app.py

import gradio as gr
import requests
from bs4 import BeautifulSoup
from transformers import pipeline

# Use a more lightweight model for Hugging Face Spaces
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")

def scrape_website(url):
    """Extracts text from a website with error handling"""
    try:
        headers = {'User-Agent': 'Mozilla/5.0'}  # Add headers to prevent 403 errors
        response = requests.get(url, headers=headers, timeout=10)
        response.raise_for_status()  # Raise HTTP errors
        
        soup = BeautifulSoup(response.text, "html.parser")
        
        # Extract text from common content-containing tags
        text_elements = soup.find_all(['p', 'article', 'main', 'section'])
        text = " ".join([e.get_text(strip=True, separator=' ') for e in text_elements])
        
        return text if text.strip() else "No content found"
        
    except Exception as e:
        return f"Scraping Error: {str(e)}"

def summarize_website(url):
    """Handles the full summarization pipeline"""
    try:
        extracted_text = scrape_website(url)
        
        if "Error" in extracted_text:
            return extracted_text
            
        # Check minimum text length
        if len(extracted_text.split()) < 50:
            return "Error: Insufficient content for summarization (minimum 50 words required)"
            
        # Truncate text to model's max input length (1024 tokens for DistilBART)
        max_input_length = 1000  # Conservative estimate for token count
        truncated_text = extracted_text[:max_input_length]
        
        # Generate summary
        summary = summarizer(
            truncated_text,
            max_length=200,
            min_length=50,
            do_sample=False,
            truncation=True  # Ensure truncation is enabled
        )
        
        return f"**Summary:**\n\n{summary[0]['summary_text']}"
        
    except Exception as e:
        return f"Summarization Error: {str(e)}"

# Gradio interface with improved configuration
iface = gr.Interface(
    fn=summarize_website,
    inputs=gr.Textbox(label="Website URL", placeholder="Enter full URL (including https://)..."),
    outputs=gr.Markdown(),
    title="AI-Powered Website Summarizer",
    description="Enter a website URL to get an AI-generated summary of its content",
    examples=[
        ["https://en.wikipedia.org/wiki/Large_language_model"],
        ["https://www.bbc.com/news/technology-66510295"]
    ]
)

iface.launch()