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

# Load summarization pipeline
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6")

def scrape_website(url):
    """Enhanced extraction with metadata support"""
    try:
        headers = {'User-Agent': 'Mozilla/5.0'}
        response = requests.get(url, headers=headers, timeout=10)
        response.raise_for_status()
        
        soup = BeautifulSoup(response.text, "html.parser")
        
        # Extract title and meta description
        title = soup.title.string.strip() if soup.title else ""
        meta_desc = soup.find("meta", attrs={"name": "description"})
        meta_desc = meta_desc["content"].strip() if meta_desc else ""

        # Extract main text content
        text_elements = soup.find_all(['p', 'article', 'main', 'section'])
        text = " ".join([e.get_text(strip=True, separator=' ') for e in text_elements])
        
        full_content = f"{title}\n{meta_desc}\n{text}".strip()
        
        return full_content if full_content else "No meaningful content found."
        
    except Exception as e:
        return f"Scraping Error: {str(e)}"

def truncate_text(text, max_tokens=1024):
    """Properly truncates text at the token level."""
    tokens = tokenizer.tokenize(text)
    return tokenizer.convert_tokens_to_string(tokens[:max_tokens])

def summarize_website(url):
    """Updated function with real-time status"""
    try:
        extracted_text = scrape_website(url)
        
        if "Error" in extracted_text:
            return "❌ " + extracted_text

        if len(extracted_text.split()) < 50:
            return "⚠️ Error: Insufficient content for summarization (minimum 50 words required)"
        
        truncated_text = truncate_text(extracted_text)
        
        summary = summarizer(
            truncated_text,
            max_length=250,  # Increased summary length
            min_length=80,    # Ensuring more detailed output
            do_sample=False
        )
        
        return f"## 📝 Summary\n\n{summary[0]['summary_text']}"
    
    except Exception as e:
        return f"⛔ Summarization Error: {str(e)}"

# Custom CSS for better mobile experience
css = """
@media screen and (max-width: 600px) {
    .container { padding: 10px !important; }
    .input-box textarea { font-size: 18px !important; }
    .gr-button { width: 100% !important; }
}
"""

# Mobile-optimized interface with real-time updates
with gr.Blocks(theme=gr.themes.Soft(), css=css, title="Website Summarizer") as app:
    gr.Markdown("# 🌐 AI Website Summarizer")
    gr.Markdown("Paste any website URL below to get an instant AI-powered summary!")
    
    with gr.Row():
        url_input = gr.Textbox(
            label="Website URL",
            placeholder="Enter full URL (https://...)",
            lines=1,
            max_lines=1,
            elem_id="input-box"
        )
    
    with gr.Row():
        submit_btn = gr.Button("Generate Summary 🚀", variant="primary")
        clear_btn = gr.Button("Clear 🔄")
    
    status = gr.Markdown("🔄 Ready for input...", elem_id="status-msg")
    output = gr.Markdown()
    
    gr.Examples(
        examples=[
            ["https://en.wikipedia.org/wiki/Large_language_model"],
            ["https://www.bbc.com/news/technology-66510295"]
        ],
        inputs=url_input,
        label="Try these examples:",
        examples_per_page=2
    )
    
    submit_btn.click(
        fn=summarize_website,
        inputs=url_input,
        outputs=[output],
        api_name="summarize"
    )
    
    clear_btn.click(
        fn=lambda: ("", "🔄 Ready for input..."),
        inputs=None,
        outputs=[url_input, status],
        queue=False
    )

# Mobile-friendly deployment
app.launch(
    server_name="0.0.0.0", 
    server_port=7860, 
    favicon_path="https://www.svgrepo.com/show/355037/huggingface.svg"
)