Update app.py

app.py

@@ -9,31 +9,9 @@ nltk.downloader.download('vader_lexicon')
 from nltk.sentiment.vader import SentimentIntensityAnalyzer
 import datetime
 import requests
-from http.server import SimpleHTTPRequestHandler, HTTPServer
-from socketserver import ThreadingMixIn
-import threading
-
-# Enable CORS with a custom handler
-class CORSRequestHandler(SimpleHTTPRequestHandler):
-    def end_headers(self):
-        self.send_header('Access-Control-Allow-Origin', '*')
-        self.send_header('Access-Control-Allow-Methods', 'GET, POST, OPTIONS')
-        self.send_header('Access-Control-Allow-Headers', 'X-Requested-With, Content-Type')
-        SimpleHTTPRequestHandler.end_headers(self)
-
-class ThreadedHTTPServer(ThreadingMixIn, HTTPServer):
-    """Handle requests in a separate thread."""
-
-def run_server(port=8501):
-    server_address = ('', port)
-    httpd = ThreadedHTTPServer(server_address, CORSRequestHandler)
-    print(f'Serving on port {port}')
-    httpd.serve_forever()
-
-# Streamlit page config
+
 st.set_page_config(page_title="Stock News Sentiment Analyzer", layout="wide")
 
-# Function to verify a link
 def verify_link(url, timeout=10, retries=3):
     for _ in range(retries):
         try:
@@ -44,7 +22,6 @@ def verify_link(url, timeout=10, retries=3):
             continue
     return False
 
-# Get news for a stock ticker from FinViz
 def get_news(ticker):
     finviz_url = 'https://finviz.com/quote.ashx?t='
     url = finviz_url + ticker
@@ -54,7 +31,6 @@ def get_news(ticker):
     news_table = html.find(id='news-table')
     return news_table
 
-# Parse news headlines and links
 def parse_news(news_table):
     parsed_news = []
     
@@ -87,7 +63,6 @@ def parse_news(news_table):
     
     return parsed_news_df
 
-# Score news sentiment using Vader
 def score_news(parsed_news_df):
     vader = SentimentIntensityAnalyzer()
     
@@ -99,7 +74,6 @@ def score_news(parsed_news_df):
 
     return parsed_and_scored_news
 
-# Plot hourly sentiment scores
 def plot_hourly_sentiment(parsed_and_scored_news, ticker):
     numeric_cols = parsed_and_scored_news.select_dtypes(include=['float64', 'int64'])
     mean_scores = numeric_cols.resample('h').mean()
@@ -118,7 +92,6 @@ def plot_hourly_sentiment(parsed_and_scored_news, ticker):
     
     return fig
 
-# Plot daily sentiment scores
 def plot_daily_sentiment(parsed_and_scored_news, ticker):
     numeric_cols = parsed_and_scored_news.select_dtypes(include=['float64', 'int64'])
     mean_scores = numeric_cols.resample('D').mean()
@@ -137,7 +110,6 @@ def plot_daily_sentiment(parsed_and_scored_news, ticker):
     
     return fig
 
-# Generate a stock recommendation based on sentiment
 def get_recommendation(sentiment_scores):
     avg_sentiment = sentiment_scores['sentiment_score'].mean()
     
@@ -148,7 +120,6 @@ def get_recommendation(sentiment_scores):
     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."
 
-# Streamlit UI
 st.header("Stock News Sentiment Analyzer")
 
 ticker = st.text_input('Enter Stock Ticker', '').upper()
@@ -193,15 +164,10 @@ except Exception as e:
     print(str(e))
     st.write("Enter a correct stock ticker, e.g. 'AAPL' above and hit Enter.")    
 
-# Hide Streamlit menu and footer
 hide_streamlit_style = """
 <style>
 #MainMenu {visibility: hidden;}
 footer {visibility: hidden;}
 </style>
 """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-
-# Start CORS-enabled server
-if __name__ == "__main__":
-    run_server()
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)