# 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 concurrent.futures from concurrent.futures import ThreadPoolExecutor import threading from queue import Queue, Empty import json # 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 Groq Cloud API key and base URL GROQ_API_KEY = os.getenv('GROQ_API_KEY') if not GROQ_API_KEY: logger.error("GROQ_API_KEY environment variable not set.") openai.api_key = GROQ_API_KEY openai.api_base = "https://api.groq.com/openai/v1" # Initialize global variables for rate limiting api_lock = threading.Lock() last_api_call_time = 0 # Rate Limiter Configuration RPM_LIMIT = 30 # Requests per minute TPM_LIMIT = 40000 # Tokens per minute # 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(0.1) # 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 llm_worker(): """ Worker thread to process LLM tasks from the queue while respecting rate limits. """ logger.info("LLM worker started.") while True: try: bookmark = llm_queue.get(timeout=60) # Wait for a task except Empty: continue # No task, continue waiting if bookmark is None: logger.info("LLM worker shutting down.") break # Exit signal try: # Rate Limiting rpm_bucket.wait_for_token() # Estimate tokens: prompt + max_tokens # Here, we assume max_tokens=150 tpm_bucket.wait_for_token(tokens=150) html_content = bookmark.get('html_content', '') soup = BeautifulSoup(html_content, 'html.parser') metadata = get_page_metadata(soup) main_content = extract_main_content(soup) # Prepare content for the prompt content_parts = [] if metadata['title']: content_parts.append(f"Title: {metadata['title']}") if metadata['description']: content_parts.append(f"Description: {metadata['description']}") if metadata['keywords']: content_parts.append(f"Keywords: {metadata['keywords']}") if main_content: content_parts.append(f"Main Content: {main_content}") content_text = '\n'.join(content_parts) # Detect insufficient or erroneous content error_keywords = ['Access Denied', 'Security Check', 'Cloudflare', 'captcha', 'unusual traffic'] if not content_text or len(content_text.split()) < 50: use_prior_knowledge = True logger.info(f"Content for {bookmark.get('url')} is insufficient. Instructing LLM to use prior knowledge.") elif any(keyword.lower() in content_text.lower() for keyword in error_keywords): use_prior_knowledge = True logger.info(f"Content for {bookmark.get('url')} contains error messages. Instructing LLM to use prior knowledge.") else: use_prior_knowledge = False if use_prior_knowledge: prompt = f""" You are a knowledgeable assistant with up-to-date information as of 2023. URL: {bookmark.get('url')} Provide: 1. A concise summary (max two sentences) about this website. 2. Assign the most appropriate category from the list below. Categories: {', '.join([f'"{cat}"' for cat in CATEGORIES])} Format: Please provide your response in the following JSON format: {{ "summary": "Your summary here.", "category": "One category from the list." }} """ else: prompt = f""" You are an assistant that creates concise webpage summaries and assigns categories. Content: {content_text} Provide: 1. A concise summary (max two sentences) focusing on the main topic. 2. Assign the most appropriate category from the list below. Categories: {', '.join([f'"{cat}"' for cat in CATEGORIES])} Format: Please provide your response in the following JSON format: {{ "summary": "Your summary here.", "category": "One category from the list." }} """ response = openai.ChatCompletion.create( model='llama-3.1-70b-versatile', messages=[ {"role": "user", "content": prompt} ], max_tokens=150, 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) summary = json_response.get('summary', '').strip() category = json_response.get('category', '').strip() # Validate and assign if not summary: summary = metadata.get('description') or metadata.get('title') or '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, bookmark['url']) except json.JSONDecodeError: logger.error(f"Failed to parse JSON response for {bookmark.get('url')}. Using fallback methods.") # Fallback methods bookmark['summary'] = metadata.get('description') or metadata.get('title') or 'No summary available.' bookmark['category'] = categorize_based_on_summary(bookmark['summary'], bookmark['url']) # Additional keyword-based validation bookmark['category'] = validate_category(bookmark) logger.info("Successfully generated summary and assigned category") except openai.error.RateLimitError as e: logger.warning(f"LLM Rate limit reached while processing {bookmark.get('url')}. Retrying later...") # Re-enqueue the bookmark for retry llm_queue.put(bookmark) time.sleep(60) # Wait before retrying except Exception as e: logger.error(f"Error generating summary and assigning category for {bookmark.get('url')}: {e}", exc_info=True) # Assign default values on failure bookmark['summary'] = 'No summary available.' bookmark['category'] = 'Uncategorized' finally: llm_queue.task_done() 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

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 generate_summary_and_assign_category(bookmark): """ Generate a concise summary and assign a category using a single LLM call. This function decides whether to use metadata or enqueue an LLM call. """ # Check if metadata can provide a summary description = bookmark.get('description', '').strip() title = bookmark.get('title', '').strip() if description: # Use description as summary bookmark['summary'] = description # Assign category based on description or title bookmark['category'] = categorize_based_on_summary(description, bookmark['url']) logger.info(f"Summary derived from metadata for {bookmark.get('url')}") elif title: # Use title as summary bookmark['summary'] = title # Assign category based on title bookmark['category'] = categorize_based_on_summary(title, bookmark['url']) logger.info(f"Summary derived from title for {bookmark.get('url')}") else: # Enqueue for LLM processing logger.info(f"No sufficient metadata for {bookmark.get('url')}. Enqueuing for LLM summary generation.") 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['description'] = '' bookmark['html_content'] = '' logger.warning(f"Dead link detected: {url} with status {response.status_code}") else: bookmark['dead_link'] = False bookmark['html_content'] = content bookmark['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;" elif bookmark.get('slow_link'): status = "⏳ Slow Response" card_style = "border: 2px solid orange;" text_style = "color: white;" else: status = "✅ Active" card_style = "border: 2px solid green;" text_style = "color: white;" title = bookmark['title'] url = bookmark['url'] etag = bookmark.get('etag', 'N/A') summary = bookmark.get('summary', '') 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'''

{index}. {title} {status}

Category: {category}

URL: {url}

ETag: {etag}

Summary: {summary}

''' 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) # Process bookmarks for summary and category logger.info("Processing bookmarks for summaries and categories") 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("Bookmarks

Bookmarks

", '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() tpm_bucket.wait_for_token(tokens=300) # Assuming max_tokens=300 per request 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', 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", 60)) logger.warning(f"Chatbot 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()