Update app.py

app.py

@@ -45,6 +45,81 @@ llm = ChatGoogleGenerativeAI(model="gemini-2.5-pro",google_api_key = GOOGLE_API_
 
 activities = st.sidebar.selectbox("Select", ["Symbol Analysis", "News Sentiment"])
 
+    def clean_google_news_url(url: str):
+        """
+        Cleans Google News redirect URLs by removing tracking parameters like &ved= and &usg=.
+        Keeps content up to .html or .cms.
+        """
+        for ext in [".html", ".cms"]:
+            if ext in url:
+                return url.split(ext)[0] + ext
+        return url.split("&")[0]  # fallback
+    def get_google_news_documents(query: str, max_articles: int = 10, timeout: int = 10):
+        """
+        Fetches news articles from Google News and returns a list of LangChain Document objects,
+        using requests + BeautifulSoup instead of newspaper3k.
+    
+        Args:
+            query (str): Search query for Google News.
+            max_articles (int): Number of articles to fetch.
+            timeout (int): Timeout for HTTP requests.
+    
+        Returns:
+            List[Document]: Parsed article content as LangChain Document objects.
+        """
+        st.caption(f"Fetching articles for query: '{query}'")
+        
+        googlenews = GoogleNews(lang="en")
+        # Set time range to last `days` days
+        end_date = datetime.today()
+        days = 2
+        start_date = end_date - timedelta(days=days)
+        googlenews.set_time_range(start_date.strftime("%m/%d/%Y"), end_date.strftime("%m/%d/%Y"))
+
+        googlenews.search(query)
+        articles = googlenews.result()
+        
+        documents = []
+        i=1
+        for article in articles:
+            
+                
+            url = clean_google_news_url(article.get("link"))
+            try:
+                with st.spinner(f" Trying URL... {url}"):
+                    # st.caption()
+                    response = requests.get(url, timeout=timeout, headers={
+                        "User-Agent": "Mozilla/5.0"
+                    })
+                    response.raise_for_status()
+                    soup = BeautifulSoup(response.text, "html.parser")
+        
+                    # Extract visible <p> tags to simulate main content
+                    paragraphs = soup.find_all("p")
+                    content = "\n".join([p.get_text(strip=True) for p in paragraphs if p.get_text(strip=True)])
+        
+                    if content and len(content) > 200:  # crude filter to skip empty or useless pages
+                        doc = Document(
+                            page_content=content,
+                            metadata={
+                                "source": "Google News",
+                                "title": article.get("title", ""),
+                                "published": article.get("date", ""),
+                                "link": url,
+                            }
+                        )
+                        documents.append(doc)
+                        
+                    if i > max_articles:
+                        st.caption("max articles reached...")
+                        break
+        
+                    i+=1
+            except Exception as e:
+                # st.error(f"Failed to fetch or parse article: {url} — Error: {e}")
+                pass
+    
+        return documents
 
 if activities == "Symbol Analysis":
     ticker_user = st.text_input("Enter Ticker for NSE Stocks","")
@@ -264,7 +339,9 @@ if activities == "Symbol Analysis":
         # else:
         #     st.warning(f"Failed to retrieve image. Status code: {response.status_code}")
         #     st.warning("Response:", response.text)
-        
+
+        google_docs = get_google_news_documents(f"Trending News for {ticker_user}", max_articles=10)
+        docs.extend(google_docs)
         llm_prompt = PromptTemplate.from_template(llm_prompt_template)
     
         llm_chain = LLMChain(llm=llm,prompt=llm_prompt)
@@ -424,82 +501,6 @@ elif activities=="News Sentiment":
     """
 
 
-    def clean_google_news_url(url: str):
-        """
-        Cleans Google News redirect URLs by removing tracking parameters like &ved= and &usg=.
-        Keeps content up to .html or .cms.
-        """
-        for ext in [".html", ".cms"]:
-            if ext in url:
-                return url.split(ext)[0] + ext
-        return url.split("&")[0]  # fallback
-    def get_google_news_documents(query: str, max_articles: int = 10, timeout: int = 10):
-        """
-        Fetches news articles from Google News and returns a list of LangChain Document objects,
-        using requests + BeautifulSoup instead of newspaper3k.
-    
-        Args:
-            query (str): Search query for Google News.
-            max_articles (int): Number of articles to fetch.
-            timeout (int): Timeout for HTTP requests.
-    
-        Returns:
-            List[Document]: Parsed article content as LangChain Document objects.
-        """
-        st.caption(f"Fetching articles for query: '{query}'")
-        
-        googlenews = GoogleNews(lang="en")
-        # Set time range to last `days` days
-        end_date = datetime.today()
-        days = 2
-        start_date = end_date - timedelta(days=days)
-        googlenews.set_time_range(start_date.strftime("%m/%d/%Y"), end_date.strftime("%m/%d/%Y"))
-
-        googlenews.search(query)
-        articles = googlenews.result()
-        
-        documents = []
-        i=1
-        for article in articles:
-            
-                
-            url = clean_google_news_url(article.get("link"))
-            try:
-                with st.spinner(f" Trying URL... {url}"):
-                    # st.caption()
-                    response = requests.get(url, timeout=timeout, headers={
-                        "User-Agent": "Mozilla/5.0"
-                    })
-                    response.raise_for_status()
-                    soup = BeautifulSoup(response.text, "html.parser")
-        
-                    # Extract visible <p> tags to simulate main content
-                    paragraphs = soup.find_all("p")
-                    content = "\n".join([p.get_text(strip=True) for p in paragraphs if p.get_text(strip=True)])
-        
-                    if content and len(content) > 200:  # crude filter to skip empty or useless pages
-                        doc = Document(
-                            page_content=content,
-                            metadata={
-                                "source": "Google News",
-                                "title": article.get("title", ""),
-                                "published": article.get("date", ""),
-                                "link": url,
-                            }
-                        )
-                        documents.append(doc)
-                        
-                    if i > max_articles:
-                        st.caption("max articles reached...")
-                        break
-        
-                    i+=1
-            except Exception as e:
-                # st.error(f"Failed to fetch or parse article: {url} — Error: {e}")
-                pass
-    
-        return documents
-
     google_docs = get_google_news_documents("Indian Stock market news NSE, Stocks in Action, Stocks in News, Stocks to Buy in next few weeks", max_articles=10)
     docs.extend(google_docs)
     # st.write(docs)
@@ -528,8 +529,8 @@ elif activities=="News Sentiment":
     
     # Layout
     for stock in top_picks:
-        col1,col2,col3, col4 = st.columns([1,1,1, 1])
         st.subheader(f"{stock['company']} ({stock['ticker']})")
+        col1,col2,col3, col4 = st.columns([1,1,1, 1])
         with col1:
             st.markdown(f"**📰 Critical News:** {stock['critical_news']}")
         with col2: