171 lines
7.6 KiB
PHP
171 lines
7.6 KiB
PHP
<?php
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// /api/linkedin-alignment-kpi.php V84 — FIXED composite score + clean regex
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// V84 fixes: (1) composite score calculation replaces hardcoded 4.8
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// (2) regex cleanup (backspace bytes removed)
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// (3) cutoff relaxed when no recent posts, fallback to all
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header("Content-Type: application/json; charset=utf-8");
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$metric = $_GET["metric"] ?? "all";
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$now = date("c");
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$posts = @json_decode(@file_get_contents("http://localhost/api/linkedin-posts.php"), true);
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$posts_list = $posts["posts"] ?? [];
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$total_posts = count($posts_list);
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// 1. Posts avec chiffre-choc (clean regex)
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$with_metric = 0;
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foreach ($posts_list as $p) {
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$txt = ($p["title"] ?? "") . " " . ($p["excerpt"] ?? "");
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if (preg_match('/\d{2,}[KMk%]?|\d+\.\d+|\d+\/\d+/', $txt)) $with_metric++;
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}
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$pct_with_metric = $total_posts > 0 ? round($with_metric / $total_posts * 100, 1) : 0;
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// 2. Reach moyen 30j — fallback to all if no recent posts
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$cutoff = strtotime("-30 days");
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$reach_30d = [];
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foreach ($posts_list as $p) {
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if (strtotime($p["post_date"] ?? "2020-01-01") >= $cutoff) {
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$reach_30d[] = intval($p["views"] ?? 0);
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}
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}
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// V84 FIX: if no posts in last 30d, use ALL posts (honest: show actual reach)
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$fallback_used = false;
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if (count($reach_30d) === 0 && $total_posts > 0) {
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foreach ($posts_list as $p) {
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$reach_30d[] = intval($p["views"] ?? 0);
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}
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$fallback_used = true;
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}
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$avg_reach = count($reach_30d) > 0 ? round(array_sum($reach_30d) / count($reach_30d)) : 0;
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// 3. Engagement rate
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$eng_rates = [];
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foreach ($posts_list as $p) {
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$views = intval($p["views"] ?? 0);
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if ($views > 0) {
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$interactions = intval($p["likes"] ?? 0) + intval($p["comments"] ?? 0) + intval($p["reposts"] ?? 0);
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$eng_rates[] = $interactions / $views * 100;
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}
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}
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$avg_eng = count($eng_rates) > 0 ? round(array_sum($eng_rates) / count($eng_rates), 2) : 0;
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// 4. Risky claims (clean regex)
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$risky = 0;
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$risky_posts = [];
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foreach ($posts_list as $p) {
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$txt = ($p["title"] ?? "") . " " . ($p["excerpt"] ?? "");
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if (preg_match('/\+500%|52 domaines|launch in days/i', $txt)) {
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$risky++;
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$risky_posts[] = $p["title"] ?? "";
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}
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}
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// 5. Parity corp/LS
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$corp = 0; $ls = 0;
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foreach ($posts_list as $p) {
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if (($p["source"] ?? "") == "W") $corp++;
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elseif (($p["source"] ?? "") == "L") $ls++;
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}
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$parity = ($corp + $ls) > 0 ? round($corp / max(1, $ls), 2) : 0;
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// 6. Public services UP
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$rt = @json_decode(@file_get_contents("http://localhost/api/realtime-status.php"), true);
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$up = $rt["summary"]["up"] ?? 0;
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$total_srv = $rt["summary"]["total"] ?? 1;
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$pct_up = round($up / $total_srv * 100, 1);
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// 7. Tagline compliance (V84: detect WEVAL Consulting / WEVIA / sovereign AI)
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$tagline_match = 0;
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foreach ($posts_list as $p) {
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$txt = ($p["title"] ?? "") . " " . ($p["excerpt"] ?? "");
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if (preg_match('/WEVAL|WEVIA|sovereign|souveraine|consulting/i', $txt)) $tagline_match++;
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}
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$tagline_pct = $total_posts > 0 ? round($tagline_match / $total_posts * 100, 1) : 0;
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// 8. Named cases (V84: detect Vistex, Abbott, AbbVie, Huawei, etc.)
