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# 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
# Initialize models and variables
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
faiss_index = None # Renamed from 'index' to 'faiss_index'
bookmarks = []
fetch_cache = {}
# Helper functions
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 2000 characters to save resources
content = content[:2000]
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 process_uploaded_file(file):
global bookmarks, faiss_index
if file is None:
return "Please upload a bookmarks HTML file."
file_content = file.read().decode('utf-8')
bookmarks = parse_bookmarks(file_content)
for bookmark in bookmarks:
fetch_url_info(bookmark)
generate_summary(bookmark)
faiss_index, embeddings = vectorize_and_index(bookmarks)
return f"Successfully processed {len(bookmarks)} bookmarks."
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]:
bookmark = bookmarks[idx]
response += f"Title: {bookmark['title']}\nURL: {bookmark['url']}\nSummary: {bookmark['summary']}\n\n"
return response.strip()
def display_bookmarks():
bookmark_list = []
for i, bookmark in enumerate(bookmarks):
status = "Dead Link" if bookmark.get('dead_link') else "Active"
bookmark_list.append([i, bookmark['title'], bookmark['url'], status])
return bookmark_list
def edit_bookmark(bookmark_idx, new_title, new_url):
global faiss_index # Reference the global faiss_index variable
try:
bookmark_idx = int(bookmark_idx)
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)
return "Bookmark updated successfully."
except Exception as e:
return f"Error: {str(e)}"
def delete_bookmark(bookmark_idx):
global faiss_index # Reference the global faiss_index variable
try:
bookmark_idx = int(bookmark_idx)
bookmarks.pop(bookmark_idx)
# Rebuild the FAISS index
if bookmarks:
faiss_index, embeddings = vectorize_and_index(bookmarks)
else:
faiss_index = None # No bookmarks left
return "Bookmark deleted successfully."
except Exception as e:
return f"Error: {str(e)}"
def build_app():
with gr.Blocks() as demo:
gr.Markdown("# Bookmark Manager App")
with gr.Tab("Upload and Process Bookmarks"):
upload = gr.File(label="Upload Bookmarks HTML File")
process_button = gr.Button("Process Bookmarks")
output_text = gr.Textbox(label="Output")
process_button.click(
process_uploaded_file,
inputs=upload,
outputs=output_text
)
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"):
bookmark_table = gr.Dataframe(
headers=["Index", "Title", "URL", "Status"],
datatype=["number", "str", "str", "str"],
interactive=False
)
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")
manage_output = gr.Textbox(label="Manage Output")
refresh_button.click(
display_bookmarks,
inputs=None,
outputs=bookmark_table
)
edit_button.click(
edit_bookmark,
inputs=[index_input, new_title_input, new_url_input],
outputs=manage_output
)
delete_button.click(
delete_bookmark,
inputs=index_input,
outputs=manage_output
)
demo.launch()
if __name__ == "__main__":
build_app()