Update app.py

app.py

@@ -1,18 +1,34 @@
 import streamlit as st
 from transformers import pipeline
+import requests
+from bs4 import BeautifulSoup
 
 # Initialize a text generation pipeline
 generator = pipeline('text-generation', model='dbmdz/german-gpt2')
 
+# Define a function to fetch trending news related to a specific niche
+def fetch_trending_news(niche):
+    url = f"https://www.google.com/search?q={niche}+news&tbs=qdr:d"
+    headers = {
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"}
+    response = requests.get(url, headers=headers)
+    soup = BeautifulSoup(response.content, "html.parser")
+    news_items = soup.find_all("div", class_="BNeawe vvjwJb AP7Wnd")
+    trending_news = [item.text for item in news_items[:5]]  # Extract the top 5 news items
+    return trending_news
+
 # Define the pages
 def page_trending_niche():
+    st.title("What is trending in my niche?")
+    st.image('Robot.png', use_column_width=True)
     
-    # Using st.columns to create a two-column layout
-    col1, col2 = st.columns([3, 1])
-    with col1:
-        st.title("What is trending in my niche?")
-    with col2:
-        st.image('Robot.png', use_column_width=True)
+    niche = st.text_input('Enter your niche', 'German clinics')
+    st.write(f"Trending news in {niche}:")
+    
+    trending_news = fetch_trending_news(niche)
+    for idx, news_item in enumerate(trending_news, start=1):
+        st.write(f"{idx}. {news_item}")
+
 
 
 # Define the pages