app.py

# app.py

import gradio as gr
from bs4 import BeautifulSoup
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import requests
import time
import re
import logging
import os
import sys
import threading
from queue import Queue, Empty
import json
from concurrent.futures import ThreadPoolExecutor

# Import OpenAI library
import openai

# Suppress only the single warning from urllib3 needed.
import urllib3
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

# Set up logging to output to the console
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

# Create a console handler
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setLevel(logging.INFO)

# Create a formatter and set it for the handler
formatter = logging.Formatter('%(asctime)s %(levelname)s %(name)s %(message)s')
console_handler.setFormatter(formatter)

# Add the handler to the logger
logger.addHandler(console_handler)

# Initialize variables and models
logger.info("Initializing variables and models")
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
faiss_index = None
bookmarks = []
fetch_cache = {}

# Lock for thread-safe operations
lock = threading.Lock()

# Define the categories
CATEGORIES = [
    "Social Media",
    "News and Media",
    "Education and Learning",
    "Entertainment",
    "Shopping and E-commerce",
    "Finance and Banking",
    "Technology",
    "Health and Fitness",
    "Travel and Tourism",
    "Food and Recipes",
    "Sports",
    "Arts and Culture",
    "Government and Politics",
    "Business and Economy",
    "Science and Research",
    "Personal Blogs and Journals",
    "Job Search and Careers",
    "Music and Audio",
    "Videos and Movies",
    "Reference and Knowledge Bases",
    "Dead Link",
    "Uncategorized",
]

# Set up OpenAI API key and base URL
OPENAI_API_KEY = os.getenv('GROQ_API_KEY')  # Ensure this environment variable is set correctly

if not OPENAI_API_KEY:
    logger.error("GROQ_API_KEY environment variable not set.")

openai.api_key = OPENAI_API_KEY
openai.api_base = "https://api.groq.com/openai/v1"  # Ensure this is the correct base URL for your API

# Rate Limiter Configuration
RPM_LIMIT = 60       # Requests per minute (adjust based on your API's limit)
TPM_LIMIT = 60000    # Tokens per minute (adjust based on your API's limit)
BATCH_SIZE = 5       # Number of bookmarks per batch

# Implementing a Token Bucket Rate Limiter
class TokenBucket:
    def __init__(self, rate, capacity):
        self.rate = rate  # tokens per second
        self.capacity = capacity
        self.tokens = capacity
        self.timestamp = time.time()
        self.lock = threading.Lock()

    def consume(self, tokens=1):
        with self.lock:
            now = time.time()
            elapsed = now - self.timestamp
            refill = elapsed * self.rate
            self.tokens = min(self.capacity, self.tokens + refill)
            self.timestamp = now
            if self.tokens >= tokens:
                self.tokens -= tokens
                return True
            else:
                return False

    def wait_for_token(self, tokens=1):
        while not self.consume(tokens):
            time.sleep(0.05)

# Initialize rate limiters
rpm_rate = RPM_LIMIT / 60  # tokens per second
tpm_rate = TPM_LIMIT / 60  # tokens per second

rpm_bucket = TokenBucket(rate=rpm_rate, capacity=RPM_LIMIT)
tpm_bucket = TokenBucket(rate=tpm_rate, capacity=TPM_LIMIT)

# Queue for LLM tasks
llm_queue = Queue()

def categorize_based_on_summary(summary, url):
    """
    Assign category based on keywords in the summary or URL.
    """
    summary_lower = summary.lower()
    url_lower = url.lower()
    if 'social media' in summary_lower or 'twitter' in summary_lower or 'x.com' in url_lower:
        return 'Social Media'
    elif 'wikipedia' in url_lower:
        return 'Reference and Knowledge Bases'
    elif 'cloud computing' in summary_lower or 'aws' in summary_lower:
        return 'Technology'
    elif 'news' in summary_lower or 'media' in summary_lower:
        return 'News and Media'
    elif 'education' in summary_lower or 'learning' in summary_lower:
        return 'Education and Learning'
    # Add more conditions as needed
    else:
        return 'Uncategorized'

