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| import streamlit as st | |
| from func import fetch_news, analyze_sentiment, extract_org_entities | |
| import time | |
| # ---------------- Page Setup ---------------- | |
| st.set_page_config(page_title="Stock News Sentiment Analysis", layout="centered") | |
| st.markdown(""" | |
| <style> | |
| .main { background-color: #f9fbfc; } | |
| .stTextInput>div>div>input, .stTextArea textarea { | |
| 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) | |
| # ---------------- User Interface ---------------- | |
| st.title("\U0001F4CA Stock News Sentiment Analysis") | |
| st.markdown(""" | |
| This tool analyzes the sentiment of news articles related to companies you mention in text. | |
| \U0001F4A1 *Try input like:* `I want to check Apple, Tesla, and Microsoft.` | |
| **Note:** If news fetching fails, it may be due to changes in the Finviz website or access restrictions. | |
| """) | |
| free_text = st.text_area("Enter text mentioning companies:", height=100) | |
| tickers = extract_org_entities(free_text) | |
| if tickers: | |
| cleaned_input = ", ".join(tickers) | |
| st.markdown(f"\U0001F50E **Identified Tickers:** `{cleaned_input}`") | |
| else: | |
| tickers = [] | |
| # ---------------- Button Trigger ---------------- | |
| if st.button("Get News and Sentiment"): | |
| if not tickers: | |
| st.warning("Please mention at least one recognizable company.") | |
| 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: | |
| sentiments = [] | |
| for news in news_list: | |
| sentiment = analyze_sentiment(news['title']) | |
| sentiments.append(sentiment) | |
| positive_count = sentiments.count("Positive") | |
| negative_count = sentiments.count("Negative") | |
| total = len(sentiments) | |
| positive_ratio = positive_count / total if total else 0 | |
| negative_ratio = negative_count / total if total else 0 | |
| if positive_ratio >= 0.4: | |
| overall_sentiment = "Positive" | |
| elif negative_ratio >= 0.6: | |
| overall_sentiment = "Negative" | |
| else: | |
| overall_sentiment = "Neutral" | |
| 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}**") | |
| st.write(f"**Overall Sentiment for {ticker}: {overall_sentiment}**") | |
| else: | |
| st.write(f"No news available for {ticker}.") | |
| progress_bar.progress((idx + 1) / total_stocks) | |
| time.sleep(0.1) | |