msg})
        messages.append({"role": "assistant", "content": assistant_msg})
    return messages

def remove_code_block(text):
    # Try to match code blocks with language markers
    patterns = [
        r'```(?:html|HTML)\n([\s\S]+?)\n```',  # Match ```html or ```HTML
        r'```\n([\s\S]+?)\n```',               # Match code blocks without language markers
        r'```([\s\S]+?)```'                      # Match code blocks without line breaks
    ]
    for pattern in patterns:
        match = re.search(pattern, text, re.DOTALL)
        if match:
            extracted = match.group(1).strip()
            return extracted
    # If no code block is found, check if the entire text is HTML
    if text.strip().startswith('<!DOCTYPE html>') or text.strip().startswith('<html') or text.strip().startswith('<'):
        return text.strip()
    return text.strip()

def history_render(history: History):
    return gr.update(visible=True), history

def clear_history():
    return [], [], None, ""  # Empty lists for both tuple format and chatbot messages, None for file, empty string for website URL

def update_image_input_visibility(model):
    """Update image input visibility based on selected model"""
    is_ernie_vl = model.get("id") == "baidu/ERNIE-4.5-VL-424B-A47B-Base-PT"
    return gr.update(visible=is_ernie_vl)

def process_image_for_model(image):
    """Convert image to base64 for model input"""
    if image is None:
        return None
    
    # Convert numpy array to PIL Image if needed
    import io
    import base64
    import numpy as np
    from PIL import Image
    
    # Handle numpy array from Gradio
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image)
    
    buffer = io.BytesIO()
    image.save(buffer, format='PNG')
    img_str = base64.b64encode(buffer.getvalue()).decode()
    return f"data:image/png;base64,{img_str}"

def create_multimodal_message(text, image=None):
    """Create a multimodal message with text and optional image"""
    if image is None:
        return {"role": "user", "content": text}
    
    content = [
        {
            "type": "text",
            "text": text
        },
        {
            "type": "image_url",
            "image_url": {
                "url": process_image_for_model(image)
            }
        }
    ]
    
    return {"role": "user", "content": content}

# Updated for faster Tavily search and closer prompt usage
# Uses 'advanced' search_depth and auto_parameters=True for speed and relevance

def perform_web_search(query: str, max_results: int = 5, include_domains=None, exclude_domains=None) -> str:
    """Perform web search using Tavily with default parameters"""
    if not tavily_client:
        return "Web search is not available. Please set the TAVILY_API_KEY environment variable."
    
    try:
        # Use Tavily defaults with advanced search depth for better results
        search_params = {
            "search_depth": "advanced",
            "max_results": min(max(1, max_results), 20)
        }
        if include_domains is not None:
            search_params["include_domains"] = include_domains
        if exclude_domains is not None:
            search_params["exclude_domains"] = exclude_domains

        response = tavily_client.search(query, **search_params)
        
        search_results = []
        for result in response.get('results', []):
            title = result.get('title', 'No title')
            url = result.get('url', 'No URL')
            content = result.get('content', 'No content')
            search_results.append(f"Title: {title}\nURL: {url}\nContent: {content}\n")
        
        if search_results:
            return "Web Search Results:\n\n" + "\n---\n".join(search_results)
        else:
            return "No search results found."
            
    except Exception as e:
        return f"Search error: {str(e)}"

def enhance_query_with_search(query: str, enable_search: bool) -> str:
    """Enhance the query with web search results if search is enabled"""
    if not enable_search or not tavily_client:
        return query
    
    # Perform search to get relevant information
    search_results = perform_web_search(query)
    
    # Combine original query with search results
    enhanced_query = f"""Original Query: {query}

{search_results}

Please use the search results above to help create the requested application with the most up-to-date information and best practices."""
    
    return enhanced_query

def send_to_sandbox(code):
    # Add a wrapper to inject necessary permissions and ensure full HTML
    wrapped_code = f"""
    <!DOCTYPE html>
    <html>
    <head>
        <meta charset=\"UTF-8\">
        <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">
        <script>
            // Safe localStorage polyfill
            const safeStorage = {{
                _data: {{}},
                getItem: function(key) {{ return this._data[key] || null; }},
                setItem: function(key, value) {{ this._data[key] = value; }},
                removeItem: function(key) {{ delete this._data[key]; }},
                clear: function() {{ this._data = {{}}; }}
            }};
            Object.defineProperty(window, 'localStorage', {{
                value: safeStorage,
                writable: false
            }});
            window.onerror = function(message, source, lineno, colno, error) {{
                console.error('Error:', message);
            }};
        </script>
    </head>
    <body>
        {code}
    </body>
    </html>
    """
    encoded_html = base64.b64encode(wrapped_code.encode('utf-8')).decode('utf-8')
    data_uri = f"data:text/html;charset=utf-8;base64,{encoded_html}"
    iframe = f'<iframe src="{data_uri}" width="100%" height="920px" sandbox="allow-scripts allow-same-origin allow-forms allow-popups allow-modals allow-presentation" allow="display-capture"></iframe>'
    return iframe

