app.py

# app.py

import gradio as gr
from bs4 import BeautifulSoup
import requests
from transformers import pipeline
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import pandas as pd

# Initialize models and variables
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-6-6")  # Using a smaller model for resource efficiency
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
faiss_index = None
bookmarks = []
fetch_cache = {}

def parse_bookmarks(file_content):
    soup = BeautifulSoup(file_content, 'html.parser')
    extracted_bookmarks = []
    for link in soup.find_all('a'):
        url = link.get('href')
        title = link.text
        if url and title:
            extracted_bookmarks.append({'url': url, 'title': title})
    return extracted_bookmarks

def fetch_url_info(bookmark):
    url = bookmark['url']
    if url in fetch_cache:
        bookmark.update(fetch_cache[url])
        return bookmark

    try:
        response = requests.get(url, timeout=5)
        bookmark['etag'] = response.headers.get('ETag', 'N/A')
        bookmark['status_code'] = response.status_code

        if response.status_code >= 400:
            bookmark['dead_link'] = True
            bookmark['content'] = ''
        else:
            bookmark['dead_link'] = False
            soup = BeautifulSoup(response.content, 'html.parser')
            meta_tags = {meta.get('name', ''): meta.get('content', '') for meta in soup.find_all('meta')}
            bookmark['meta_tags'] = meta_tags
            bookmark['content'] = soup.get_text(separator=' ', strip=True)
    except Exception as e:
        bookmark['dead_link'] = True
        bookmark['etag'] = 'N/A'
        bookmark['status_code'] = 'N/A'
        bookmark['meta_tags'] = {}
        bookmark['content'] = ''
    finally:
        fetch_cache[url] = {
            'etag': bookmark.get('etag'),
            'status_code': bookmark.get('status_code'),
            'dead_link': bookmark.get('dead_link'),
            'meta_tags': bookmark.get('meta_tags'),
            'content': bookmark.get('content'),
        }
    return bookmark

def generate_summary(bookmark):
    content = bookmark.get('content', '')
    if content:
        # Limit content to first 500 characters to save resources
        content = content[:500]
        summary = summarizer(content, max_length=50, min_length=25, do_sample=False)
        bookmark['summary'] = summary[0]['summary_text']
    else:
        bookmark['summary'] = 'No content available to summarize.'
    return bookmark

def vectorize_and_index(bookmarks):
    summaries = [bookmark['summary'] for bookmark in bookmarks]
    embeddings = embedding_model.encode(summaries)
    dimension = embeddings.shape[1]
    faiss_idx = faiss.IndexFlatL2(dimension)
    faiss_idx.add(np.array(embeddings))
    return faiss_idx, embeddings

def display_bookmarks():
    data = []
    for i, bookmark in enumerate(bookmarks):
        status = "Dead Link" if bookmark.get('dead_link') else "Active"
        css_class = "dead-link" if bookmark.get('dead_link') else ""
        data.append({
            'Index': i,
            'Title': bookmark['title'],
            'URL': f"<a href='{bookmark['url']}' target='_blank'>{bookmark['url']}</a>",
            'Status': status,
            'ETag': bookmark.get('etag', 'N/A'),
            'Summary': bookmark.get('summary', ''),
            'css_class': css_class
        })
    df = pd.DataFrame(data)
    return df

def process_uploaded_file(file):
    global bookmarks, faiss_index
    if file is None:
        return "Please upload a bookmarks HTML file.", pd.DataFrame()
    try:
        # Decode the binary data to a string
        file_content = file.decode('utf-8')
    except UnicodeDecodeError:
        return "Error decoding the file. Please ensure it's a valid HTML file.", pd.DataFrame()

    bookmarks = parse_bookmarks(file_content)

    if not bookmarks:
        return "No bookmarks found in the uploaded file.", pd.DataFrame()

    for bookmark in bookmarks:
        fetch_url_info(bookmark)
        generate_summary(bookmark)

    faiss_index, embeddings = vectorize_and_index(bookmarks)
    message = f"Successfully processed {len(bookmarks)} bookmarks."
    bookmark_df = display_bookmarks()
    return message, bookmark_df

def chatbot_response(user_query):
    if faiss_index is None or not bookmarks:
        return "No bookmarks available. Please upload and process your bookmarks first."

