Update app.py

app.py

@@ -12,12 +12,15 @@ import requests
 
 st.set_page_config(page_title="Stock News Sentiment Analyzer", layout="wide")
 
-def verify_link(url):
-    try:
-        response = requests.head(url, timeout=5)
-        return response.status_code == 200
-    except requests.RequestException:
-        return False
+def verify_link(url, timeout=10, retries=3):
+    for _ in range(retries):
+        try:
+            response = requests.head(url, timeout=timeout, allow_redirects=True)
+            if 200 <= response.status_code < 300:
+                return True
+        except requests.RequestException:
+            continue
+    return False
 
 def get_news(ticker):
     finviz_url = 'https://finviz.com/quote.ashx?t='
@@ -83,15 +86,33 @@ def plot_daily_sentiment(parsed_and_scored_news, ticker):
     fig = px.bar(mean_scores, x=mean_scores.index, y='sentiment_score', title=f'{ticker} Daily Sentiment Scores')
     return fig
 
+def get_recommendation(sentiment_scores):
+    avg_sentiment = sentiment_scores['sentiment_score'].mean()
+    
+    if avg_sentiment >= 0.05:
+        return f"Positive sentiment (Score: {avg_sentiment:.2f}). The recent news suggests a favorable outlook for this stock. Consider buying or holding if you already own it."
+    elif avg_sentiment <= -0.05:
+        return f"Negative sentiment (Score: {avg_sentiment:.2f}). The recent news suggests caution. Consider selling or avoiding this stock for now."
+    else:
+        return f"Neutral sentiment (Score: {avg_sentiment:.2f}). The recent news doesn't show a strong bias. Consider holding if you own the stock, or watch for more definitive trends before making a decision."
+
 st.header("Stock News Sentiment Analyzer")
 
 ticker = st.text_input('Enter Stock Ticker', '').upper()
 
 try:
-    st.subheader(f"Hourly and Daily Sentiment of {ticker} Stock")
+    st.subheader(f"Sentiment Analysis and Recommendation for {ticker} Stock")
     news_table = get_news(ticker)
     parsed_news_df = parse_news(news_table)
     parsed_and_scored_news = score_news(parsed_news_df)
+    
+    # Generate and display recommendation
+    recommendation = get_recommendation(parsed_and_scored_news)
+    st.write(recommendation)
+    
+    # Display a disclaimer
+    st.warning("Disclaimer: This recommendation is based solely on recent news sentiment and should not be considered as financial advice. Always do your own research and consult with a qualified financial advisor before making investment decisions.")
+    
     fig_hourly = plot_hourly_sentiment(parsed_and_scored_news, ticker)
     fig_daily = plot_daily_sentiment(parsed_and_scored_news, ticker) 
      
@@ -99,7 +120,7 @@ try:
     st.plotly_chart(fig_daily)
 
     description = f"""
-        The above chart averages the sentiment scores of {ticker} stock hourly and daily.
+        The above charts average the sentiment scores of {ticker} 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.