def demo_card_click(e: gr.EventData):
    try:
        # Get the index from the event data
        if hasattr(e, '_data') and e._data:
            # Try different ways to get the index
            if 'index' in e._data:
                index = e._data['index']
            elif 'component' in e._data and 'index' in e._data['component']:
                index = e._data['component']['index']
            elif 'target' in e._data and 'index' in e._data['target']:
                index = e._data['target']['index']
            else:
                # If we can't get the index, try to extract it from the card data
                index = 0
        else:
            index = 0
        
        # Ensure index is within bounds
        if index >= len(DEMO_LIST):
            index = 0
            
        return DEMO_LIST[index]['description']
    except (KeyError, IndexError, AttributeError) as e:
        # Return the first demo description as fallback
        return DEMO_LIST[0]['description']

def extract_text_from_image(image_path):
    """Extract text from image using OCR"""
    try:
        # Check if tesseract is available
        try:
            pytesseract.get_tesseract_version()
        except Exception:
            return "Error: Tesseract OCR is not installed. Please install Tesseract to extract text from images. See install_tesseract.md for instructions."
        
        # Read image using OpenCV
        image = cv2.imread(image_path)
        if image is None:
            return "Error: Could not read image file"
        
        # Convert to RGB (OpenCV uses BGR)
        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        
        # Preprocess image for better OCR results
        # Convert to grayscale
        gray = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2GRAY)
        
        # Apply thresholding to get binary image
        _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
        
        # Extract text using pytesseract
        text = pytesseract.image_to_string(binary, config='--psm 6')
        
        return text.strip() if text.strip() else "No text found in image"
        
    except Exception as e:
        return f"Error extracting text from image: {e}"

def extract_text_from_file(file_path):
    if not file_path:
        return ""
    mime, _ = mimetypes.guess_type(file_path)
    ext = os.path.splitext(file_path)[1].lower()
    try:
        if ext == ".pdf":
            with open(file_path, "rb") as f:
                reader = PyPDF2.PdfReader(f)
                return "\n".join(page.extract_text() or "" for page in reader.pages)
        elif ext in [".txt", ".md"]:
            with open(file_path, "r", encoding="utf-8") as f:
                return f.read()
        elif ext == ".csv":
            with open(file_path, "r", encoding="utf-8") as f:
                return f.read()
        elif ext == ".docx":
            doc = docx.Document(file_path)
            return "\n".join([para.text for para in doc.paragraphs])
        elif ext.lower() in [".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".tif", ".gif", ".webp"]:
            return extract_text_from_image(file_path)
        else:
            return ""
    except Exception as e:
        return f"Error extracting text: {e}"

def extract_website_content(url: str) -> str:
    """Extract content from a website URL"""
    try:
        # Validate URL
        parsed_url = urlparse(url)
        if not parsed_url.scheme:
            url = "https://" + url
            parsed_url = urlparse(url)
        
        if not parsed_url.netloc:
            return "Error: Invalid URL provided"
        
        # Set headers to mimic a browser request
        headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
        }
        
        # Make the request
        response = requests.get(url, headers=headers, timeout=10)
        response.raise_for_status()
        
        # Parse HTML content
        soup = BeautifulSoup(response.content, 'html.parser')
        
        # Remove script and style elements
        for script in soup(["script", "style"]):
            script.decompose()
        
        # Extract title
        title = soup.find('title')
        title_text = title.get_text().strip() if title else "No title found"
        
        # Extract meta description
        meta_desc = soup.find('meta', attrs={'name': 'description'})
        description = meta_desc.get('content', '') if meta_desc else ""
        
        # Extract main content areas
        content_sections = []
        
        # Look for common content containers
        main_selectors = [
            'main', 'article', '.content', '.main-content', '.post-content',
            '#content', '#main', '.entry-content', '.post-body'
        ]
        
        for selector in main_selectors:
            elements = soup.select(selector)
            for element in elements:
                text = element.get_text().strip()
                if len(text) > 100:  # Only include substantial content
                    content_sections.append(text)
        