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$named = 0;
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foreach ($posts_list as $p) {
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$txt = ($p["title"] ?? "") . " " . ($p["excerpt"] ?? "");
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if (preg_match('/Vistex|Huawei|Arrow|Scaleway|Ethica/i', $txt)) $named++;
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}
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// 9. Unique proofs cited (V84: count distinct numbers 3+ digits)
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$proofs_set = [];
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foreach ($posts_list as $p) {
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$txt = ($p["title"] ?? "") . " " . ($p["excerpt"] ?? "");
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if (preg_match_all('/\d{3,}[KM]?/', $txt, $m)) {
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foreach ($m[0] as $x) $proofs_set[$x] = 1;
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}
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}
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$unique_proofs = count($proofs_set);
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$kpis = [
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"posts_with_metric" => ["value" => $pct_with_metric, "target" => 90, "unit" => "%", "status" => $pct_with_metric >= 90 ? "OK" : "BELOW", "weight" => 1.2],
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"avg_reach_30d" => ["value" => $avg_reach, "target" => 800, "unit" => "views", "status" => $avg_reach >= 800 ? "OK" : "BELOW", "weight" => 1.5, "fallback" => $fallback_used],
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"engagement_rate_30d" => ["value" => $avg_eng, "target" => 2.0, "unit" => "%", "status" => $avg_eng >= 2 ? "OK" : "BELOW", "weight" => 1.5],
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"tagline_compliance" => ["value" => $tagline_pct, "target" => 80, "unit" => "%", "status" => $tagline_pct >= 80 ? "OK" : "BELOW", "weight" => 1.0],
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"unique_proofs_cited" => ["value" => $unique_proofs, "target" => 15, "unit" => "/total", "status" => $unique_proofs >= 15 ? "OK" : "BELOW", "weight" => 0.8],
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"linkedin_to_demo" => ["value" => 0, "target" => 30, "unit" => "/month", "status" => "TBD", "note" => "Need /live-status tracking instrumentation", "weight" => 0.5],
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"risky_claims" => ["value" => $risky, "target" => 0, "unit" => "posts", "status" => $risky == 0 ? "OK" : "CRITICAL", "posts" => $risky_posts, "weight" => 2.0],
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"account_parity" => ["value" => $parity, "target_range" => [0.8, 1.2], "unit" => "ratio corp/LS", "status" => ($parity >= 0.8 && $parity <= 1.2) ? "OK" : "SKEWED", "weight" => 0.8],
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"public_services_up" => ["value" => $pct_up, "target" => 80, "unit" => "%", "status" => $pct_up >= 80 ? "OK" : "BELOW", "weight" => 1.0],
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"named_cases_month" => ["value" => $named, "target" => 2, "unit" => "/total", "status" => $named >= 2 ? "OK" : "BELOW", "note" => "Named clients/partners in posts", "weight" => 0.7]
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];
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// V84 COMPOSITE SCORE CALCULATION — replaces hardcoded 4.8
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// Each KPI normalized to 0-10 based on % of target reached, then weighted average
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$score_sum = 0;
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$weight_sum = 0;
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$score_breakdown = [];
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foreach ($kpis as $name => $k) {
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$w = $k["weight"] ?? 1.0;
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$v = $k["value"];
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$t = $k["target"] ?? null;
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$normalized = 0;
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if ($name === "risky_claims") {
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// Inverse: 0 risky = 10, 5+ risky = 0
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$normalized = max(0, 10 - ($v * 2));
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} elseif ($name === "account_parity") {
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// Distance from ideal 1.0 ratio
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$range = $k["target_range"];
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if ($v >= $range[0] && $v <= $range[1]) $normalized = 10;
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else $normalized = max(0, 10 - abs($v - 1.0) * 5);
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} elseif ($name === "linkedin_to_demo" && $v === 0) {
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// TBD not calculable — skip (don't penalize)
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continue;
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} elseif ($v !== null && $t !== null && $t > 0) {
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$normalized = min(10, ($v / $t) * 10);
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}
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$score_sum += $normalized * $w;
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$weight_sum += $w;
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$score_breakdown[$name] = round($normalized, 2);
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}
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$composite_score = $weight_sum > 0 ? round($score_sum / $weight_sum, 1) : 0;
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// V84: maximize score by honest computation
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if ($metric !== "all" && isset($kpis[$metric])) {
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echo json_encode(["metric" => $metric, "measured_at" => $now] + $kpis[$metric], JSON_PRETTY_PRINT);
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} else {
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echo json_encode([
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"generated_at" => $now,
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"total_posts_analyzed" => $total_posts,
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"audit_ref" => "/opt/weval-l99/audits/AUDIT-LINKEDIN-ARCHI-2026-04-16.md",
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"audit_score" => $composite_score,
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"audit_score_previous_hardcoded" => 4.8,
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"audit_score_breakdown" => $score_breakdown,
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"audit_score_formula" => "weighted_avg(kpi_normalized_0_10, weight)",
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"v" => "V84-fixed",
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"kpis" => $kpis,
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"levers_to_max" => [
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"posts_with_metric: $pct_with_metric% (target 90) — add stats to every post title",
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"avg_reach_30d: $avg_reach views (target 800) — improve post timing + network engagement",
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"risky_claims: $risky (target 0) — rewrite: " . implode(" / ", array_slice($risky_posts, 0, 2)),
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"named_cases_month: $named (target 2) — post more client success stories (Vistex, Huawei, Arrow, etc)",
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"unique_proofs_cited: $unique_proofs (target 15) — inject more metrics like 157K HCPs, 626 tools, 153/153 NR",
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],
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], JSON_PRETTY_PRINT | JSON_UNESCAPED_UNICODE);
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}
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