    # Vectorize user query
    user_embedding = embedding_model.encode([user_query])
    D, I = faiss_index.search(np.array(user_embedding), k=5)  # Retrieve top 5 matches

    # Generate response
    response = ""
    for idx in I[0]:
        if idx < len(bookmarks):
            bookmark = bookmarks[idx]
            response += f"Title: {bookmark['title']}\nURL: {bookmark['url']}\nSummary: {bookmark['summary']}\n\n"
    return response.strip()

def edit_bookmark(bookmark_idx, new_title, new_url):
    global faiss_index
    try:
        bookmark_idx = int(bookmark_idx)
        if bookmark_idx < 0 or bookmark_idx >= len(bookmarks):
            return "Invalid bookmark index.", display_bookmarks()
        bookmarks[bookmark_idx]['title'] = new_title
        bookmarks[bookmark_idx]['url'] = new_url
        fetch_url_info(bookmarks[bookmark_idx])
        generate_summary(bookmarks[bookmark_idx])
        # Rebuild the FAISS index
        faiss_index, embeddings = vectorize_and_index(bookmarks)
        message = "Bookmark updated successfully."
        updated_df = display_bookmarks()
        return message, updated_df
    except Exception as e:
        return f"Error: {str(e)}", display_bookmarks()

def delete_bookmark(bookmark_idx):
    global faiss_index
    try:
        bookmark_idx = int(bookmark_idx)
        if bookmark_idx < 0 or bookmark_idx >= len(bookmarks):
            return "Invalid bookmark index.", display_bookmarks()
        bookmarks.pop(bookmark_idx)
        # Rebuild the FAISS index
        if bookmarks:
            faiss_index, embeddings = vectorize_and_index(bookmarks)
        else:
            faiss_index = None
        message = "Bookmark deleted successfully."
        updated_df = display_bookmarks()
        return message, updated_df
    except Exception as e:
        return f"Error: {str(e)}", display_bookmarks()

def build_app():
    with gr.Blocks(css="app.css") as demo:
        gr.Markdown("<h1 style='text-align: center;'>Bookmark Manager App</h1>")

        with gr.Tab("Upload and Process Bookmarks"):
            upload = gr.File(label="Upload Bookmarks HTML File", type='binary')
            process_button = gr.Button("Process Bookmarks")
            output_text = gr.Textbox(label="Output")
            bookmark_table = gr.HTML(label="Bookmarks")

            def update_bookmark_table(file):
                message, df = process_uploaded_file(file)
                html_table = df.to_html(escape=False, index=False)
                return message, html_table

            process_button.click(
                update_bookmark_table,
                inputs=upload,
                outputs=[output_text, bookmark_table]
            )

        with gr.Tab("Chat with Bookmarks"):
            user_input = gr.Textbox(label="Ask about your bookmarks")
            chat_output = gr.Textbox(label="Chatbot Response")
            chat_button = gr.Button("Send")

            chat_button.click(
                chatbot_response,
                inputs=user_input,
                outputs=chat_output
            )

        with gr.Tab("Manage Bookmarks"):
            manage_output = gr.Textbox(label="Manage Output")
            bookmark_table_manage = gr.HTML(label="Bookmarks")
            refresh_button = gr.Button("Refresh Bookmark List")

            with gr.Row():
                index_input = gr.Number(label="Bookmark Index")
                new_title_input = gr.Textbox(label="New Title")
                new_url_input = gr.Textbox(label="New URL")

            edit_button = gr.Button("Edit Bookmark")
            delete_button = gr.Button("Delete Bookmark")

            def update_manage_table():
                df = display_bookmarks()
                html_table = df.to_html(escape=False, index=False)
                return html_table

            refresh_button.click(
                update_manage_table,
                inputs=None,
                outputs=bookmark_table_manage
            )

            edit_button.click(
                edit_bookmark,
                inputs=[index_input, new_title_input, new_url_input],
                outputs=[manage_output, bookmark_table_manage]
            )

            delete_button.click(
                delete_bookmark,
                inputs=index_input,
                outputs=[manage_output, bookmark_table_manage]
            )

            # Initial load of the bookmarks table
            bookmark_table_manage.value = update_manage_table()

    demo.launch()

if __name__ == "__main__":
    build_app()