        # If no main content found, extract from body
        if not content_sections:
            body = soup.find('body')
            if body:
                # Remove navigation, footer, and other non-content elements
                for element in body.find_all(['nav', 'footer', 'header', 'aside']):
                    element.decompose()
                content_sections.append(body.get_text().strip())
        
        # Extract navigation links
        nav_links = []
        nav_elements = soup.find_all(['nav', 'header'])
        for nav in nav_elements:
            links = nav.find_all('a')
            for link in links:
                link_text = link.get_text().strip()
                link_href = link.get('href', '')
                if link_text and link_href:
                    nav_links.append(f"{link_text}: {link_href}")
        
        # Extract images
        images = []
        img_elements = soup.find_all('img')
        for img in img_elements:
            src = img.get('src', '')
            alt = img.get('alt', '')
            if src:
                # Convert relative URLs to absolute
                if not src.startswith(('http://', 'https://')):
                    src = urljoin(url, src)
                images.append(f"Image: {alt} ({src})")
        
        # Compile the extracted content
        website_content = f"""
WEBSITE CONTENT EXTRACTION
==========================

URL: {url}
Title: {title_text}
Description: {description}

NAVIGATION MENU:
{chr(10).join(nav_links[:10]) if nav_links else "No navigation found"}

MAIN CONTENT:
{chr(10).join(content_sections[:3]) if content_sections else "No main content found"}

IMAGES:
{chr(10).join(images[:10]) if images else "No images found"}

PAGE STRUCTURE:
- This appears to be a {title_text.lower()} website
- Contains {len(content_sections)} main content sections
- Has {len(nav_links)} navigation links
- Includes {len(images)} images
"""
        
        return website_content.strip()
        
    except requests.exceptions.RequestException as e:
        return f"Error accessing website: {str(e)}"
    except Exception as e:
        return f"Error extracting website content: {str(e)}"

def generation_code(query: Optional[str], image: Optional[gr.Image], file: Optional[str], website_url: Optional[str], _setting: Dict[str, str], _history: Optional[History], _current_model: Dict, enable_search: bool = False):
    if query is None:
        query = ''
    if _history is None:
        _history = []
    
    # Choose system prompt based on search setting
    system_prompt = SystemPromptWithSearch if enable_search else _setting['system']
    messages = history_to_messages(_history, system_prompt)
    
    # Extract file text and append to query if file is present
    file_text = ""
    if file:
        file_text = extract_text_from_file(file)
        if file_text:
            file_text = file_text[:5000]  # Limit to 5000 chars for prompt size
            query = f"{query}\n\n[Reference file content below]\n{file_text}"
    
    # Extract website content and append to query if website URL is present
    website_text = ""
    if website_url and website_url.strip():
        website_text = extract_website_content(website_url.strip())
        if website_text and not website_text.startswith("Error"):
            website_text = website_text[:8000]  # Limit to 8000 chars for prompt size
            query = f"{query}\n\n[Website content to redesign below]\n{website_text}"
        elif website_text.startswith("Error"):
            query = f"{query}\n\n[Error extracting website: {website_text}]"
    
    # Enhance query with search if enabled
    enhanced_query = enhance_query_with_search(query, enable_search)
    
    if image is not None:
        messages.append(create_multimodal_message(enhanced_query, image))
    else:
        messages.append({'role': 'user', 'content': enhanced_query})
    try:
        completion = client.chat.completions.create(
            model=_current_model["id"],
            messages=messages,
            stream=True,
            max_tokens=5000
        )
        content = ""
        for chunk in completion:
            if chunk.choices[0].delta.content:
                content += chunk.choices[0].delta.content
                clean_code = remove_code_block(content)
                search_status = " (with web search)" if enable_search and tavily_client else ""
                yield {
                    code_output: clean_code,
                    history_output: history_to_chatbot_messages(_history),
                }
        _history = messages_to_history(messages + [{
            'role': 'assistant',
            'content': content
        }])
        yield {
            code_output: remove_code_block(content),
            history: _history,
            sandbox: send_to_sandbox(remove_code_block(content)),
            history_output: history_to_chatbot_messages(_history),
        }
    except Exception as e:
        error_message = f"Error: {str(e)}"
        yield {
            code_output: error_message,
            history_output: history_to_chatbot_messages(_history),
        }

