feat: Implement bulk AI product categorization through a new UI component and API route.

src/app/api/ai/analyze/route.ts

@@ -11,10 +11,20 @@ const AnalyzeSchema = z.object({
     sales12m: z.record(z.string(), z.number()),
     codeGamme: z.string().nullable(),
     score: z.number().nullable().optional(),
+    // Champs enrichis (optionnels, envoyés par le batch)
+    weightInNomenclature2: z.number().optional(),
+    adjustedCaWeight: z.number().optional(),
+    nomenclature2Weight: z.number().optional(),
+    scorePercentile: z.number().optional(),
+    moisActifs: z.number().optional(),
+    nomenclature: z.string().optional(),
+    // Mode batch : retourne du JSON structuré au lieu de texte libre
+    batchMode: z.boolean().optional(),
 });
 
 const OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions";
-const SYSTEM_PROMPT = `Tu es un expert en stratégie d'achat retail. Ton rôle est de catégoriser ce produit (A=Permanent, C=Saisonnier, Z=Sortie) via une analyse de VALEUR CONTEXTUELLE.
+
+const SYSTEM_PROMPT_INDIVIDUAL = `Tu es un expert en stratégie d'achat retail. Ton rôle est de catégoriser ce produit (A=Permanent, C=Saisonnier, Z=Sortie) via une analyse de VALEUR CONTEXTUELLE.
 
 L'IMPORTANCE ÉCONOMIQUE PRIME SUR LE SCORE DE PERFORMANCE RELATIVE.
 
@@ -31,39 +41,78 @@ DÉFINITION :
 Réponse courte, mentionnant la contribution économique et le score. Ne mentionne aucun seuil en euros.
 Format: "[Recommandation]: [Justification basée sur la valeur et le score]"`;
 
+const SYSTEM_PROMPT_BATCH = `Tu es un expert en assortiment retail. Categorise ce produit : A (garder en permanence), C (saisonnier), Z (sortir de la gamme).
+
+L'IMPORTANCE ECONOMIQUE PRIME SUR LE SCORE.
+
+DONNEES CLES :
+- weightInNomenclature2 : % du CA du produit dans sa categorie. LE CRITERE PRINCIPAL. >= 5% = pilier de categorie → A.
+- adjustedCaWeight : % du CA produit dans le total fournisseur (extrapole reseau).
+- scorePercentile : rang du produit parmi le fournisseur (50 = median). Utilise CA POUR COMPARER, pas le score brut.
+- moisActifs : mois avec ventes > 0 sur 12 mois. >= 8 = rotation reguliere → A. <= 3 = sporadique.
+
+REGLES :
+1. weightInNomenclature2 >= 5% → A (pilier de categorie)
+2. moisActifs >= 8 → A (fond de rayon, rotation reguliere)
+3. scorePercentile >= 50 ET moisActifs >= 4 → A (performeur regulier)
+4. moisActifs 2-4 avec pics concentres → C (saisonnier)
+5. Z seulement si TOUT est faible : weightInNomenclature2 bas + moisActifs < 5 + scorePercentile < 30
+
+Reponds UNIQUEMENT en JSON : {"recommandationGamme":"A|C|Z","justificationCourte":"..."}`;
+
 export async function POST(req: NextRequest) {
-    console.log("[AI] Analysis request received.");
     const config = await getSavedDatabaseConfig();
     const apiKey = process.env.OPENROUTER_API_KEY || config?.openRouterKey;
     const model = config?.openRouterModel || "google/gemini-flash-1.5";
 
     if (!apiKey) {
-        console.error("[AI] OPENROUTER_API_KEY is missing (neither in env nor in config file).");
+        console.error("[AI] OPENROUTER_API_KEY is missing.");
         return NextResponse.json({ error: "OPENROUTER_API_KEY not configured. Please set it in Settings." }, { status: 503 });
     }
 