def validate_category(bookmark):
    """
    Further validate and adjust the category if needed.
    """
    # Example: Specific cases based on URL
    url_lower = bookmark['url'].lower()
    if 'facebook' in url_lower or 'x.com' in url_lower:
        return 'Social Media'
    elif 'wikipedia' in url_lower:
        return 'Reference and Knowledge Bases'
    elif 'aws.amazon.com' in url_lower:
        return 'Technology'
    # Add more specific cases as needed
    else:
        return bookmark['category']

def extract_main_content(soup):
    """
    Extract the main content from a webpage while filtering out boilerplate content.
    """
    if not soup:
        return ""

    # Remove unwanted elements
    for element in soup(['script', 'style', 'header', 'footer', 'nav', 'aside', 'form', 'noscript']):
        element.decompose()

    # Extract text from <p> tags
    p_tags = soup.find_all('p')
    if p_tags:
        content = ' '.join([p.get_text(strip=True, separator=' ') for p in p_tags])
    else:
        # Fallback to body content
        content = soup.get_text(separator=' ', strip=True)

    # Clean up the text
    content = re.sub(r'\s+', ' ', content)

    # Truncate content to a reasonable length (e.g., 1500 words)
    words = content.split()
    if len(words) > 1500:
        content = ' '.join(words[:1500])

    return content

def get_page_metadata(soup):
    """
    Extract metadata from the webpage including title, description, and keywords.
    """
    metadata = {
        'title': '',
        'description': '',
        'keywords': ''
    }

    if not soup:
        return metadata

    # Get title
    title_tag = soup.find('title')
    if title_tag and title_tag.string:
        metadata['title'] = title_tag.string.strip()

    # Get meta description
    meta_desc = (
        soup.find('meta', attrs={'name': 'description'}) or
        soup.find('meta', attrs={'property': 'og:description'}) or
        soup.find('meta', attrs={'name': 'twitter:description'})
    )
    if meta_desc:
        metadata['description'] = meta_desc.get('content', '').strip()

    # Get meta keywords
    meta_keywords = soup.find('meta', attrs={'name': 'keywords'})
    if meta_keywords:
        metadata['keywords'] = meta_keywords.get('content', '').strip()

    # Get OG title if main title is empty
    if not metadata['title']:
        og_title = soup.find('meta', attrs={'property': 'og:title'})
        if og_title:
            metadata['title'] = og_title.get('content', '').strip()

    return metadata

def llm_worker():
    """
    Worker thread to process LLM tasks from the queue while respecting rate limits.
    """
    logger.info("LLM worker started.")
    while True:
        batch = []
        try:
            # Collect bookmarks up to BATCH_SIZE
            while len(batch) < BATCH_SIZE:
                bookmark = llm_queue.get(timeout=1)
                if bookmark is None:
                    # Shutdown signal
                    logger.info("LLM worker shutting down.")
                    return
                if not bookmark.get('dead_link') and not bookmark.get('slow_link'):
                    batch.append(bookmark)
                else:
                    # Skip processing for dead or slow links
                    bookmark['summary'] = 'No summary available.'
                    bookmark['category'] = 'Uncategorized'
                    llm_queue.task_done()

        except Empty:
            pass  # No more bookmarks at the moment

        if batch:
            try:
                # Rate Limiting
                rpm_bucket.wait_for_token()
                # Estimate tokens: prompt + max_tokens
                # Here, we assume max_tokens=150 per bookmark
                total_tokens = 150 * len(batch)
                tpm_bucket.wait_for_token(tokens=total_tokens)

                # Prepare prompt
                prompt = "You are an assistant that creates concise webpage summaries and assigns categories.\n\n"
                prompt += "Provide summaries and categories for the following bookmarks:\n\n"

                for idx, bookmark in enumerate(batch, 1):
                    prompt += f"Bookmark {idx}:\nURL: {bookmark['url']}\nTitle: {bookmark['title']}\n\n"

