const { outputProgress, formatElapsedTime, estimateRemaining, calculateRate, logError } = require('./utils/progress');
const { getConnection } = require('./utils/db');

async function calculateCategoryMetrics(startTime, totalProducts, processedCount, isCancelled = false) {
    const connection = await getConnection();
    try {
        if (isCancelled) {
            outputProgress({
                status: 'cancelled',
                operation: 'Category metrics calculation cancelled',
                current: processedCount,
                total: totalProducts,
                elapsed: formatElapsedTime(startTime),
                remaining: null,
                rate: calculateRate(startTime, processedCount),
                percentage: ((processedCount / totalProducts) * 100).toFixed(1),
                timing: {
                    start_time: new Date(startTime).toISOString(),
                    end_time: new Date().toISOString(),
                    elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
                }
            });
            return processedCount;
        }

        outputProgress({
            status: 'running',
            operation: 'Starting category metrics calculation',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });
        
        // First, calculate base category metrics
        await connection.query(`
            INSERT INTO category_metrics (
                category_id,
                product_count,
                active_products,
                total_value,
                status,
                last_calculated_at
            )
            SELECT 
                c.cat_id,
                COUNT(DISTINCT p.pid) as product_count,
                COUNT(DISTINCT CASE WHEN p.visible = true THEN p.pid END) as active_products,
                COALESCE(SUM(p.stock_quantity * p.cost_price), 0) as total_value,
                c.status,
                NOW() as last_calculated_at
            FROM categories c
            LEFT JOIN product_categories pc ON c.cat_id = pc.cat_id
            LEFT JOIN products p ON pc.pid = p.pid
            GROUP BY c.cat_id, c.status
            ON DUPLICATE KEY UPDATE
                product_count = VALUES(product_count),
                active_products = VALUES(active_products),
                total_value = VALUES(total_value),
                status = VALUES(status),
                last_calculated_at = VALUES(last_calculated_at)
        `);

        processedCount = Math.floor(totalProducts * 0.90);
        outputProgress({
            status: 'running',
            operation: 'Base category metrics calculated, updating with margin data',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        if (isCancelled) return processedCount;

        // Then update with margin and turnover data
        await connection.query(`
            WITH category_sales AS (
                SELECT 
                    pc.cat_id,
                    SUM(o.quantity * o.price) as total_sales,
                    SUM(o.quantity * (o.price - p.cost_price)) as total_margin,
                    SUM(o.quantity) as units_sold,
                    AVG(GREATEST(p.stock_quantity, 0)) as avg_stock,
                    COUNT(DISTINCT DATE(o.date)) as active_days
                FROM product_categories pc
                JOIN products p ON pc.pid = p.pid
                JOIN orders o ON p.pid = o.pid
                LEFT JOIN turnover_config tc ON 
                    (tc.category_id = pc.cat_id AND tc.vendor = p.vendor) OR
                    (tc.category_id = pc.cat_id AND tc.vendor IS NULL) OR
                    (tc.category_id IS NULL AND tc.vendor = p.vendor) OR
                    (tc.category_id IS NULL AND tc.vendor IS NULL)
                WHERE o.canceled = false 
                    AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL COALESCE(tc.calculation_period_days, 30) DAY)
                GROUP BY pc.cat_id
            )
            UPDATE category_metrics cm
            JOIN category_sales cs ON cm.category_id = cs.cat_id
            LEFT JOIN turnover_config tc ON 
                (tc.category_id = cm.category_id AND tc.vendor IS NULL) OR
                (tc.category_id IS NULL AND tc.vendor IS NULL)
            SET 
                cm.avg_margin = COALESCE(cs.total_margin * 100.0 / NULLIF(cs.total_sales, 0), 0),
                cm.turnover_rate = CASE 
                    WHEN cs.avg_stock > 0 AND cs.active_days > 0 
                    THEN LEAST(
                        (cs.units_sold / cs.avg_stock) * (365.0 / cs.active_days),
                        999.99
                    )
                    ELSE 0 
                END,
                cm.last_calculated_at = NOW()
        `);

        processedCount = Math.floor(totalProducts * 0.95);
        outputProgress({
            status: 'running',
            operation: 'Margin data updated, calculating growth rates',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        if (isCancelled) return processedCount;