-    console.log(`[AI] Using model: ${model} with API key starting with ${apiKey.substring(0, 10)}...`);
-
     const body = await req.json();
     const parsed = AnalyzeSchema.safeParse(body);
     if (!parsed.success) {
         return NextResponse.json({ error: "Invalid payload", details: parsed.error.flatten() }, { status: 400 });
     }
 
-    const { codein, libelle1, totalCa, tauxMarge, totalQuantite, sales12m, codeGamme, score } = parsed.data;
+    const p = parsed.data;
+    const isBatch = p.batchMode === true;
 
     // Format monthly sales for the prompt
-    const monthlySummary = Object.entries(sales12m)
+    const monthlySummary = Object.entries(p.sales12m)
         .map(([k, v]) => `${k}: ${Math.round(v)} unités`)
         .join(", ");
 
-    const userMessage = `Produit: "${libelle1}" (Code: ${codein})
+    let userMessage: string;
-Gamme actuelle: ${codeGamme ?? "Non définie"}
+    let systemPrompt: string;
-Score de performance: ${score ?? "N/A"}/100
+
-CA total 12m: ${totalCa.toFixed(0)}€ | Taux de marge: ${tauxMarge.toFixed(1)}% | Volume: ${Math.round(totalQuantite)} unités
+    if (isBatch) {
+        // Mode batch : prompt simplifié avec métriques enrichies, PAS de codeGamme
+        systemPrompt = SYSTEM_PROMPT_BATCH;
+        const enrichedLines = [
+            `Produit: "${p.libelle1}" (${p.codein})`,
+            p.nomenclature ? `Nomenclature: ${p.nomenclature}` : null,
+            `CA: ${p.totalCa.toFixed(0)} EUR | Marge: ${p.tauxMarge.toFixed(1)}% | Volume: ${Math.round(p.totalQuantite)} unites`,
+            p.weightInNomenclature2 != null ? `Poids dans sa categorie N2: ${p.weightInNomenclature2}%` : null,
+            p.adjustedCaWeight != null ? `Poids global fournisseur (ajuste): ${p.adjustedCaWeight}%` : null,
+            p.nomenclature2Weight != null ? `Poids de la categorie N2 dans le fournisseur: ${p.nomenclature2Weight}%` : null,
+            p.scorePercentile != null ? `Rang percentile: ${p.scorePercentile}/100` : null,
+            p.moisActifs != null ? `Mois actifs (ventes > 0): ${p.moisActifs}/12` : null,
+            `Historique: ${monthlySummary}`,
+        ].filter(Boolean).join("\n");
+        userMessage = enrichedLines;
+    } else {
+        // Mode individuel : prompt original
+        systemPrompt = SYSTEM_PROMPT_INDIVIDUAL;
+        userMessage = `Produit: "${p.libelle1}" (Code: ${p.codein})
+Gamme actuelle: ${p.codeGamme ?? "Non définie"}
+Score de performance: ${p.score ?? "N/A"}/100
+CA total 12m: ${p.totalCa.toFixed(0)}€ | Taux de marge: ${p.tauxMarge.toFixed(1)}% | Volume: ${Math.round(p.totalQuantite)} unités
 Historique mensuel: ${monthlySummary}
 
 Quelle gamme recommandes-tu et pourquoi ?`;
+    }
 
     try {
         const response = await fetch(OPENROUTER_URL, {
@@ -77,23 +126,47 @@ Quelle gamme recommandes-tu et pourquoi ?`;
             body: JSON.stringify({
                 model,
                 messages: [
-                    { role: "system", content: SYSTEM_PROMPT },
+                    { role: "system", content: systemPrompt },
                     { role: "user", content: userMessage },
                 ],
+                ...(isBatch ? { response_format: { type: "json_object" } } : {}),
                 max_tokens: 150,
-                temperature: 0.3,
+                temperature: 0.1,
             }),
         });
 