                # Corrected f-string without backslashes
                prompt += f"Categories:\n{', '.join([f'\"{cat}\"' for cat in CATEGORIES])}\n\n"

                prompt += "Format your response as a JSON object where each key is the bookmark URL and the value is another JSON object containing 'summary' and 'category'.\n\n"
                prompt += "Example:\n"
                prompt += "{\n"
                prompt += "  \"https://example.com\": {\n"
                prompt += "    \"summary\": \"This is an example summary.\",\n"
                prompt += "    \"category\": \"Technology\"\n"
                prompt += "  }\n"
                prompt += "}\n\n"
                prompt += "Now, provide the summaries and categories for the bookmarks listed above."

                response = openai.ChatCompletion.create(
                    model='llama-3.1-70b-versatile',  # Ensure this model is correct and available
                    messages=[
                        {"role": "user", "content": prompt}
                    ],
                    max_tokens=150 * len(batch),
                    temperature=0.5,
                )

                content = response['choices'][0]['message']['content'].strip()
                if not content:
                    raise ValueError("Empty response received from the model.")

                # Parse JSON response
                try:
                    json_response = json.loads(content)
                    for bookmark in batch:
                        url = bookmark['url']
                        if url in json_response:
                            summary = json_response[url].get('summary', '').strip()
                            category = json_response[url].get('category', '').strip()

                            if not summary:
                                summary = 'No summary available.'
                            bookmark['summary'] = summary

                            if category in CATEGORIES:
                                bookmark['category'] = category
                            else:
                                # Fallback to keyword-based categorization
                                bookmark['category'] = categorize_based_on_summary(summary, url)
                        else:
                            logger.warning(f"No data returned for {url}. Using fallback methods.")
                            bookmark['summary'] = 'No summary available.'
                            bookmark['category'] = 'Uncategorized'

                        # Additional keyword-based validation
                        bookmark['category'] = validate_category(bookmark)

                        logger.info(f"Processed bookmark: {url}")

                except json.JSONDecodeError:
                    logger.error("Failed to parse JSON response from LLM. Using fallback methods.")
                    for bookmark in batch:
                        bookmark['summary'] = 'No summary available.'
                        bookmark['category'] = categorize_based_on_summary(bookmark.get('summary', ''), bookmark['url'])
                        bookmark['category'] = validate_category(bookmark)

                except Exception as e:
                    logger.error(f"Error processing LLM response: {e}", exc_info=True)
                    for bookmark in batch:
                        bookmark['summary'] = 'No summary available.'
                        bookmark['category'] = 'Uncategorized'

            except openai.error.RateLimitError as e:
                logger.warning(f"LLM Rate limit reached. Retrying after 60 seconds.")
                # Re-enqueue the entire batch for retry
                for bookmark in batch:
                    llm_queue.put(bookmark)
                time.sleep(60)  # Wait before retrying
                continue  # Skip the rest and retry

            except Exception as e:
                logger.error(f"Error during LLM processing: {e}", exc_info=True)
                for bookmark in batch:
                    bookmark['summary'] = 'No summary available.'
                    bookmark['category'] = 'Uncategorized'

            finally:
                # Mark all bookmarks in the batch as done
                for _ in batch:
                    llm_queue.task_done()

def generate_summary_and_assign_category(bookmark):
    """
    Enqueue bookmarks for LLM processing.
    """
    logger.info(f"Enqueuing bookmark for LLM processing: {bookmark.get('url')}")
    llm_queue.put(bookmark)

def parse_bookmarks(file_content):
    """
    Parse bookmarks from HTML file.
    """
    logger.info("Parsing bookmarks")
    try:
        soup = BeautifulSoup(file_content, 'html.parser')
        extracted_bookmarks = []
        for link in soup.find_all('a'):
            url = link.get('href')
            title = link.text.strip()
            if url and title:
                if url.startswith('http://') or url.startswith('https://'):
                    extracted_bookmarks.append({'url': url, 'title': title})
                else:
                    logger.info(f"Skipping non-http/https URL: {url}")
        logger.info(f"Extracted {len(extracted_bookmarks)} bookmarks")
        return extracted_bookmarks
    except Exception as e:
        logger.error("Error parsing bookmarks: %s", e, exc_info=True)
        raise

