import streamlit as st
import yfinance as yf
import pandas as pd
import time

# 🎯 Define Indian Indices
indian_indices = {
    "NIFTY 50": "^NSEI",
    "SENSEX": "^BSESN",
    "BANK NIFTY": "^NSEBANK",
    "NIFTY IT": "^CNXIT",
    "NIFTY PHARMA": "^CNXPHARMA",
    "NIFTY AUTO": "^CNXAUTO",
    "NIFTY FMCG": "^CNXFMCG",
    "NIFTY METAL": "^CNXMETAL",
    "NIFTY ENERGY": "^CNXENERGY",
    "NIFTY REALTY": "^CNXREALTY",
    "NIFTY INFRA": "^CNXINFRA",
    "NIFTY MEDIA": "^CNXMEDIA",
    "NIFTY PSU BANK": "^CNXPSUBANK",
}

# 🔹 More Extensive List of Sector Stocks
sector_stocks = {
    "IT": [
        "TCS.NS", "INFY.NS", "HCLTECH.NS", "TECHM.NS", "WIPRO.NS", "LTIM.NS", "COFORGE.NS", "PERSISTENT.NS",
        "MINDTREE.NS", "MPHASIS.NS", "OFSS.NS", "CYIENT.NS", "ZENSARTECH.NS"
    ],
    "Banking & Finance": [
        "HDFCBANK.NS", "ICICIBANK.NS", "SBIN.NS", "AXISBANK.NS", "KOTAKBANK.NS", "IDFCFIRSTB.NS", "PNB.NS",
        "BANDHANBNK.NS", "FEDERALBNK.NS", "RBLBANK.NS", "YESBANK.NS", "INDUSINDBK.NS", "CANBK.NS"
    ],
    "Energy & Oil": [
        "RELIANCE.NS", "ONGC.NS", "POWERGRID.NS", "NTPC.NS", "BPCL.NS", "IOC.NS", "GAIL.NS", "TATAPOWER.NS",
        "JSWENERGY.NS", "NHPC.NS", "ADANIENERGY.NS", "ADANIGREEN.NS", "ADANITRANS.NS"
    ],
    "Automobile": [
        "TATAMOTORS.NS", "M&M.NS", "MARUTI.NS", "HEROMOTOCO.NS", "EICHERMOT.NS", "BAJAJ-AUTO.NS", "TVSMOTOR.NS",
        "ASHOKLEY.NS", "ESCORTS.NS", "SMLISUZU.NS", "FORCEMOT.NS", "BOSCHLTD.NS", "AMARAJABAT.NS"
    ],
    "Consumer Goods": [
        "HINDUNILVR.NS", "ITC.NS", "NESTLEIND.NS", "BRITANNIA.NS", "TITAN.NS", "DABUR.NS", "MARICO.NS",
        "COLPAL.NS", "GODREJCP.NS", "EMAMILTD.NS", "BAJAJCON.NS", "RADICO.NS", "HATSUN.NS"
    ],
    "Metals & Mining": [
        "TATASTEEL.NS", "HINDALCO.NS", "JSWSTEEL.NS", "COALINDIA.NS", "VEDL.NS", "SAIL.NS", "NALCO.NS",
        "MOIL.NS", "HINDZINC.NS", "APLAPOLLO.NS", "JINDALSTEL.NS", "GODAWARI.NS", "SHYAMMETL.NS"
    ],
    "Pharmaceuticals": [
        "SUNPHARMA.NS", "DRREDDY.NS", "CIPLA.NS", "DIVISLAB.NS", "LUPIN.NS", "AUROPHARMA.NS", "BIOCON.NS",
        "TORNTPHARM.NS", "ABBOTINDIA.NS", "GLENMARK.NS", "ZYDUSLIFE.NS", "ALKEM.NS", "IPCALAB.NS"
    ],
    "Infrastructure & Realty": [
        "LT.NS", "ULTRACEMCO.NS", "SHREECEM.NS", "GRASIM.NS", "DLF.NS", "GODREJPROP.NS", "PHOENIXLTD.NS",
        "OBEROIRLTY.NS", "IBREALEST.NS", "NCC.NS", "SOBHA.NS", "BRIGADE.NS", "PRESTIGE.NS"
    ]
}

# 📌 Streamlit Page Configuration
st.set_page_config(page_title="📈 Live Indian Stock Market", layout="wide")

st.title("📈 Live Indian Stock Market Dashboard")

# 🏦 Index Selection
selected_index = st.selectbox("Select an Index", list(indian_indices.keys()))
index_ticker = indian_indices[selected_index]

# 🔹 Sector Selection
selected_sector = st.selectbox("Select a Sector", list(sector_stocks.keys()))
sector_tickers = sector_stocks[selected_sector]

# 📊 Function to Fetch Live Data with Error Handling
def fetch_live_data(ticker):
    try:
        stock = yf.Ticker(ticker)
        live_data = stock.history(period="1d", interval="1m")
        return live_data
    except yf.YFRateLimitError:
        st.warning("⚠️ Yahoo Finance rate limit reached. Retrying after 30 seconds...")
        time.sleep(30)  # Wait before retrying
        return None
    except Exception as e:
        st.error(f"⚠️ Error fetching data for {ticker}: {e}")
        return None

# 📊 Fetch Live Data for Index
st.subheader(f"📊 Live Price for {selected_index}")
live_price_placeholder = st.empty()

# 📈 Fetch Live Data for Sector Stocks
st.subheader(f"📈 Live Sector-Wise Stock Data ({selected_sector})")
sector_table_placeholder = st.empty()

# 🚀 Continuous Live Streaming Using Loop
while True:
    try:
        # Fetch Live Index Data
        live_data = fetch_live_data(index_ticker)
        if live_data is not None and not live_data.empty:
            latest_price = live_data["Close"].iloc[-1]

            # ✅ FIX: Get previous close safely
            historical_data = yf.Ticker(index_ticker).history(period="2d")
            previous_close = historical_data["Close"].iloc[-2] if len(historical_data) > 1 else latest_price

            price_change = latest_price - previous_close
            percentage_change = (price_change / previous_close) * 100

            live_price_placeholder.metric(
                f"{selected_index} Live Price",
                f"₹{latest_price:.2f}",
                f"{price_change:.2f} ({percentage_change:.2f}%)",
                delta_color="inverse" if price_change < 0 else "normal"
            )

        # Fetch Live Data for Sector Stocks
        sector_data = []
        for stock in sector_tickers:
            stock_info = fetch_live_data(stock)
            if stock_info is not None and not stock_info.empty:
                latest_price = stock_info["Close"].iloc[-1]

                # ✅ FIX: Get previous close safely
                historical_data = yf.Ticker(stock).history(period="2d")
                prev_close = historical_data["Close"].iloc[-2] if len(historical_data) > 1 else latest_price

                price_change = latest_price - prev_close
                percentage_change = (price_change / prev_close) * 100

                sector_data.append({
                    "Stock": stock,
                    "Price (₹)": latest_price,
                    "Change (₹)": price_change,
                    "Change (%)": f"{percentage_change:.2f}%",
                })

        # Convert to DataFrame & Display
        if sector_data:
            df = pd.DataFrame(sector_data)
            sector_table_placeholder.dataframe(df, height=400)

        # ✅ Refresh Every **10 Seconds**
        time.sleep(10)

    except Exception as e:
        st.error(f"⚠️ Unexpected error: {e}")
        time.sleep(10)  # Prevent app from breaking and wait before retrying