        // Finally update growth rates
        await connection.query(`
            WITH current_period AS (
                SELECT 
                    pc.cat_id,
                    SUM(o.quantity * (o.price - COALESCE(o.discount, 0)) / 
                        (1 + COALESCE(ss.seasonality_factor, 0))) as revenue,
                    COUNT(DISTINCT DATE(o.date)) as days
                FROM product_categories pc
                JOIN products p ON pc.pid = p.pid
                JOIN orders o ON p.pid = o.pid
                LEFT JOIN sales_seasonality ss ON MONTH(o.date) = ss.month
                WHERE o.canceled = false 
                    AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 3 MONTH)
                GROUP BY pc.cat_id
            ),
            previous_period AS (
                SELECT 
                    pc.cat_id,
                    SUM(o.quantity * (o.price - COALESCE(o.discount, 0)) / 
                        (1 + COALESCE(ss.seasonality_factor, 0))) as revenue,
                    COUNT(DISTINCT DATE(o.date)) as days
                FROM product_categories pc
                JOIN products p ON pc.pid = p.pid
                JOIN orders o ON p.pid = o.pid
                LEFT JOIN sales_seasonality ss ON MONTH(o.date) = ss.month
                WHERE o.canceled = false 
                    AND o.date BETWEEN DATE_SUB(CURRENT_DATE, INTERVAL 15 MONTH) 
                    AND DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
                GROUP BY pc.cat_id
            ),
            trend_data AS (
                SELECT 
                    pc.cat_id,
                    MONTH(o.date) as month,
                    SUM(o.quantity * (o.price - COALESCE(o.discount, 0)) / 
                        (1 + COALESCE(ss.seasonality_factor, 0))) as revenue,
                    COUNT(DISTINCT DATE(o.date)) as days_in_month
                FROM product_categories pc
                JOIN products p ON pc.pid = p.pid
                JOIN orders o ON p.pid = o.pid
                LEFT JOIN sales_seasonality ss ON MONTH(o.date) = ss.month
                WHERE o.canceled = false 
                    AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 15 MONTH)
                GROUP BY pc.cat_id, MONTH(o.date)
            ),
            trend_stats AS (
                SELECT 
                    cat_id,
                    COUNT(*) as n,
                    AVG(month) as avg_x,
                    AVG(revenue / NULLIF(days_in_month, 0)) as avg_y,
                    SUM(month * (revenue / NULLIF(days_in_month, 0))) as sum_xy,
                    SUM(month * month) as sum_xx
                FROM trend_data
                GROUP BY cat_id
                HAVING COUNT(*) >= 6
            ),
            trend_analysis AS (
                SELECT 
                    cat_id,
                    ((n * sum_xy) - (avg_x * n * avg_y)) / 
                        NULLIF((n * sum_xx) - (n * avg_x * avg_x), 0) as trend_slope,
                    avg_y as avg_daily_revenue
                FROM trend_stats
            )
            UPDATE category_metrics cm
            LEFT JOIN current_period cp ON cm.category_id = cp.cat_id
            LEFT JOIN previous_period pp ON cm.category_id = pp.cat_id
            LEFT JOIN trend_analysis ta ON cm.category_id = ta.cat_id
            SET 
                cm.growth_rate = CASE
                    WHEN pp.revenue = 0 AND COALESCE(cp.revenue, 0) > 0 THEN 100.0
                    WHEN pp.revenue = 0 OR cp.revenue IS NULL THEN 0.0
                    WHEN ta.trend_slope IS NOT NULL THEN
                        GREATEST(
                            -100.0,
                            LEAST(
                                (ta.trend_slope / NULLIF(ta.avg_daily_revenue, 0)) * 365 * 100,
                                999.99
                            )
                        )
                    ELSE
                        GREATEST(
                            -100.0,
                            LEAST(
                                ((COALESCE(cp.revenue, 0) - pp.revenue) / 
                                    NULLIF(ABS(pp.revenue), 0)) * 100.0,
                                999.99
                            )
                        )
                END,
                cm.avg_margin = CASE 
                    WHEN cp.revenue > 0 THEN
                        (SUM(o.quantity * (o.price - COALESCE(o.discount, 0) - p.cost_price)) / 
                         NULLIF(SUM(o.quantity * (o.price - COALESCE(o.discount, 0))), 0)) * 100
                    ELSE cm.avg_margin
                END,
                cm.last_calculated_at = NOW()
            WHERE cp.cat_id IS NOT NULL OR pp.cat_id IS NOT NULL
        `);

        processedCount = Math.floor(totalProducts * 0.97);
        outputProgress({
            status: 'running',
            operation: 'Growth rates calculated, updating time-based metrics',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        if (isCancelled) return processedCount;

