<?php
defined('BASEPATH') or exit('No direct script access allowed');

class N8n_company extends My_Controller
{

    public function __construct()
    {
        parent::__construct();
    }

    public function predictive($start_date = '', $end_date = '', $top = 30)
    {
        if (!$start_date || !$end_date) {
            $end_date = date('Y-m-d');
            $start_date = date('Y-m-d', strtotime('-6 months'));
        }

        $top = is_numeric($top) ? intval($top) : 30;
        $params = [$start_date, $end_date];

        $sql = "
        SELECT 
            c.M_CompanyID,
            c.M_CompanyName,
            MAX(o.T_OrderHeaderDate) AS last_order_date,
            COUNT(DISTINCT o.T_OrderHeaderID) AS order_count,
            SUM(o.T_OrderHeaderTotal) AS total_sales,
            MAX(p.F_PaymentDate) AS last_payment_date,
            SUM(p.F_PaymentTotal) AS total_payment,
            AVG(DATEDIFF(p.F_PaymentDate, o.T_OrderHeaderDate)) AS avg_payment_delay,
            COUNT(DISTINCT od.T_OrderDetailT_TestName) AS distinct_tests
        FROM m_company c
        JOIN t_orderheader o 
            ON o.T_OrderHeaderM_CompanyID = c.M_CompanyID
            AND o.T_OrderHeaderIsActive = 'Y'
            AND o.T_OrderHeaderDate BETWEEN ? AND ?
        JOIN f_payment p 
            ON p.F_PaymentT_OrderHeaderID = o.T_OrderHeaderID 
            AND p.F_PaymentIsActive = 'Y'
        JOIN t_orderdetail od 
            ON od.T_OrderDetailT_OrderHeaderID = o.T_OrderHeaderID 
            AND od.T_OrderDetailIsActive = 'Y'
        WHERE c.M_CompanyIsActive = 'Y'
        GROUP BY c.M_CompanyID
        order by total_sales desc limit 0,150
    ";

        $query = $this->db->query($sql, $params);
        $data = $query->result_array();

        $max = [
            'recency' => 365,
            'frequency' => 100,
            'monetary' => 1000000,
            'payment_delay' => 90,
            'item_breadth' => 50,
        ];

        $today = new DateTime();

        foreach ($data as &$row) {
            $recency_days = 999;
            if (!empty($row['last_order_date'])) {
                $recency_days = $today->diff(new DateTime($row['last_order_date']))->days;
            }

            $order_count = (int) $row['order_count'];
            $total_sales = (float) $row['total_sales'];
            $avg_payment_delay = isset($row['avg_payment_delay']) ? (float)$row['avg_payment_delay'] : $max['payment_delay'];
            $distinct_tests = (int) $row['distinct_tests'];

            $recency_score   = max(0, 100 - min(($recency_days / $max['recency']) * 100, 100));
            $frequency_score = min(($order_count / $max['frequency']) * 100, 100);
            $monetary_score  = min(($total_sales / $max['monetary']) * 100, 100);
            $payment_score   = max(0, 100 - min(($avg_payment_delay / $max['payment_delay']) * 100, 100));
            $breadth_score   = min(($distinct_tests / $max['item_breadth']) * 100, 100);

            $final_score = (
                0.25 * $recency_score +
                0.25 * $frequency_score +
                0.25 * $monetary_score +
                0.15 * $payment_score +
                0.10 * $breadth_score
            );

            $row['recency_days'] = $recency_days;
            $row['score_recency'] = round($recency_score, 2);
            $row['score_frequency'] = round($frequency_score, 2);
            $row['score_monetary'] = round($monetary_score, 2);
            $row['score_payment'] = round($payment_score, 2);
            $row['score_breadth'] = round($breadth_score, 2);
            $row['score_final'] = round($final_score, 2);
        }

        usort($data, function ($a, $b) {
            return $b['score_final'] <=> $a['score_final'];
        });

        $top_data = array_slice($data, 0, $top);

        // Only for top data, compute monthly_sales & avg_days_between_orders
        foreach ($top_data as &$row) {
            $pattern = $this->analyze_order_pattern($row['M_CompanyID'], $start_date, $end_date);
            $row['avg_days_between_orders'] = $pattern['avg_days_between_orders'];
            $row['monthly_sales'] = $pattern['monthly_sales'];
        }

        header('Content-Type: application/json');
        echo json_encode([
            'status' => 'success',
            'start_date' => $start_date,
            'end_date' => $end_date,
            'top' => $top,
            'total_companies' => count($data),
            'data' => $top_data
        ]);
        exit;
    }
    public function old_predictive($start_date = '', $end_date = '', $top = 30)
    {
        if (!$start_date || !$end_date) {
            $end_date = date('Y-m-d');
            $start_date = date('Y-m-d', strtotime('-6 months'));
        }

        $top = is_numeric($top) ? intval($top) : 50;
        $params = [$start_date, $end_date];