def fetch_url_info(bookmark):
    """
    Fetch information about a URL.
    """
    url = bookmark['url']
    if url in fetch_cache:
        with lock:
            bookmark.update(fetch_cache[url])
        return

    try:
        logger.info(f"Fetching URL info for: {url}")
        headers = {
            'User-Agent': 'Mozilla/5.0',
            'Accept-Language': 'en-US,en;q=0.9',
        }
        response = requests.get(url, headers=headers, timeout=5, verify=False, allow_redirects=True)
        bookmark['etag'] = response.headers.get('ETag', 'N/A')
        bookmark['status_code'] = response.status_code

        content = response.text
        logger.info(f"Fetched content length for {url}: {len(content)} characters")

        if response.status_code >= 500:
            bookmark['dead_link'] = True
            bookmark['html_content'] = ''
            bookmark['description'] = ''
            logger.warning(f"Dead link detected: {url} with status {response.status_code}")
        else:
            bookmark['dead_link'] = False
            bookmark['html_content'] = content
            # Extract description from metadata
            soup = BeautifulSoup(content, 'html.parser')
            metadata = get_page_metadata(soup)
            bookmark['description'] = metadata.get('description', '')
            logger.info(f"Fetched information for {url}")
    except requests.exceptions.Timeout:
        bookmark['dead_link'] = False
        bookmark['etag'] = 'N/A'
        bookmark['status_code'] = 'Timeout'
        bookmark['description'] = ''
        bookmark['html_content'] = ''
        bookmark['slow_link'] = True
        logger.warning(f"Timeout while fetching {url}. Marking as 'Slow'.")
    except Exception as e:
        bookmark['dead_link'] = True
        bookmark['etag'] = 'N/A'
        bookmark['status_code'] = 'Error'
        bookmark['description'] = ''
        bookmark['html_content'] = ''
        logger.error(f"Error fetching URL info for {url}: {e}", exc_info=True)
    finally:
        with lock:
            fetch_cache[url] = {
                'etag': bookmark.get('etag'),
                'status_code': bookmark.get('status_code'),
                'dead_link': bookmark.get('dead_link'),
                'description': bookmark.get('description'),
                'html_content': bookmark.get('html_content', ''),
                'slow_link': bookmark.get('slow_link', False),
            }

def vectorize_and_index(bookmarks_list):
    """
    Create vector embeddings for bookmarks and build FAISS index with ID mapping.
    """
    global faiss_index
    logger.info("Vectorizing summaries and building FAISS index")
    try:
        summaries = [bookmark['summary'] for bookmark in bookmarks_list]
        embeddings = embedding_model.encode(summaries)
        dimension = embeddings.shape[1]
        index = faiss.IndexIDMap(faiss.IndexFlatL2(dimension))
        ids = np.array([bookmark['id'] for bookmark in bookmarks_list], dtype=np.int64)
        index.add_with_ids(np.array(embeddings).astype('float32'), ids)
        faiss_index = index
        logger.info("FAISS index built successfully with IDs")
        return index
    except Exception as e:
        logger.error(f"Error in vectorizing and indexing: {e}", exc_info=True)
        raise

def display_bookmarks():
    """
    Generate HTML display for bookmarks.
    """
    logger.info("Generating HTML display for bookmarks")
    cards = ''
    for i, bookmark in enumerate(bookmarks):
        index = i + 1
        if bookmark.get('dead_link'):
            status = "❌ Dead Link"
            card_style = "border: 2px solid red;"
            text_style = "color: white;"
            summary = 'No summary available.'
        elif bookmark.get('slow_link'):
            status = "⏳ Slow Response"
            card_style = "border: 2px solid orange;"
            text_style = "color: white;"
            summary = bookmark.get('summary', 'No summary available.')
        else:
            status = "✅ Active"
            card_style = "border: 2px solid green;"
            text_style = "color: white;"
            summary = bookmark.get('summary', 'No summary available.')

        title = bookmark['title']
        url = bookmark['url']
        etag = bookmark.get('etag', 'N/A')
        category = bookmark.get('category', 'Uncategorized')