        // Calculate time-based metrics
        await connection.query(`
            INSERT INTO category_time_metrics (
                category_id,
                year,
                month,
                product_count,
                active_products,
                total_value,
                total_revenue,
                avg_margin,
                turnover_rate
            )
            SELECT 
                pc.cat_id,
                YEAR(o.date) as year,
                MONTH(o.date) as month,
                COUNT(DISTINCT p.pid) as product_count,
                COUNT(DISTINCT CASE WHEN p.visible = true THEN p.pid END) as active_products,
                SUM(p.stock_quantity * p.cost_price) as total_value,
                SUM(o.quantity * o.price) as total_revenue,
                CASE
                    WHEN SUM(o.quantity * o.price) > 0 THEN
                        LEAST(
                            GREATEST(
                                SUM(o.quantity * (o.price - GREATEST(p.cost_price, 0))) * 100.0 / 
                                SUM(o.quantity * o.price),
                                -100
                            ),
                            100
                        )
                    ELSE 0
                END as avg_margin,
                COALESCE(
                    LEAST(
                        SUM(o.quantity) / NULLIF(AVG(GREATEST(p.stock_quantity, 0)), 0),
                        999.99
                    ),
                    0
                ) as turnover_rate
            FROM product_categories pc
            JOIN products p ON pc.pid = p.pid
            JOIN orders o ON p.pid = o.pid
            WHERE o.canceled = false
                AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
            GROUP BY pc.cat_id, YEAR(o.date), MONTH(o.date)
            ON DUPLICATE KEY UPDATE
                product_count = VALUES(product_count),
                active_products = VALUES(active_products),
                total_value = VALUES(total_value),
                total_revenue = VALUES(total_revenue),
                avg_margin = VALUES(avg_margin),
                turnover_rate = VALUES(turnover_rate)
        `);

        processedCount = Math.floor(totalProducts * 0.99);
        outputProgress({
            status: 'running',
            operation: 'Time-based metrics calculated, updating category-sales metrics',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        if (isCancelled) return processedCount;

        // Calculate category-sales metrics
        await connection.query(`
            INSERT INTO category_sales_metrics (
                category_id,
                brand,
                period_start,
                period_end,
                avg_daily_sales,
                total_sold,
                num_products,
                avg_price,
                last_calculated_at
            )
            WITH date_ranges AS (
                SELECT 
                    DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY) as period_start,
                    CURRENT_DATE as period_end
                UNION ALL
                SELECT 
                    DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY),
                    DATE_SUB(CURRENT_DATE, INTERVAL 31 DAY)
                UNION ALL
                SELECT 
                    DATE_SUB(CURRENT_DATE, INTERVAL 180 DAY),
                    DATE_SUB(CURRENT_DATE, INTERVAL 91 DAY)
                UNION ALL
                SELECT 
                    DATE_SUB(CURRENT_DATE, INTERVAL 365 DAY),
                    DATE_SUB(CURRENT_DATE, INTERVAL 181 DAY)
            ),
            sales_data AS (
                SELECT 
                    pc.cat_id,
                    COALESCE(p.brand, 'Unknown') as brand,
                    dr.period_start,
                    dr.period_end,
                    COUNT(DISTINCT p.pid) as num_products,
                    SUM(o.quantity) as total_sold,
                    SUM(o.quantity * o.price) as total_revenue,
                    COUNT(DISTINCT DATE(o.date)) as num_days
                FROM products p
                JOIN product_categories pc ON p.pid = pc.pid
                JOIN orders o ON p.pid = o.pid
                CROSS JOIN date_ranges dr
                WHERE o.canceled = false
                    AND o.date BETWEEN dr.period_start AND dr.period_end
                GROUP BY pc.cat_id, p.brand, dr.period_start, dr.period_end
            )
            SELECT 
                cat_id as category_id,
                brand,
                period_start,
                period_end,
                CASE 
                    WHEN num_days > 0 
                    THEN total_sold / num_days 
                    ELSE 0 
                END as avg_daily_sales,
                total_sold,
                num_products,
                CASE 
                    WHEN total_sold > 0 
                    THEN total_revenue / total_sold 
                    ELSE 0 
                END as avg_price,
                NOW() as last_calculated_at
            FROM sales_data
            ON DUPLICATE KEY UPDATE
                avg_daily_sales = VALUES(avg_daily_sales),
                total_sold = VALUES(total_sold),
                num_products = VALUES(num_products),
                avg_price = VALUES(avg_price),
                last_calculated_at = VALUES(last_calculated_at)
        `);

        processedCount = Math.floor(totalProducts * 1.0);
        outputProgress({
            status: 'running',
            operation: 'Category-sales metrics calculated',
            current: processedCount,
            total: totalProducts,
            elapsed: formatElapsedTime(startTime),
            remaining: estimateRemaining(startTime, processedCount, totalProducts),
            rate: calculateRate(startTime, processedCount),
            percentage: ((processedCount / totalProducts) * 100).toFixed(1),
            timing: {
                start_time: new Date(startTime).toISOString(),
                end_time: new Date().toISOString(),
                elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
            }
        });

        return processedCount;
    } catch (error) {
        logError(error, 'Error calculating category metrics');
        throw error;
    } finally {
        if (connection) {
            connection.release();
        }
    }
}

module.exports = calculateCategoryMetrics;