import streamlit as st
from func import (
    get_sentiment_pipeline,
    get_ner_pipeline,
    fetch_news,
    analyze_sentiment,
    extract_org_entities,
)
import time

# ----------- Page Config -----------
st.set_page_config(
    page_title="EquiPulse: Real-Time Stock News Sentiment",
    layout="centered",
    initial_sidebar_state="collapsed"
)

# ----------- Custom Styling -----------
st.markdown("""
    <style>
        body {
            background-color: #ffffff;
        }
        h1 {
            color: #002b45;
            font-family: 'Segoe UI', sans-serif;
            font-size: 32px;
        }
        .stTextInput > div > div > input,
        .stTextArea textarea {
            font-size: 16px;
        }
        .stButton>button {
            background-color: #002b45;
            color: white;
            font-size: 16px;
            padding: 0.4rem 1rem;
            border-radius: 6px;
        }
        .stButton>button:hover {
            background-color: #004b78;
        }
        .stMarkdown {
            font-size: 16px;
        }
    </style>
""", unsafe_allow_html=True)

# ----------- Header Section -----------
col1, col2 = st.columns([1, 9])
with col1:
    st.image("https://cdn-icons-png.flaticon.com/512/2721/2721203.png", width=48)
with col2:
    st.markdown("<h1 style='margin-bottom: 0.2rem;'>EquiPulse: Real-Time Stock News Sentiment</h1>", unsafe_allow_html=True)

# ----------- Description -----------
st.markdown("""
<div style='font-size:16px; line-height:1.6;'>
    Uncover real-time sentiment from financial headlines mentioning your target companies.<br>
    <b>💬 Try:</b> <i>Apple, Tesla, and Microsoft</i>
</div>
""", unsafe_allow_html=True)

# ----------- Input Area -----------
st.markdown("#### 🎯 Enter Your Target Company Tickers")
free_text = st.text_area("Example: Apple, Nvidia, Google", height=90)

ner_pipeline = get_ner_pipeline()
tickers = extract_org_entities(free_text, ner_pipeline)

if tickers:
    st.markdown(f"✅ **Identified Tickers:** `{', '.join(tickers)}`")
else:
    tickers = []

# ----------- Action Button -----------
if st.button("Get News and Sentiment"):
    if not tickers:
        st.warning("⚠️ Please enter at least one recognizable company name.")
    else:
        sentiment_pipeline = get_sentiment_pipeline()
        progress_bar = st.progress(0)
        total_stocks = len(tickers)

        for idx, ticker in enumerate(tickers):
            st.markdown(f"---\n#### 🔍 Analyzing: `{ticker}`")

            news_list = fetch_news(ticker)

            if news_list:
                sentiments = [analyze_sentiment(news['title'], sentiment_pipeline) for news in news_list]

                pos_count = sentiments.count("Positive")
                neg_count = sentiments.count("Negative")
                total = len(sentiments)
                pos_ratio = pos_count / total if total else 0
                neg_ratio = neg_count / total if total else 0

                if pos_ratio >= 0.25:
                    overall = "Positive"
                elif neg_ratio >= 0.75:
                    overall = "Negative"
                else:
                    overall = "Neutral"

                st.markdown(f"**📰 Top News for `{ticker}`:**")
                for i, news in enumerate(news_list[:3]):
                    st.markdown(f"{i+1}. [{news['title']}]({news['link']}) — **{sentiments[i]}**")

                st.success(f"📈 **Overall Sentiment for `{ticker}`: {overall}**")
            else:
                st.info(f"No recent news available for `{ticker}`.")

            progress_bar.progress((idx + 1) / total_stocks)
            time.sleep(0.1)