        # Escape HTML content to prevent XSS attacks
        from html import escape
        title = escape(title)
        url = escape(url)
        summary = escape(summary)
        category = escape(category)

        card_html = f'''
        <div class="card" style="{card_style} padding: 10px; margin: 10px; border-radius: 5px; background-color: #1e1e1e;">
            <div class="card-content">
                <h3 style="{text_style}">{index}. {title} {status}</h3>
                <p style="{text_style}"><strong>Category:</strong> {category}</p>
                <p style="{text_style}"><strong>URL:</strong> <a href="{url}" target="_blank" style="{text_style}">{url}</a></p>
                <p style="{text_style}"><strong>ETag:</strong> {etag}</p>
                <p style="{text_style}"><strong>Summary:</strong> {summary}</p>
            </div>
        </div>
        '''
        cards += card_html
    logger.info("HTML display generated")
    return cards

def process_uploaded_file(file, state_bookmarks):
    """
    Process the uploaded bookmarks file.
    """
    global bookmarks, faiss_index
    logger.info("Processing uploaded file")

    if file is None:
        logger.warning("No file uploaded")
        return "Please upload a bookmarks HTML file.", '', state_bookmarks, display_bookmarks(), gr.update(choices=[])

    try:
        file_content = file.decode('utf-8')
    except UnicodeDecodeError as e:
        logger.error(f"Error decoding the file: {e}", exc_info=True)
        return "Error decoding the file. Please ensure it's a valid HTML file.", '', state_bookmarks, display_bookmarks(), gr.update(choices=[])

    try:
        bookmarks = parse_bookmarks(file_content)
    except Exception as e:
        logger.error(f"Error parsing bookmarks: {e}", exc_info=True)
        return "Error parsing the bookmarks HTML file.", '', state_bookmarks, display_bookmarks(), gr.update(choices=[])

    if not bookmarks:
        logger.warning("No bookmarks found in the uploaded file")
        return "No bookmarks found in the uploaded file.", '', state_bookmarks, display_bookmarks(), gr.update(choices=[])

    # Assign unique IDs to bookmarks
    for idx, bookmark in enumerate(bookmarks):
        bookmark['id'] = idx

    # Fetch bookmark info concurrently
    logger.info("Fetching URL info concurrently")
    with ThreadPoolExecutor(max_workers=10) as executor:
        executor.map(fetch_url_info, bookmarks)

    # Enqueue bookmarks for LLM processing
    logger.info("Enqueuing bookmarks for LLM processing")
    for bookmark in bookmarks:
        generate_summary_and_assign_category(bookmark)

    # Wait until all LLM tasks are completed
    llm_queue.join()
    logger.info("All LLM tasks have been processed")

    try:
        faiss_index = vectorize_and_index(bookmarks)
    except Exception as e:
        logger.error(f"Error building FAISS index: {e}", exc_info=True)
        return "Error building search index.", '', state_bookmarks, display_bookmarks(), gr.update(choices=[])

    message = f"✅ Successfully processed {len(bookmarks)} bookmarks."
    logger.info(message)

    # Generate displays and updates
    bookmark_html = display_bookmarks()
    choices = [f"{i+1}. {bookmark['title']} (Category: {bookmark['category']})"
               for i, bookmark in enumerate(bookmarks)]

    # Update state
    state_bookmarks = bookmarks.copy()

    return message, bookmark_html, state_bookmarks, bookmark_html, gr.update(choices=choices)

def delete_selected_bookmarks(selected_indices, state_bookmarks):
    """
    Delete selected bookmarks and remove their vectors from the FAISS index.
    """
    global bookmarks, faiss_index
    if not selected_indices:
        return "⚠️ No bookmarks selected.", gr.update(choices=[]), display_bookmarks()

    ids_to_delete = []
    indices_to_delete = []
    for s in selected_indices:
        idx = int(s.split('.')[0]) - 1
        if 0 <= idx < len(bookmarks):
            bookmark_id = bookmarks[idx]['id']
            ids_to_delete.append(bookmark_id)
            indices_to_delete.append(idx)
            logger.info(f"Deleting bookmark at index {idx + 1}")

