import streamlit as st
import plotly.graph_objects as go
import pandas as pd # Added import statement for pandas
from utils.data_loader import load_nifty50_symbols, fetch_stock_data, get_stock_info

st.title("Market Overview")

# Create sector categories
sectors = {
    "IT": ["TCS.NS", "INFY.NS", "HCLTECH.NS", "TECHM.NS", "WIPRO.NS"],
    "Banking & Finance": ["HDFCBANK.NS", "ICICIBANK.NS", "SBIN.NS", "AXISBANK.NS", "KOTAKBANK.NS",
                         "BAJFINANCE.NS", "BAJAJFINSV.NS", "HDFC.NS", "INDUSINDBK.NS"],
    "Energy & Oil": ["RELIANCE.NS", "ONGC.NS", "POWERGRID.NS", "NTPC.NS", "BPCL.NS"],
    "Automobile": ["TATAMOTORS.NS", "M&M.NS", "MARUTI.NS", "HEROMOTOCO.NS", "EICHERMOT.NS"],
    "Consumer Goods": ["HINDUNILVR.NS", "ITC.NS", "NESTLEIND.NS", "BRITANNIA.NS", "TITAN.NS"],
    "Metals & Mining": ["TATASTEEL.NS", "HINDALCO.NS", "JSWSTEEL.NS", "COALINDIA.NS"],
    "Pharmaceuticals": ["SUNPHARMA.NS", "DRREDDY.NS", "CIPLA.NS", "DIVISLAB.NS"],
    "Infrastructure": ["LT.NS", "ADANIPORTS.NS", "ULTRACEMCO.NS", "SHREECEM.NS", "GRASIM.NS"],
    "Others": ["BHARTIARTL.NS", "ASIANPAINT.NS", "HDFCLIFE.NS", "SBILIFE.NS", "UPL.NS", 
              "ADANIENT.NS", "BAJAJ-AUTO.NS", "APOLLOHOSP.NS", "DMART.NS", "PIDILITIND.NS"]
}

# Sector selection
selected_sector = st.selectbox("Select Sector", list(sectors.keys()))

# Load data for selected sector
cols = st.columns(3)

# Market Movers for selected sector
with cols[0]:
    st.subheader("Top Gainers")
    gainers_data = []
    for symbol in sectors[selected_sector]:
        data = fetch_stock_data(symbol, period='1d')
        if data is not None:
            change = ((data['Close'][-1] - data['Open'][0]) / data['Open'][0]) * 100
            info = get_stock_info(symbol)
            company_name = info['name'] if info else symbol
            gainers_data.append({
                'Company': company_name,
                'Symbol': symbol,
                'Change%': change
            })

    # Sort gainers by change percentage
    gainers_data = sorted(gainers_data, key=lambda x: x['Change%'], reverse=True)[:3]
    st.dataframe(pd.DataFrame(gainers_data))

with cols[1]:
    st.subheader("Top Losers")
    losers_data = []
    for symbol in sectors[selected_sector]:
        data = fetch_stock_data(symbol, period='1d')
        if data is not None:
            change = ((data['Close'][-1] - data['Open'][0]) / data['Open'][0]) * 100
            info = get_stock_info(symbol)
            company_name = info['name'] if info else symbol
            losers_data.append({
                'Company': company_name,
                'Symbol': symbol,
                'Change%': change
            })

    # Sort losers by change percentage
    losers_data = sorted(losers_data, key=lambda x: x['Change%'])[:3]
    st.dataframe(pd.DataFrame(losers_data))

with cols[2]:
    st.subheader("Most Active")
    active_data = []
    for symbol in sectors[selected_sector]:
        info = get_stock_info(symbol)
        if info:
            active_data.append({
                'Company': info['name'],
                'Symbol': symbol,
                'Volume': info['volume']
            })

    # Sort by volume
    active_data = sorted(active_data, key=lambda x: x['Volume'], reverse=True)[:3]
    st.dataframe(pd.DataFrame(active_data))

# Sector Performance Heat Map
st.subheader(f"{selected_sector} Performance Heat Map")
heat_map_data = []
for symbol in sectors[selected_sector]:
    data = fetch_stock_data(symbol, period='1d')
    info = get_stock_info(symbol)
    if data is not None and info:
        change = ((data['Close'][-1] - data['Open'][0]) / data['Open'][0]) * 100
        heat_map_data.append({
            'Company': info['name'],
            'Symbol': symbol,
            'Change%': change,
            'Market Cap': info['market_cap'],
            'Volume': info['volume']
        })

if heat_map_data:
    fig = go.Figure(data=[go.Treemap(
        labels=[f"{d['Company']}<br>{d['Symbol']}" for d in heat_map_data],
        parents=[''] * len(heat_map_data),
        values=[d['Market Cap'] for d in heat_map_data],
        textinfo="label+value+percent",
        marker=dict(
            colors=[d['Change%'] for d in heat_map_data],
            colorscale='RdYlGn',
            showscale=True
        ),
        hovertemplate="""
        Company: %{label}<br>
        Change: %{color:.2f}%<br>
        Market Cap: ₹%{value:,.0f}<br>
        <extra></extra>
        """
    )])

    fig.update_layout(title=f"{selected_sector} Companies Heat Map")
    st.plotly_chart(fig, use_container_width=True)

# Detailed Sector Analysis
st.subheader(f"{selected_sector} Companies")
detailed_data = []
for symbol in sectors[selected_sector]:
    info = get_stock_info(symbol)
    if info:
        detailed_data.append({
            'Company': info['name'],
            'Symbol': symbol,
            'Market Cap (Cr)': info['market_cap'] / 10000000,  # Convert to Crores
            'P/E Ratio': info['pe_ratio'],
            'Volume': info['volume'],
            'Recommendation': info['recommendation'],
            'Target Price': info['target_price']
        })

if detailed_data:
    st.dataframe(pd.DataFrame(detailed_data))