<?php
// opus-arch-predictive-heal.php — Cap 14 Predictive auto-heal (Doctrine 88)
// Linear regression on load/disk/fpm to predict failures
header('Content-Type: application/json');

$metric = $_GET['metric'] ?? 'load';
$window = (int)($_GET['window'] ?? 60); // minutes

// Gather time series
$ts_file = '/var/log/weval/metrics-' . $metric . '.log';
// V54 ALWAYS_APPEND sample before regression
@mkdir('/var/log/weval', 0755, true);
$_v54_load = (float)explode(' ', file_get_contents('/proc/loadavg'))[0];
$_v54_disk_out = []; exec('df -h / | tail -1', $_v54_disk_out);
preg_match('/(\d+)%/', implode(' ', $_v54_disk_out), $_v54_dm);
$_v54_disk_pct = (int)($_v54_dm[1] ?? 0);
$_v54_fpm_out = []; exec('pgrep -c php-fpm', $_v54_fpm_out);
$_v54_fpm = (int)($_v54_fpm_out[0] ?? 0);
$_v54_map = ['load' => $_v54_load, 'disk' => $_v54_disk_pct, 'fpm' => $_v54_fpm];
@file_put_contents($ts_file, time() . ',' . $_v54_map[$metric] . chr(10), FILE_APPEND);

$lines = array_slice(file($ts_file), -max(10, $window));
$pts = [];
foreach ($lines as $l) {
    $parts = explode(',', trim($l));
    if (count($parts) >= 2) $pts[] = ['x'=>(float)$parts[0], 'y'=>(float)$parts[1]];
}
$n = count($pts);
if ($n < 5) {
    echo json_encode(['ok'=>false, 'error'=>'insufficient_data', 'n'=>$n]);
    exit;
}

// Linear regression y = a + b*x
$sx = $sy = $sxy = $sxx = 0;
foreach ($pts as $p) {
    $sx += $p['x']; $sy += $p['y'];
    $sxy += $p['x']*$p['y']; $sxx += $p['x']*$p['x'];
}
$b = ($n*$sxy - $sx*$sy) / ($n*$sxx - $sx*$sx + 0.0001);
$a = ($sy - $b*$sx) / $n;

// Predict next hour
$next_hour = time() + 3600;
$predicted = $a + $b * $next_hour;

// Alert thresholds
$thresholds = ['load'=>5.0, 'disk'=>85, 'fpm'=>150];
$alert = $predicted >= ($thresholds[$metric] ?? 999);

$actions = [];
if ($alert) {
    if ($metric === 'load') $actions[] = 'systemctl restart php-fpm';
    if ($metric === 'disk') $actions[] = 'find /tmp -mmin +60 -delete; docker image prune -f';
    if ($metric === 'fpm') $actions[] = 'systemctl reload php-fpm';
}

echo json_encode([
    'ok'=>true,
    'metric'=>$metric,
    'n_samples'=>$n,
    'regression'=>['slope'=>$b, 'intercept'=>$a],
    'predicted_next_hour'=>round($predicted, 2),
    'threshold'=>$thresholds[$metric] ?? null,
    'alert'=>$alert,
    'recommended_actions'=>$actions,
]);