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import streamlit as st
import requests
from bs4 import BeautifulSoup
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = "LinkLinkWu/Boss_Stock_News_Analysis"
# Load tokenizer & Model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
# Initialize sentiment analysis pipeline
sentiment_pipeline = pipeline("sentiment-analysis")
# Function to fetch top 3 news articles from FinViz
def fetch_news(ticker):
try:
url = f"https://finviz.com/quote.ashx?t={ticker}"
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, 'html.parser')
news_table = soup.find(id='news-table')
news = []
for row in news_table.findAll('tr')[:3]: # Limit to top 3
title = row.a.get_text()
link = row.a['href']
news.append({'title': title, 'link': link})
return news
except Exception as e:
st.error(f"Failed to fetch news for {ticker}: {e}")
return []
# Function to analyze sentiment of news title
def analyze_sentiment(text):
try:
result = sentiment_pipeline(text)[0]
return "Positive" if result['label'] == 'POSITIVE' else "Negative"
except Exception as e:
st.error(f"Sentiment analysis failed: {e}")
return "Unknown"
# Streamlit UI
st.title("Stock News Sentiment Analysis")
# Input field for stock tickers
tickers_input = st.text_input("Enter five stock tickers separated by commas (e.g., AAPL, MSFT, GOOGL, AMZN, TSLA):")
if st.button("Get News and Sentiment"):
if tickers_input:
tickers = [ticker.strip().upper() for ticker in tickers_input.split(',')]
# Validate input
if len(tickers) != 5:
st.error("Please enter exactly five stock tickers.")
else:
# Process each ticker
for ticker in tickers:
st.subheader(f"Top 3 News Articles for {ticker}")
news_list = fetch_news(ticker)
if news_list:
for i, news in enumerate(news_list, 1):
sentiment = analyze_sentiment(news['title'])
st.markdown(f"{i}. [{news['title']}]({news['link']}) - **{sentiment}**")
else:
st.write("No news available for this ticker.")
else:
st.warning("Please enter stock tickers.") |