Update app.py
Browse files
app.py
CHANGED
@@ -5,16 +5,36 @@ from transformers import pipeline
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import time
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#
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForSequenceClassification.from_pretrained(model_id)
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# Initialize sentiment analysis pipeline
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sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
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# Function
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def fetch_news(ticker):
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try:
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url = f"https://finviz.com/quote.ashx?t={ticker}"
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@@ -32,7 +52,6 @@ def fetch_news(ticker):
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st.error(f"Failed to fetch news for {ticker}: {e}")
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return []
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# Function to analyze sentiment of news title
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def analyze_sentiment(text):
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try:
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result = sentiment_pipeline(text)[0]
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@@ -41,51 +60,59 @@ def analyze_sentiment(text):
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st.error(f"Sentiment analysis failed: {e}")
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return "Unknown"
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# Streamlit UI
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st.title("Stock News Sentiment Analysis")
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# Input field for stock tickers
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tickers_input = st.text_input("Enter
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if st.button("Get News and Sentiment"):
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sentiment = analyze_sentiment(news['title'])
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sentiments.append(sentiment)
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# Determine overall sentiment based on majority
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positive_count = sentiments.count("Positive")
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negative_count = sentiments.count("Negative")
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overall_sentiment = "Positive" if positive_count > negative_count else "Negative"
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# Display top 3 news articles with sentiment
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st.write(f"**Top 3 News Articles for {ticker}**")
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for i, news in enumerate(news_list[:3], 1):
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sentiment = sentiments[i-1]
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st.markdown(f"{i}. [{news['title']}]({news['link']}) - **{sentiment}**")
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# Display overall sentiment
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st.write(f"**Overall Sentiment for {ticker}: {overall_sentiment}**")
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else:
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st.write(f"No news available for {ticker}.")
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#
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import time
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# ----------- Page Layout & Custom Styling -----------
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st.set_page_config(page_title="Stock News Sentiment Analysis", layout="centered")
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st.markdown("""
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<style>
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.main { background-color: #f9fbfc; }
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.stTextInput>div>div>input {
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font-size: 16px;
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padding: 0.5rem;
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}
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.stButton>button {
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background-color: #4CAF50;
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color: white;
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font-size: 16px;
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padding: 0.5rem 1rem;
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border-radius: 8px;
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}
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.stButton>button:hover {
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background-color: #45a049;
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}
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</style>
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""", unsafe_allow_html=True)
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# ----------- Model Setup -----------
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model_id = "LinkLinkWu/Boss_Stock_News_Analysis"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForSequenceClassification.from_pretrained(model_id)
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sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
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# ----------- Function Definitions -----------
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def fetch_news(ticker):
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try:
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url = f"https://finviz.com/quote.ashx?t={ticker}"
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st.error(f"Failed to fetch news for {ticker}: {e}")
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return []
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def analyze_sentiment(text):
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try:
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result = sentiment_pipeline(text)[0]
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st.error(f"Sentiment analysis failed: {e}")
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return "Unknown"
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# ----------- Streamlit UI -----------
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st.title("📊 Stock News Sentiment Analysis")
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st.markdown("""
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This tool parses stock tickers and analyzes the sentiment of related news articles.
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💡 *Example input:* `META, NVDA, AAPL, NTES, NCTY`
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""")
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# Input field for stock tickers
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tickers_input = st.text_input("Enter stock tickers separated by commas:", "META, NVDA, AAPL, NTES, NCTY")
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# Parse and display cleaned tickers in real-time
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if tickers_input:
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tickers = [ticker.strip().upper() for ticker in tickers_input.split(",") if ticker.strip()]
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cleaned_input = ", ".join(tickers)
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st.markdown(f"🔎 **Parsed Tickers:** `{cleaned_input}`")
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else:
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tickers = []
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# Button to trigger sentiment analysis
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if st.button("Get News and Sentiment"):
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if not tickers:
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st.warning("Please enter at least one stock ticker.")
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else:
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progress_bar = st.progress(0)
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total_stocks = len(tickers)
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for idx, ticker in enumerate(tickers):
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st.subheader(f"Analyzing {ticker}...")
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news_list = fetch_news(ticker)
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if news_list:
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# Analyze sentiment for all news articles (up to 50)
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sentiments = []
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for news in news_list:
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sentiment = analyze_sentiment(news['title'])
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sentiments.append(sentiment)
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# Determine overall sentiment based on majority
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positive_count = sentiments.count("Positive")
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negative_count = sentiments.count("Negative")
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overall_sentiment = "Positive" if positive_count > negative_count else "Negative"
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# Display top 3 news articles with sentiment
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st.write(f"**Top 3 News Articles for {ticker}**")
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for i, news in enumerate(news_list[:3], 1):
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sentiment = sentiments[i-1]
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st.markdown(f"{i}. [{news['title']}]({news['link']}) - **{sentiment}**")
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# Display overall sentiment
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st.write(f"**Overall Sentiment for {ticker}: {overall_sentiment}**")
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else:
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st.write(f"No news available for {ticker}.")
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# Update progress bar
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progress_bar.progress((idx + 1) / total_stocks)
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time.sleep(0.1) # Simulate processing time
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