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import streamlit as st |
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import requests |
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from bs4 import BeautifulSoup |
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from transformers import pipeline |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import time |
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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_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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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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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'} |
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response = requests.get(url, headers=headers) |
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soup = BeautifulSoup(response.text, 'html.parser') |
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news_table = soup.find(id='news-table') |
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news = [] |
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for row in news_table.findAll('tr')[:50]: |
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title = row.a.get_text() |
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link = row.a['href'] |
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news.append({'title': title, 'link': link}) |
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return news |
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except Exception as e: |
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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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return "Positive" if result['label'] == 'POSITIVE' else "Negative" |
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except Exception as e: |
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st.error(f"Sentiment analysis failed: {e}") |
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return "Unknown" |
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st.title("π 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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π‘ *Example input:* `META, NVDA, AAPL, NTES, NCTY` |
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""") |
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tickers_input = st.text_input("Enter stock tickers separated by commas:", "META, NVDA, AAPL, NTES, NCTY") |
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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"π **Parsed Tickers:** `{cleaned_input}`") |
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else: |
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tickers = [] |
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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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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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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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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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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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progress_bar.progress((idx + 1) / total_stocks) |
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time.sleep(0.1) |