"""UI component functions for the financial dashboard."""

import streamlit as st
import pandas as pd
from data import format_financial_value, get_profitability_metrics


def display_price_metrics(metrics: dict):
    """Display key price metrics in columns."""
    st.markdown('<div class="section-title">📊 Price Metrics</div>', unsafe_allow_html=True)

    col1, col2, col3, col4 = st.columns(4)

    with col1:
        st.metric("Current Price", f"${metrics['current_price']:.2f}",
                 f"{metrics['price_change']:+.2f}", delta_color="normal")

    with col2:
        st.metric("Day Change %", f"{metrics['price_change_pct']:+.2f}%",
                 None, delta_color="normal")

    with col3:
        st.metric("52W High", f"${metrics['high_52w']:.2f}")

    with col4:
        st.metric("52W Low", f"${metrics['low_52w']:.2f}")


def display_company_info(profile_info):
    """Display company information."""
    st.markdown('<div class="section-title">📋 Company Information</div>', unsafe_allow_html=True)

    if profile_info:
        info_col1, info_col2 = st.columns(2)
        with info_col1:
            st.write(f"**Company Name:** {getattr(profile_info, 'name', 'N/A')}")
            st.write(f"**Sector:** {getattr(profile_info, 'sector', 'N/A')}")
            st.write(f"**Industry:** {getattr(profile_info, 'industry', 'N/A')}")

        with info_col2:
            st.write(f"**Country:** {getattr(profile_info, 'country', 'N/A')}")
            st.write(f"**Exchange:** {getattr(profile_info, 'exchange', 'N/A')}")
            st.write(f"**Website:** {getattr(profile_info, 'website', 'N/A')}")


def display_financial_metrics(income_stmt: pd.DataFrame):
    """Display financial metrics from income statement."""
    st.markdown('<div class="section-title">💰 Financial Metrics</div>', unsafe_allow_html=True)

    latest_income = income_stmt.iloc[0] if len(income_stmt) > 0 else None

    if latest_income is not None:
        # First row of metrics
        fin_col1, fin_col2, fin_col3, fin_col4 = st.columns(4)

        with fin_col1:
            revenue = latest_income.get('total_revenue', 0)
            if pd.notna(revenue) and revenue > 0:
                st.metric("Total Revenue", format_financial_value(revenue))
            else:
                st.metric("Total Revenue", "N/A")

        with fin_col2:
            net_income = latest_income.get('net_income', 0)
            if pd.notna(net_income) and net_income > 0:
                st.metric("Net Income", format_financial_value(net_income))
            else:
                st.metric("Net Income", "N/A")

        with fin_col3:
            gross_profit = latest_income.get('gross_profit', 0)
            if pd.notna(gross_profit) and gross_profit > 0:
                st.metric("Gross Profit", format_financial_value(gross_profit))
            else:
                st.metric("Gross Profit", "N/A")

        with fin_col4:
            operating_income = latest_income.get('operating_income', 0)
            if pd.notna(operating_income) and operating_income > 0:
                st.metric("Operating Income", format_financial_value(operating_income))
            else:
                st.metric("Operating Income", "N/A")

        # Second row of metrics
        fin_col5, fin_col6, fin_col7, fin_col8 = st.columns(4)

        with fin_col5:
            eps = latest_income.get('diluted_earnings_per_share', 0)
            if pd.notna(eps):
                st.metric("EPS (Diluted)", f"${eps:.2f}")
            else:
                st.metric("EPS (Diluted)", "N/A")

        with fin_col6:
            ebitda = latest_income.get('ebitda', 0)
            if pd.notna(ebitda) and ebitda > 0:
                st.metric("EBITDA", format_financial_value(ebitda))
            else:
                st.metric("EBITDA", "N/A")

        with fin_col7:
            cogs = latest_income.get('cost_of_revenue', 0)
            if pd.notna(cogs) and cogs > 0:
                st.metric("Cost of Revenue", format_financial_value(cogs))
            else:
                st.metric("Cost of Revenue", "N/A")

        with fin_col8:
            rd_expense = latest_income.get('research_and_development_expense', 0)
            if pd.notna(rd_expense) and rd_expense > 0:
                st.metric("R&D Expense", format_financial_value(rd_expense))
            else:
                st.metric("R&D Expense", "N/A")


def display_income_statement(income_stmt: pd.DataFrame):
    """Display formatted income statement table."""
    st.markdown("### Income Statement")

    if not income_stmt.empty:
        display_columns = [
            'period_ending',
            'total_revenue',
            'cost_of_revenue',
            'gross_profit',
            'operating_income',
            'net_income',
            'diluted_earnings_per_share',
            'ebitda'
        ]

        available_cols = [col for col in display_columns if col in income_stmt.columns]
        financial_display = income_stmt[available_cols].copy()

        for col in financial_display.columns:
            if col != 'period_ending':
                financial_display[col] = financial_display[col].apply(
                    lambda x: format_financial_value(x)
                )

        st.dataframe(financial_display, use_container_width=True, hide_index=True)


def display_profitability_metrics(income_stmt: pd.DataFrame):
    """Display profitability metrics."""
    st.markdown("### Profitability Metrics")

    prof_col1, prof_col2 = st.columns(2)
    latest_data = income_stmt.iloc[0]
    metrics = get_profitability_metrics(latest_data)

    with prof_col1:
        if "gross_margin" in metrics:
            st.metric("Gross Margin", f"{metrics['gross_margin']:.2f}%")
        if "net_margin" in metrics:
            st.metric("Net Profit Margin", f"{metrics['net_margin']:.2f}%")

    with prof_col2:
        if "operating_margin" in metrics:
            st.metric("Operating Margin", f"{metrics['operating_margin']:.2f}%")

        if len(income_stmt) > 1:
            prev_revenue = income_stmt.iloc[1].get('total_revenue', 0)
            total_rev = latest_data.get('total_revenue', 0)
            if prev_revenue and prev_revenue > 0:
                revenue_growth = ((total_rev - prev_revenue) / prev_revenue) * 100
                st.metric("Revenue Growth (YoY)", f"{revenue_growth:+.2f}%")