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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 | |
| # ----------- Page Layout & Custom Styling ----------- | |
| st.set_page_config(page_title="Stock News Sentiment Analysis", layout="centered") | |
| st.markdown(""" | |
| <style> | |
| .main { background-color: #f9fbfc; } | |
| .stTextInput>div>div>input { | |
| font-size: 16px; | |
| padding: 0.5rem; | |
| } | |
| .stButton>button { | |
| background-color: #4CAF50; | |
| color: white; | |
| font-size: 16px; | |
| padding: 0.5rem 1rem; | |
| border-radius: 8px; | |
| } | |
| .stButton>button:hover { | |
| background-color: #45a049; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # ----------- Model Setup ----------- | |
| model_id = "LinkLinkWu/Boss_Stock_News_Analysis" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_id) | |
| sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer) | |
| # ----------- Function Definitions ----------- | |
| 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', | |
| 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8', | |
| 'Accept-Language': 'en-US,en;q=0.5', | |
| 'Referer': 'https://finviz.com/', | |
| 'Connection': 'keep-alive', | |
| } | |
| response = requests.get(url, headers=headers) | |
| if response.status_code != 200: | |
| st.error(f"Failed to fetch page for {ticker}: Status code {response.status_code}") | |
| return [] | |
| soup = BeautifulSoup(response.text, 'html.parser') | |
| title = soup.title.text if soup.title else "" | |
| if ticker not in title: | |
| st.error(f"Page for {ticker} not found or access denied.") | |
| return [] | |
| news_table = soup.find(id='news-table') | |
| if news_table is None: | |
| st.error(f"News table not found for {ticker}. The website structure might have changed.") | |
| return [] | |
| news = [] | |
| for row in news_table.findAll('tr')[:50]: # Fetch up to 50 articles | |
| a_tag = row.find('a') | |
| if a_tag: | |
| title = a_tag.get_text() | |
| link = a_tag['href'] | |
| news.append({'title': title, 'link': link}) | |
| return news | |
| except Exception as e: | |
| st.error(f"Failed to fetch news for {ticker}: {e}") | |
| return [] | |
| 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") | |
| st.markdown(""" | |
| This tool parses stock tickers and analyzes the sentiment of related news articles. | |
| 💡 *Example input:* `META, NVDA, AAPL, NTES, NCTY` | |
| **Note:** If news fetching fails, it might be due to changes in the Finviz website structure or access restrictions. Please verify the website manually or try again later. | |
| """) | |
| # Input field for stock tickers | |
| tickers_input = st.text_input("Enter stock tickers separated by commas:", "META, NVDA, AAPL, NTES, NCTY") | |
| # Parse and display cleaned tickers in real-time | |
| if tickers_input: | |
| tickers = [ticker.strip().upper() for ticker in tickers_input.split(",") if ticker.strip()] | |
| cleaned_input = ", ".join(tickers) | |
| st.markdown(f"🔎 **Parsed Tickers:** `{cleaned_input}`") | |
| else: | |
| tickers = [] | |
| # Button to trigger sentiment analysis | |
| if st.button("Get News and Sentiment"): | |
| if not tickers: | |
| st.warning("Please enter at least one stock ticker.") | |
| 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 |