        $sql = "
            SELECT 
                c.M_CompanyID,
                c.M_CompanyName,
                MAX(o.T_OrderHeaderDate) AS last_order_date,
                COUNT(DISTINCT o.T_OrderHeaderID) AS order_count,
                SUM(o.T_OrderHeaderTotal) AS total_sales,
                MAX(p.F_PaymentDate) AS last_payment_date,
                SUM(p.F_PaymentTotal) AS total_payment,
                AVG(DATEDIFF(p.F_PaymentDate, o.T_OrderHeaderDate)) AS avg_payment_delay,
                COUNT(DISTINCT od.T_OrderDetailT_TestName) AS distinct_tests
            FROM m_company c
            JOIN t_orderheader o 
                ON o.T_OrderHeaderM_CompanyID = c.M_CompanyID
                AND o.T_OrderHeaderIsActive = 'Y'
                AND o.T_OrderHeaderDate BETWEEN ? AND ?
            JOIN f_payment p 
                ON p.F_PaymentT_OrderHeaderID = o.T_OrderHeaderID 
                AND p.F_PaymentIsActive = 'Y'
            JOIN t_orderdetail od 
                ON od.T_OrderDetailT_OrderHeaderID = o.T_OrderHeaderID 
                AND od.T_OrderDetailIsActive = 'Y'
            WHERE c.M_CompanyIsActive = 'Y'
            GROUP BY c.M_CompanyID
            order by total_sales desc limit 0,200
        ";

        $query = $this->db->query($sql, $params);
        $data = $query->result_array();

        // Scoring max thresholds
        $max = [
            'recency' => 365,
            'frequency' => 100,
            'monetary' => 1000000,
            'payment_delay' => 90,
            'item_breadth' => 50,
        ];

        $today = new DateTime();

        foreach ($data as &$row) {
            $recency_days = 999;
            if (!empty($row['last_order_date'])) {
                $recency_days = $today->diff(new DateTime($row['last_order_date']))->days;
            }

            $order_count = (int) $row['order_count'];
            $total_sales = (float) $row['total_sales'];
            $avg_payment_delay = isset($row['avg_payment_delay']) ? (float)$row['avg_payment_delay'] : $max['payment_delay'];
            $distinct_tests = (int) $row['distinct_tests'];

            // Score calculation
            $recency_score   = max(0, 100 - min(($recency_days / $max['recency']) * 100, 100));
            $frequency_score = min(($order_count / $max['frequency']) * 100, 100);
            $monetary_score  = min(($total_sales / $max['monetary']) * 100, 100);
            $payment_score   = max(0, 100 - min(($avg_payment_delay / $max['payment_delay']) * 100, 100));
            $breadth_score   = min(($distinct_tests / $max['item_breadth']) * 100, 100);

            $final_score = (
                0.25 * $recency_score +
                0.25 * $frequency_score +
                0.25 * $monetary_score +
                0.15 * $payment_score +
                0.10 * $breadth_score
            );

            $row['recency_days'] = $recency_days;
            $row['score_recency'] = round($recency_score, 2);
            $row['score_frequency'] = round($frequency_score, 2);
            $row['score_monetary'] = round($monetary_score, 2);
            $row['score_payment'] = round($payment_score, 2);
            $row['score_breadth'] = round($breadth_score, 2);
            $row['score_final'] = round($final_score, 2);
        }

        // Sort by score
        // usort($data, fn($a, $b) => $b['score_final'] <=> $a['score_final']);
        usort($data, function ($a, $b) {
            return $b['score_final'] <=> $a['score_final'];
        });
        $top_data = array_slice($data, 0, $top);

        // Output JSON
        header('Content-Type: application/json');
        echo json_encode([
            'status' => 'success',
            'start_date' => $start_date,
            'end_date' => $end_date,
            'top' => $top,
            'total_companies' => count($data),
            'data' => $top_data
        ]);
        exit;
    }

    private function analyze_order_pattern($company_id, $start_date, $end_date)
    {
        $result = [
            'monthly_sales' => [],
            'avg_days_between_orders' => null
        ];

        $orders = $this->db->query("
        SELECT T_OrderHeaderDate, T_OrderHeaderTotal
        FROM t_orderheader
        WHERE T_OrderHeaderIsActive = 'Y'
            AND T_OrderHeaderM_CompanyID = ?
            AND T_OrderHeaderDate BETWEEN ? AND ?
        ORDER BY T_OrderHeaderDate ASC
    ", [$company_id, $start_date, $end_date])->result();

        $prev_date = null;
        $interval_sum = 0;
        $interval_count = 0;

        foreach ($orders as $order) {
            $month = date('Y-m', strtotime($order->T_OrderHeaderDate));
            if (!isset($result['monthly_sales'][$month])) {
                $result['monthly_sales'][$month] = 0;
            }
            $result['monthly_sales'][$month] += (float) $order->T_OrderHeaderTotal;

            if ($prev_date) {
                $diff_days = (strtotime($order->T_OrderHeaderDate) - strtotime($prev_date)) / (60 * 60 * 24);
                $interval_sum += $diff_days;
                $interval_count++;
            }

            $prev_date = $order->T_OrderHeaderDate;
        }

        if ($interval_count > 0) {
            $result['avg_days_between_orders'] = round($interval_sum / $interval_count, 1);
        }

        return $result;
    }
}