Update app.py

app.py

@@ -37,8 +37,9 @@ def parse_news(news_table):
     
     for x in news_table.findAll('tr'):
         try:
-            # Get the headline text
+            # Get the headline text and link
             text = x.a.get_text()
+            link = x.a['href']
             # Get the date and time from the first <td> tag
             date_scrape = x.td.text.strip().split()
             
@@ -55,14 +56,14 @@ def parse_news(news_table):
             datetime_parsed = parser.parse(datetime_str)
 
             # Append the parsed news to the list
-            parsed_news.append([datetime_parsed, text])
+            parsed_news.append([datetime_parsed, text, link])
             
         except Exception as e:
             print("Error parsing news:", e)
             continue
     
     # Convert the list to a DataFrame
-    columns = ['datetime', 'headline']
+    columns = ['datetime', 'headline', 'link']
     parsed_news_df = pd.DataFrame(parsed_news, columns=columns)
     
     return parsed_news_df
@@ -123,30 +124,35 @@ df = pd.DataFrame({'datetime': datetime.datetime.now(), 'ticker': ticker}, index
 
 
 try:
-	st.subheader("Hourly and Daily Sentiment of {} Stock".format(ticker))
-	news_table = get_news(ticker)
-	parsed_news_df = parse_news(news_table)
-	print(parsed_news_df)
-	parsed_and_scored_news = score_news(parsed_news_df)
-	fig_hourly = plot_hourly_sentiment(parsed_and_scored_news, ticker)
-	fig_daily = plot_daily_sentiment(parsed_and_scored_news, ticker) 
-	 
-	st.plotly_chart(fig_hourly)
-	st.plotly_chart(fig_daily)
-
-	description = """
-		The above chart averages the sentiment scores of {} stock hourly and daily.
-		The table below gives each of the most recent headlines of the stock and the negative, neutral, positive and an aggregated sentiment score.
-		The news headlines are obtained from the FinViz website.
-		Sentiments are given by the nltk.sentiment.vader Python library.
-		""".format(ticker)
-		
-	st.write(description)	 
-	st.table(parsed_and_scored_news)
-	
+    st.subheader("Hourly and Daily Sentiment of {} Stock".format(ticker))
+    news_table = get_news(ticker)
+    parsed_news_df = parse_news(news_table)
+    print(parsed_news_df)
+    parsed_and_scored_news = score_news(parsed_news_df)
+    fig_hourly = plot_hourly_sentiment(parsed_and_scored_news, ticker)
+    fig_daily = plot_daily_sentiment(parsed_and_scored_news, ticker) 
+     
+    st.plotly_chart(fig_hourly)
+    st.plotly_chart(fig_daily)
+
+    description = """
+        The above chart averages the sentiment scores of {} stock hourly and daily.
+        The table below gives each of the most recent headlines of the stock and the negative, neutral, positive and an aggregated sentiment score.
+        The news headlines are obtained from the FinViz website.
+        Sentiments are given by the nltk.sentiment.vader Python library.
+        """.format(ticker)
+        
+    st.write(description)     
+    
+    # Convert links to clickable HTML
+    parsed_and_scored_news['link'] = parsed_and_scored_news['link'].apply(lambda x: f'<a href="{x}" target="_blank">Link</a>')
+    
+    # Display the table with the new link column
+    st.write(parsed_and_scored_news.to_html(escape=False), unsafe_allow_html=True)
+    
 except Exception as e:
-	print(str(e))
-	st.write("Enter a correct stock ticker, e.g. 'AAPL' above and hit Enter.")	
+    print(str(e))
+    st.write("Enter a correct stock ticker, e.g. 'AAPL' above and hit Enter.")    
 
 hide_streamlit_style = """
 <style>
@@ -154,4 +160,4 @@ hide_streamlit_style = """
 footer {visibility: hidden;}
 </style>
 """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)