    # Remove vectors from FAISS index
    if faiss_index is not None and ids_to_delete:
        faiss_index.remove_ids(np.array(ids_to_delete, dtype=np.int64))

    # Remove bookmarks from the list (reverse order to avoid index shifting)
    for idx in sorted(indices_to_delete, reverse=True):
        bookmarks.pop(idx)

    message = "🗑️ Selected bookmarks deleted successfully."
    logger.info(message)
    choices = [f"{i+1}. {bookmark['title']} (Category: {bookmark['category']})"
               for i, bookmark in enumerate(bookmarks)]

    # Update state
    state_bookmarks = bookmarks.copy()

    return message, gr.update(choices=choices), display_bookmarks()

def edit_selected_bookmarks_category(selected_indices, new_category, state_bookmarks):
    """
    Edit category of selected bookmarks.
    """
    if not selected_indices:
        return "⚠️ No bookmarks selected.", gr.update(choices=[]), display_bookmarks(), state_bookmarks
    if not new_category:
        return "⚠️ No new category selected.", gr.update(choices=[]), display_bookmarks(), state_bookmarks

    indices = [int(s.split('.')[0])-1 for s in selected_indices]
    for idx in indices:
        if 0 <= idx < len(bookmarks):
            bookmarks[idx]['category'] = new_category
            logger.info(f"Updated category for bookmark {idx + 1} to {new_category}")

    message = "✏️ Category updated for selected bookmarks."
    logger.info(message)

    # Update choices and display
    choices = [f"{i+1}. {bookmark['title']} (Category: {bookmark['category']})"
               for i, bookmark in enumerate(bookmarks)]

    # Update state
    state_bookmarks = bookmarks.copy()

    return message, gr.update(choices=choices), display_bookmarks(), state_bookmarks

def export_bookmarks():
    """
    Export bookmarks to an HTML file.
    """
    if not bookmarks:
        logger.warning("No bookmarks to export")
        return None

    try:
        logger.info("Exporting bookmarks to HTML")
        soup = BeautifulSoup("<!DOCTYPE NETSCAPE-Bookmark-file-1><Title>Bookmarks</Title><H1>Bookmarks</H1>", 'html.parser')
        dl = soup.new_tag('DL')
        for bookmark in bookmarks:
            dt = soup.new_tag('DT')
            a = soup.new_tag('A', href=bookmark['url'])
            a.string = bookmark['title']
            dt.append(a)
            dl.append(dt)
        soup.append(dl)
        html_content = str(soup)
        output_file = "exported_bookmarks.html"
        with open(output_file, 'w', encoding='utf-8') as f:
            f.write(html_content)
        logger.info("Bookmarks exported successfully")
        return output_file
    except Exception as e:
        logger.error(f"Error exporting bookmarks: {e}", exc_info=True)
        return None

def chatbot_response(user_query, chat_history):
    """
    Generate chatbot response using the FAISS index and embeddings.
    """
    if not bookmarks or faiss_index is None:
        logger.warning("No bookmarks available for chatbot")
        chat_history.append({"role": "assistant", "content": "⚠️ No bookmarks available. Please upload and process your bookmarks first."})
        return chat_history

    logger.info(f"Chatbot received query: {user_query}")

    try:
        chat_history.append({"role": "user", "content": user_query})

        # Rate Limiting
        rpm_bucket.wait_for_token()
        # Estimate tokens: prompt + max_tokens
        # Here, we assume max_tokens=300 per chatbot response
        tpm_bucket.wait_for_token(tokens=300)

        query_vector = embedding_model.encode([user_query]).astype('float32')
        k = 5
        distances, ids = faiss_index.search(query_vector, k)
        ids = ids.flatten()

        id_to_bookmark = {bookmark['id']: bookmark for bookmark in bookmarks}
        # Filter out bookmarks without summaries
        matching_bookmarks = [id_to_bookmark.get(id) for id in ids if id in id_to_bookmark and id_to_bookmark.get(id).get('summary')]

        if not matching_bookmarks:
            answer = "No relevant bookmarks found for your query."
            chat_history.append({"role": "assistant", "content": answer})
            return chat_history

        bookmarks_info = "\n".join([
            f"Title: {bookmark['title']}\nURL: {bookmark['url']}\nSummary: {bookmark['summary']}"
            for bookmark in matching_bookmarks
        ])

        prompt = f"""
A user asked: "{user_query}"
Based on the bookmarks below, provide a helpful answer to the user's query, referencing the relevant bookmarks.
Bookmarks:
{bookmarks_info}
Provide a concise and helpful response.
"""

        response = openai.ChatCompletion.create(
            model='llama-3.1-70b-versatile',  # Ensure this model is correct and available
            messages=[
                {"role": "user", "content": prompt}
            ],
            max_tokens=300,
            temperature=0.7,
        )

        answer = response['choices'][0]['message']['content'].strip()
        logger.info("Chatbot response generated")

        chat_history.append({"role": "assistant", "content": answer})
        return chat_history

    except openai.error.RateLimitError as e:
        wait_time = int(e.headers.get("Retry-After", 5))
        logger.warning(f"Rate limit reached. Waiting for {wait_time} seconds before retrying...")
        time.sleep(wait_time)
        return chatbot_response(user_query, chat_history)
    except Exception as e:
        error_message = f"⚠️ Error processing your query: {str(e)}"
        logger.error(error_message, exc_info=True)
        chat_history.append({"role": "assistant", "content": error_message})
        return chat_history

def build_app():
    """
    Build and launch the Gradio app.
    """
    try:
        logger.info("Building Gradio app")
        with gr.Blocks(css="app.css") as demo:
            # Initialize state
            state_bookmarks = gr.State([])

            # General Overview
            gr.Markdown("""
# 📚 SmartMarks - AI Browser Bookmarks Manager

Welcome to **SmartMarks**, your intelligent assistant for managing browser bookmarks. SmartMarks leverages AI to help you organize, search, and interact with your bookmarks seamlessly.

---

## 🚀 **How to Use SmartMarks**

SmartMarks is divided into three main sections:

1. **📂 Upload and Process Bookmarks:** Import your existing bookmarks and let SmartMarks analyze and categorize them for you.
2. **💬 Chat with Bookmarks:** Interact with your bookmarks using natural language queries to find relevant links effortlessly.
3. **🛠️ Manage Bookmarks:** View, edit, delete, and export your bookmarks with ease.

Navigate through the tabs to explore each feature in detail.
""")

            # Upload and Process Bookmarks Tab
            with gr.Tab("Upload and Process Bookmarks"):
                gr.Markdown("""
## 📂 **Upload and Process Bookmarks**

### 📝 **Steps to Upload and Process:**

1. **Upload Bookmarks File:**
   - Click on the **"📁 Upload Bookmarks HTML File"** button.
   - Select your browser's exported bookmarks HTML file from your device.

2. **Process Bookmarks:**
   - After uploading, click on the **"⚙️ Process Bookmarks"** button.
   - SmartMarks will parse your bookmarks, fetch additional information, generate summaries, and categorize each link based on predefined categories.

3. **View Processed Bookmarks:**
   - Once processing is complete, your bookmarks will be displayed in an organized and visually appealing format below.
""")

                upload = gr.File(label="📁 Upload Bookmarks HTML File", type='binary')
                process_button = gr.Button("⚙️ Process Bookmarks")
                output_text = gr.Textbox(label="✅ Output", interactive=False)
                bookmark_display = gr.HTML(label="📄 Processed Bookmarks")

            # Chat with Bookmarks Tab
            with gr.Tab("Chat with Bookmarks"):
                gr.Markdown("""
## 💬 **Chat with Bookmarks**

### 🤖 **How to Interact:**

1. **Enter Your Query:**
   - In the **"✍️ Ask about your bookmarks"** textbox, type your question or keyword related to your bookmarks.

2. **Submit Your Query:**
   - Click the **"📨 Send"** button to submit your query.

