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import streamlit as st
from gfnews import GoogleBussinessNews
from datetime import datetime
from transformers import pipeline
financial_business_news_domains = [
"economictimes.indiatimes.com", "business-standard.com", "financialexpress.com",
"livemint.com", "thehindubusinessline.com", "moneycontrol.com", "bloombergquint.com",
"cnbctv18.com", "businesstoday.in", "forbesindia.com", "reuters.com", "bloomberg.com",
"ft.com", "wsj.com", "cnbc.com", "marketwatch.com", "investing.com", "finance.yahoo.com",
"seekingalpha.com", "businessinsider.com"
]
st.markdown(
"""
<style>
.stMainBlockContainer{
max-width: 70vw;
padding-top: 4rem;
}
</style>
""",
unsafe_allow_html=True
)
sentiment_analyzer = pipeline("sentiment-analysis")
if 'search_history' not in st.session_state:
st.session_state.search_history = []
common_topics = [
"HCLTech", "Stock Market", "Interest Rates", "RBI Policies",
"Nifty 50", "Banking Sector", "Mutual Funds", "Corporate Earnings", "Budget 2025"
]
nifty_50_companies = [
"Reliance", "TCS", "HDFC", "Infosys", "HUL", "ICICI", "SBI",
"Bajaj Finance", "Airtel", "Wipro", "Kotak Mahindra", "Adani Enterprises",
"Larsen & Toubro", "Tata Motors", "Maruti Suzuki", "Asian Paints",
"NTPC", "Nestle India", "Sun Pharma", "Power Grid", "ITC", "HDFC", "Tata Steel"
]
st.title("📰 Finance News App")
st.markdown(
"""
**Welcome to the Finance News App!**
Stay informed with the latest financial news from trusted sources. Use the search bar or select from common topics.
""",
unsafe_allow_html=True
)
col1, col2 = st.columns([2, 1])
scraper = None
with col2:
selected_sources = st.multiselect("Select News Sources", default=financial_business_news_domains[:2], options=financial_business_news_domains)
scraper = GoogleBussinessNews(selected_sources,max_articles=25)
with col1:
st.markdown("## 🔍 Search for News")
st.markdown("")
topic_disabled = False
query = st.text_input("Enter search query:")
if query:
topic_disabled = True
st.markdown("")
topic = st.selectbox("OR Search by topic:", common_topics + nifty_50_companies, disabled=topic_disabled)
st.markdown("")
query = query if query else topic
first_day_of_current_month = datetime(datetime.today().year, datetime.today().month, 1)
start_date = st.date_input("Start Date:", value=first_day_of_current_month)
end_date = st.date_input("End Date:", value=datetime.today())
if st.button("Search", type="primary"):
if start_date > end_date:
st.error("Start date cannot be after the end date.")
elif not query:
st.warning("Please enter or select a query.")
else:
st.session_state.search_history.insert(0, query)
if len(st.session_state.search_history) > 5:
st.session_state.search_history.pop()
with st.spinner("Fetching news..."):
results = scraper.scrape(query=query, start_date=start_date, end_date=end_date)
if results:
for result in results:
sentiment = sentiment_analyzer(result["description"])[0]
sentiment_label = sentiment["label"]
sentiment_score = sentiment["score"]
if sentiment_label == "POSITIVE":
progress_color = "green"
elif sentiment_label == "NEGATIVE":
progress_color = "red"
else:
progress_color = "gray"
st.markdown("---")
with st.container():
st.subheader(result["title"])
st.write(f"**Source:** {result['source']}")
st.write(f"**Date:** {result['date']}")
st.write(f"**Description:** {result['description']}")
st.markdown(f"**Sentiment:** <span style='color:{progress_color}; font-weight:bold;'>{sentiment_label}</span> (Score: {sentiment_score:.2f})", unsafe_allow_html=True)
st.markdown(
f"""
<div style="height: 10px; background-color: {progress_color}; width: {sentiment_score * 100}%;">
</div>
""",
unsafe_allow_html=True
)
st.markdown(f"[Go to Page]({result['url']})", unsafe_allow_html=True)
else:
st.info("Please try again, might be a network delay.")
with col2:
st.markdown("")
st.markdown("---")
st.markdown("")
st.markdown("## 🔄 Recent Searches")
if st.session_state.search_history:
for idx, past_query in enumerate(st.session_state.search_history):
st.button(past_query, key=f"search_{idx}_{past_query}")
else:
st.text('No previous searches.')
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