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| import streamlit as st | |
| import requests | |
| from bs4 import BeautifulSoup | |
| from transformers import pipeline | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import time | |
| 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", model=model, tokenizer=tokenizer) | |
| # Function to fetch top 50 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')[:50]: # Fetch up to 50 articles | |
| 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: | |
| progress_bar = st.progress(0) | |
| total_stocks = len(tickers) | |
| for idx, ticker in enumerate(tickers): | |
| st.subheader(f"Analyzing {ticker}...") | |
| news_list = fetch_news(ticker) | |
| if news_list: | |
| # Analyze sentiment for all news articles (up to 50) | |
| sentiments = [] | |
| for news in news_list: | |
| sentiment = analyze_sentiment(news['title']) | |
| sentiments.append(sentiment) | |
| # Determine overall sentiment based on majority | |
| positive_count = sentiments.count("Positive") | |
| negative_count = sentiments.count("Negative") | |
| overall_sentiment = "Positive" if positive_count > negative_count else "Negative" | |
| # Display top 3 news articles with sentiment | |
| st.write(f"**Top 3 News Articles for {ticker}**") | |
| for i, news in enumerate(news_list[:3], 1): | |
| sentiment = sentiments[i-1] | |
| st.markdown(f"{i}. [{news['title']}]({news['link']}) - **{sentiment}**") | |
| # Display overall sentiment | |
| st.write(f"**Overall Sentiment for {ticker}: {overall_sentiment}**") | |
| else: | |
| st.write(f"No news available for {ticker}.") | |
| # Update progress bar | |
| progress_bar.progress((idx + 1) / total_stocks) | |
| time.sleep(0.1) # Simulate processing time | |
| else: | |
| st.warning("Please enter stock tickers.") |