3. **Receive AI-Driven Responses:**
   - SmartMarks will analyze your query and provide relevant bookmarks that match your request.

4. **View Chat History:**
   - All your queries and the corresponding AI responses are displayed in the chat history.
""")

                chatbot = gr.Chatbot(label="💬 Chat with SmartMarks", type='messages')
                user_input = gr.Textbox(
                    label="✍️ Ask about your bookmarks",
                    placeholder="e.g., Do I have any bookmarks about AI?"
                )
                chat_button = gr.Button("📨 Send")

                chat_button.click(
                    chatbot_response,
                    inputs=[user_input, chatbot],
                    outputs=chatbot
                )

            # Manage Bookmarks Tab
            with gr.Tab("Manage Bookmarks"):
                gr.Markdown("""
## 🛠️ **Manage Bookmarks**

### 🗂️ **Features:**

1. **View Bookmarks:**
   - All your processed bookmarks are displayed here with their respective categories and summaries.

2. **Select Bookmarks:**
   - Use the checkboxes next to each bookmark to select one, multiple, or all bookmarks you wish to manage.

3. **Delete Selected Bookmarks:**
   - After selecting the desired bookmarks, click the **"🗑️ Delete Selected"** button to remove them from your list.

4. **Edit Categories:**
   - Select the bookmarks you want to re-categorize.
   - Choose a new category from the dropdown menu labeled **"🆕 New Category"**.
   - Click the **"✏️ Edit Category"** button to update their categories.

5. **Export Bookmarks:**
   - Click the **"💾 Export"** button to download your updated bookmarks as an HTML file.

6. **Refresh Bookmarks:**
   - Click the **"🔄 Refresh Bookmarks"** button to ensure the latest state is reflected in the display.
""")

                manage_output = gr.Textbox(label="🔄 Status", interactive=False)

                # CheckboxGroup for selecting bookmarks
                bookmark_selector = gr.CheckboxGroup(
                    label="✅ Select Bookmarks",
                    choices=[]
                )

                new_category = gr.Dropdown(
                    label="🆕 New Category",
                    choices=CATEGORIES,
                    value="Uncategorized"
                )
                bookmark_display_manage = gr.HTML(label="📄 Bookmarks")

                with gr.Row():
                    delete_button = gr.Button("🗑️ Delete Selected")
                    edit_category_button = gr.Button("✏️ Edit Category")
                    export_button = gr.Button("💾 Export")
                    refresh_button = gr.Button("🔄 Refresh Bookmarks")

                download_link = gr.File(label="📥 Download Exported Bookmarks")

                # Connect all the button actions
                process_button.click(
                    process_uploaded_file,
                    inputs=[upload, state_bookmarks],
                    outputs=[output_text, bookmark_display, state_bookmarks, bookmark_display, bookmark_selector]
                )

                delete_button.click(
                    delete_selected_bookmarks,
                    inputs=[bookmark_selector, state_bookmarks],
                    outputs=[manage_output, bookmark_selector, bookmark_display_manage]
                )

                edit_category_button.click(
                    edit_selected_bookmarks_category,
                    inputs=[bookmark_selector, new_category, state_bookmarks],
                    outputs=[manage_output, bookmark_selector, bookmark_display_manage, state_bookmarks]
                )

                export_button.click(
                    export_bookmarks,
                    outputs=download_link
                )

                refresh_button.click(
                    lambda state_bookmarks: (
                        [
                            f"{i+1}. {bookmark['title']} (Category: {bookmark['category']})" 
                            for i, bookmark in enumerate(state_bookmarks)
                        ],
                        display_bookmarks()
                    ),
                    inputs=[state_bookmarks],
                    outputs=[bookmark_selector, bookmark_display_manage]
                )

        logger.info("Launching Gradio app")
        demo.launch(debug=True)
    except Exception as e:
        logger.error(f"Error building Gradio app: {e}", exc_info=True)
        print(f"Error building Gradio app: {e}")

if __name__ == "__main__":
    # Start the LLM worker thread before launching the app
    llm_thread = threading.Thread(target=llm_worker, daemon=True)
    llm_thread.start()

    build_app()