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.')