Update app.py

app.py

@@ -1,22 +1,40 @@
 import gradio as gr
 import requests
 from bs4 import BeautifulSoup
+from transformers import pipeline
 
-def summarize_website(url):
+# Load summarization pipeline from Hugging Face
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+
+def scrape_website(url):
+    """Extracts text from a website."""
     try:
-        response = requests.get(url)
+        response = requests.get(url, timeout=10)
         soup = BeautifulSoup(response.text, "html.parser")
         paragraphs = soup.find_all("p")
-        
-        # Extract more content (e.g., first 10 paragraphs)
-        text = "\n\n".join([p.get_text() for p in paragraphs[:10]])
-        
-        # Format text as Markdown
-        markdown_summary = f"## Website Summary\n\n{text}" if text else "No content found."
-        
-        return markdown_summary
+        text = " ".join([p.get_text() for p in paragraphs])
+        return text if text else "No content found."
     except Exception as e:
-        return f"**Error:** {str(e)}"
+        return f"Error: {str(e)}"
+
+def summarize_website(url):
+    """Scrapes website and summarizes the extracted content."""
+    extracted_text = scrape_website(url)
+    
+    if "Error:" in extracted_text or len(extracted_text.split()) < 50:
+        return "Could not extract enough text to summarize."
+    
+    # Summarize using Hugging Face model
+    summary = summarizer(extracted_text, max_length=200, min_length=50, do_sample=False)
+    return f"**Summary:**\n\n{summary[0]['summary_text']}"
+
+# Gradio interface
+iface = gr.Interface(
+    fn=summarize_website,
+    inputs="text",
+    outputs="markdown",
+    title="AI-Powered Website Summarizer",
+    description="Enter a website URL, and this tool will summarize its content using an AI model."
+)
 
-iface = gr.Interface(fn=summarize_website, inputs="text", outputs=gr.Markdown(), title="Website Summarizer")
 iface.launch()