# Main application
with gr.Blocks(
    theme=gr.themes.Base(
        primary_hue="blue",
        secondary_hue="gray",
        neutral_hue="gray",
        font=gr.themes.GoogleFont("Inter"),
        font_mono=gr.themes.GoogleFont("JetBrains Mono"),
        text_size=gr.themes.sizes.text_md,
        spacing_size=gr.themes.sizes.spacing_md,
        radius_size=gr.themes.sizes.radius_md
    ),
    title="AnyCoder - AI Code Generator"
) as demo:
    history = gr.State([])
    setting = gr.State({
        "system": SystemPrompt,
    })
    current_model = gr.State(AVAILABLE_MODELS[0])
    open_panel = gr.State(None)

    with gr.Sidebar():
        gr.Markdown("# AnyCoder")
        gr.Markdown("*AI-Powered Code Generator*")
        
        # Main input section
        input = gr.Textbox(
            label="What would you like to build?",
            placeholder="Describe your application...",
            lines=3
        )
        
        # URL input for website redesign
        website_url_input = gr.Textbox(
            label="Website URL (for redesign)",
            placeholder="https://example.com",
            lines=1,
            visible=True
        )
        
        # File upload (minimal)
        file_input = gr.File(
            label="Reference file",
            file_types=[".pdf", ".txt", ".md", ".csv", ".docx", ".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".tif", ".gif", ".webp"],
            visible=True
        )
        
        # Image input (only for ERNIE model)
        image_input = gr.Image(
            label="UI design image",
            visible=False
        )
        
        # Action buttons
        with gr.Row():
            btn = gr.Button("Generate", variant="primary", size="lg", scale=2)
            clear_btn = gr.Button("Clear", variant="secondary", size="sm", scale=1)
        
        # Search toggle (minimal)
        search_toggle = gr.Checkbox(
            label="🔍 Web search",
            value=False
        )
        
        # Model selection (minimal)
        model_dropdown = gr.Dropdown(
            choices=[model['name'] for model in AVAILABLE_MODELS],
            value=AVAILABLE_MODELS[0]['name'],
            label="Model"
        )
        
        # Quick examples (minimal)
        gr.Markdown("**Quick start**")
        with gr.Column():
            for i, demo_item in enumerate(DEMO_LIST[:3]):
                demo_card = gr.Button(
                    value=demo_item['title'], 
                    variant="secondary",
                    size="sm"
                )
                demo_card.click(
                    fn=lambda idx=i: gr.update(value=DEMO_LIST[idx]['description']),
                    outputs=input
                )
        
        # Status indicators (minimal)
        if not tavily_client:
            gr.Markdown("⚠️ Web search unavailable")
        else:
            gr.Markdown("✅ Web search available")
        
        # Hidden elements for functionality
        model_display = gr.Markdown(f"**Model:** {AVAILABLE_MODELS[0]['name']}", visible=False)
        
        def on_model_change(model_name):
            for m in AVAILABLE_MODELS:
                if m['name'] == model_name:
                    return m, f"**Model:** {m['name']}", update_image_input_visibility(m)
            return AVAILABLE_MODELS[0], f"**Model:** {AVAILABLE_MODELS[0]['name']}", update_image_input_visibility(AVAILABLE_MODELS[0])
        
        def save_prompt(input):
            return {setting: {"system": input}}
        
        model_dropdown.change(
            on_model_change,
            inputs=model_dropdown,
            outputs=[current_model, model_display, image_input]
        )
        
        # System prompt (collapsed by default)
        with gr.Accordion("Advanced", open=False):
            systemPromptInput = gr.Textbox(
                value=SystemPrompt,
                label="System prompt",
                lines=5
            )
            save_prompt_btn = gr.Button("Save", variant="primary", size="sm")
            save_prompt_btn.click(save_prompt, inputs=systemPromptInput, outputs=setting)

    with gr.Column():
        with gr.Tabs():
            with gr.Tab("Code"):
                code_output = gr.Code(
                    language="html", 
                    lines=25, 
                    interactive=False,
                    label="Generated code"
                )
            with gr.Tab("Preview"):
                sandbox = gr.HTML(label="Live preview")
            with gr.Tab("History"):
                history_output = gr.Chatbot(show_label=False, height=400, type="messages")


    # Event handlers
    btn.click(
        generation_code,
        inputs=[input, image_input, file_input, website_url_input, setting, history, current_model, search_toggle],
        outputs=[code_output, history, sandbox, history_output]
    )
    clear_btn.click(clear_history, outputs=[history, history_output, file_input, website_url_input])

if __name__ == "__main__":
    demo.queue(default_concurrency_limit=20).launch(ssr_mode=True, mcp_server=True)