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import streamlit as st |
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from func import ( |
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get_sentiment_pipeline, |
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get_ner_pipeline, |
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fetch_news, |
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analyze_sentiment, |
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extract_org_entities, |
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) |
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import time |
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st.set_page_config( |
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page_title="EquiPulse: Real-Time Stock News Sentiment", |
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layout="centered", |
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initial_sidebar_state="collapsed" |
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) |
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st.markdown(""" |
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<style> |
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body { |
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background-color: #ffffff; |
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} |
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h1 { |
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color: #002b45; |
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font-family: 'Segoe UI', sans-serif; |
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font-size: 32px; |
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} |
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.stTextInput > div > div > input, |
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.stTextArea textarea { |
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font-size: 16px; |
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} |
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.stButton>button { |
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background-color: #002b45; |
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color: white; |
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font-size: 16px; |
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padding: 0.4rem 1rem; |
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border-radius: 6px; |
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} |
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.stButton>button:hover { |
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background-color: #004b78; |
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} |
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.stMarkdown { |
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font-size: 16px; |
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} |
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</style> |
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""", unsafe_allow_html=True) |
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col1, col2 = st.columns([1, 9]) |
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with col1: |
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st.image("https://cdn-icons-png.flaticon.com/512/2721/2721203.png", width=48) |
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with col2: |
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st.markdown("<h1 style='margin-bottom: 0.2rem;'>EquiPulse: Real-Time Stock News Sentiment</h1>", unsafe_allow_html=True) |
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st.markdown(""" |
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<div style='font-size:16px; line-height:1.6;'> |
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Uncover real-time sentiment from financial headlines mentioning your target companies.<br> |
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<b>💬 Try:</b> <i>Apple, Tesla, and Microsoft</i> |
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</div> |
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""", unsafe_allow_html=True) |
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st.markdown("#### 🎯 Enter Your Target Company Tickers") |
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free_text = st.text_area("Example: Apple, Nvidia, Google", height=90) |
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ner_pipeline = get_ner_pipeline() |
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tickers = extract_org_entities(free_text, ner_pipeline) |
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if tickers: |
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st.markdown(f"✅ **Identified Tickers:** `{', '.join(tickers)}`") |
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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 recognizable company name.") |
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else: |
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sentiment_pipeline = get_sentiment_pipeline() |
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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.markdown(f"---\n#### 🔍 Analyzing: `{ticker}`") |
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news_list = fetch_news(ticker) |
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if news_list: |
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sentiments = [analyze_sentiment(news['title'], sentiment_pipeline) for news in news_list] |
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pos_count = sentiments.count("Positive") |
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neg_count = sentiments.count("Negative") |
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total = len(sentiments) |
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pos_ratio = pos_count / total if total else 0 |
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neg_ratio = neg_count / total if total else 0 |
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if pos_ratio >= 0.25: |
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overall = "Positive" |
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elif neg_ratio >= 0.75: |
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overall = "Negative" |
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else: |
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overall = "Neutral" |
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st.markdown(f"**📰 Top News for `{ticker}`:**") |
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for i, news in enumerate(news_list[:3]): |
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st.markdown(f"{i+1}. [{news['title']}]({news['link']}) — **{sentiments[i]}**") |
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st.success(f"📈 **Overall Sentiment for `{ticker}`: {overall}**") |
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else: |
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st.info(f"No recent news available for `{ticker}`.") |
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progress_bar.progress((idx + 1) / total_stocks) |
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time.sleep(0.1) |
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