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feat: Add bulk AI product analysis feature with a new UI component and API route.

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Michael committed 2026-02-22 11:00:44 +01:00
1 parent 4b3672e65a
commit 7f9c07b58e
2 files changed
+24 -140
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src/features/grid/components/bulk-ai-analyzer.tsx
+20 -65
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@@ -13,70 +13,20 @@ export function BulkAiAnalyzer() {
const handleAnalyze = async () => {
const { rows } = useGridStore.getState();
const supplierTotalCa = rows.reduce((acc, row) => acc + (row.totalCa || 0), 0);
console.log("[BulkAiAnalyzer] Starting analysis on", rows.length, "rows");
// --- Statistiques globales pour contextualiser le score ---
const allScores = rows.map(r => r.score || 0).sort((a, b) => a - b);
const totalProducts = rows.length;
const maxStoreCount = Math.max(...rows.map(r => r.workingStores?.length || 1), 1);
// Percentile par produit
const scorePercentileMap = new Map<string, number>();
rows.forEach(r => {
const productScore = r.score || 0;
const rank = allScores.filter(s => s <= productScore).length;
scorePercentileMap.set(r.codein, Math.round((rank / totalProducts) * 100));
});
// Double poids par nomenclature niveau 2
const nomenclature2CaMap = new Map<string, number>();
rows.forEach(r => {
const n2 = r.libelleNiveau2 || "Non classe";
nomenclature2CaMap.set(n2, (nomenclature2CaMap.get(n2) || 0) + (r.totalCa || 0));
});
const nomenclature2WeightMap = new Map<string, number>();
nomenclature2CaMap.forEach((ca, n2) => {
nomenclature2WeightMap.set(n2, supplierTotalCa > 0
? parseFloat(((ca / supplierTotalCa) * 100).toFixed(1))
: 0);
});
// Préparer les payloads enrichis pour chaque produit
const productPayloads = rows.map(r => {
const rawCaWeight = supplierTotalCa > 0
? parseFloat(((r.totalCa / supplierTotalCa) * 100).toFixed(3))
: 0;
const storeCount = r.workingStores?.length || 1;
const storeRatio = storeCount < maxStoreCount ? maxStoreCount / storeCount : 1;
const adjustedCaWeight = parseFloat((rawCaWeight * storeRatio).toFixed(3));
const n2 = r.libelleNiveau2 || "Non classe";
const n2Ca = nomenclature2CaMap.get(n2) || 1;
const weightInNomenclature2 = parseFloat((((r.totalCa || 0) / n2Ca) * 100).toFixed(2));
const moisActifs = Object.values(r.sales12m || {}).filter(v => v > 0).length;
return {
codein: r.codein,
libelle1: r.libelle1 || "",
totalCa: r.totalCa || 0,
tauxMarge: r.tauxMarge || 0,
totalQuantite: r.totalQuantite || 0,
sales12m: r.sales12m || {},
codeGamme: r.codeGamme || null,
score: r.score || 0,
// Champs enrichis
weightInNomenclature2,
adjustedCaWeight,
nomenclature2Weight: nomenclature2WeightMap.get(n2) ?? 0,
scorePercentile: scorePercentileMap.get(r.codein) ?? 0,
moisActifs,
nomenclature: `${r.libelleNiveau1 || ""} > ${r.libelleNiveau2 || ""} > ${r.libelle3 || ""}`,
batchMode: true,
};
});
// Payload identique à l'analyse individuelle — aucun enrichissement
const productPayloads = rows.map(r => ({
codein: r.codein,
libelle1: r.libelle1 || "",
totalCa: r.totalCa || 0,
tauxMarge: r.tauxMarge || 0,
totalQuantite: r.totalQuantite || 0,
sales12m: r.sales12m || {},
codeGamme: r.codeGamme || null,
score: r.score || 0,
}));
setIsAnalyzing(true);
let completed = 0;
@@ -119,10 +69,15 @@ export function BulkAiAnalyzer() {
errors++;
} else {
const data = await res.json();
if (data.recommandationGamme) {
setDraftGamme(data.codein, data.recommandationGamme);
const justification = `Gamme ${data.recommandationGamme} — ${data.justificationCourte || "Analyse effectuée."}`;
setInsight(data.codein, justification, data.isDuplicate ?? false);
if (data.insight) {
// Extraire la recommandation (première lettre A, C ou Z)
const match = data.insight.match(/^[^A-Z]*([ACZ])\b/i);
const gamme = match ? match[1].toUpperCase() : null;
if (gamme) {
setDraftGamme(data.codein, gamme);
setInsight(data.codein, data.insight, false);
}
}
}
} catch {