Update app.py

app.py

@@ -1,3 +1,61 @@
 import gradio as gr
+import requests 
 
-gr.load("models/meta-llama/Llama-3.2-1B").launch()
+
+import requests
+from bs4 import BeautifulSoup
+import re
+
+
+from transformers import pipeline
+pipe = pipeline("text-generation", model="meta-llama/Llama-3.2-1B")
+
+def get_clean_text(url):
+    try:
+        # Send a GET request to the URL
+        response = requests.get(url)
+        
+        # Check if the request was successful
+        if response.status_code != 200:
+            return None
+        
+        # Parse the HTML content using BeautifulSoup
+        soup = BeautifulSoup(response.text, 'html.parser')
+        
+        # Remove all script and style elements
+        for script in soup(["script", "style"]):
+            script.decompose()
+        
+        # Get the text from the HTML content
+        text = soup.get_text()
+        
+        # Break the text into lines and remove leading and trailing whitespace
+        lines = (line.strip() for line in text.splitlines())
+        
+        # Break multi-headlines into a line each
+        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+        
+        # Remove blank lines
+        text = '\n'.join(chunk for chunk in chunks if chunk)
+        
+        # Remove extra whitespace
+        text = re.sub(r'\s+', ' ', text)
+        
+        return text
+    
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return None
+
+
+
+def summarize(alink):
+    summary = ""
+    alink = "https://www.aljazeeramubasher.net/palestine/"
+    text = get_clean_text
+    summary = pipe(f"summarize the following news into bullet points {text}")
+    return summary
+
+gr.interface(fn=summarize, input="text", output="text")
+
+# gr.load("models/meta-llama/Llama-3.2-1B").launch()