+        if (response.status === 429) {
+            const retryAfter = response.headers.get("Retry-After") || response.headers.get("x-ratelimit-reset-requests");
+            const waitSeconds = retryAfter ? parseInt(retryAfter, 10) : 60;
+            return NextResponse.json({ error: "rate_limited", retryAfter: waitSeconds }, { status: 429 });
+        }
+
         if (!response.ok) {
             const err = await response.text();
             return NextResponse.json({ error: `OpenRouter error: ${err}` }, { status: response.status });
         }
 
         const data = await response.json();
-        const insight = data.choices?.[0]?.message?.content ?? "Analyse indisponible.";
+        const content: string = data.choices?.[0]?.message?.content ?? "";
 
-        return NextResponse.json({ insight, codein });
+        if (isBatch) {
+            // Parse JSON response for batch mode
+            let jsonStr = content;
+            const start = content.indexOf("{");
+            const end = content.lastIndexOf("}");
+            if (start !== -1 && end !== -1) {
+                jsonStr = content.slice(start, end + 1);
+            }
+            const result = JSON.parse(jsonStr);
+            return NextResponse.json({
+                codein: p.codein,
+                recommandationGamme: result.recommandationGamme || "A",
+                justificationCourte: result.justificationCourte || "",
+                isDuplicate: false,
+            });
+        } else {
+            return NextResponse.json({ insight: content, codein: p.codein });
+        }
     } catch (err) {
         const msg = err instanceof Error ? err.message : "Unknown error";
         return NextResponse.json({ error: msg }, { status: 500 });

src/features/grid/components/bulk-ai-analyzer.tsx

@@ -4,7 +4,6 @@ import React, { useState } from "react";
 import { useGridStore } from "@/features/grid/store/use-grid-store";
 import { useAiCopilotStore } from "@/features/ai-copilot/store/use-ai-copilot-store";
 import { Sparkles, Loader2, CheckCircle2 } from "lucide-react";
-import { ProductRow } from "@/types/grid";
 
 export function BulkAiAnalyzer() {
     const [isAnalyzing, setIsAnalyzing] = useState(false);
@@ -15,29 +14,16 @@ export function BulkAiAnalyzer() {
     const handleAnalyze = async () => {
         const { rows } = useGridStore.getState();
         const supplierTotalCa = rows.reduce((acc, row) => acc + (row.totalCa || 0), 0);
-        const supplierTotalMarge = rows.reduce((acc, row) => acc + (row.totalMarge || 0), 0);
 
-        console.log("[BulkAiAnalyzer] Starting analysis on", rows.length, "rows", { supplierTotalCa, supplierTotalMarge });
+        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 medianScore = totalProducts > 0
-            ? (totalProducts % 2 === 0
-                ? (allScores[totalProducts / 2 - 1] + allScores[totalProducts / 2]) / 2
-                : allScores[Math.floor(totalProducts / 2)])
-            : 0;
-
-        const scoreDistribution = {
-            above70: allScores.filter(s => s > 70).length,
-            between30and70: allScores.filter(s => s >= 30 && s <= 70).length,
-            below30: allScores.filter(s => s < 30).length,
-        };
-
         const maxStoreCount = Math.max(...rows.map(r => r.workingStores?.length || 1), 1);
 
-        // Percentile par produit (rang centile 0-100)
+        // Percentile par produit
         const scorePercentileMap = new Map<string, number>();
         rows.forEach(r => {
             const productScore = r.score || 0;
@@ -45,7 +31,7 @@ export function BulkAiAnalyzer() {
             scorePercentileMap.set(r.codein, Math.round((rank / totalProducts) * 100));
         });
 
-        // --- Double poids par nomenclature niveau 2 ---
+        // Double poids par nomenclature niveau 2
         const nomenclature2CaMap = new Map<string, number>();
         rows.forEach(r => {
             const n2 = r.libelleNiveau2 || "Non classe";
@@ -59,99 +45,69 @@ export function BulkAiAnalyzer() {
                 : 0);
         });
 
-        const nomenclature2Count = nomenclature2CaMap.size;
+        // 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;
 
-        console.log("[BulkAiAnalyzer] Stats:", { totalProducts, medianScore, scoreDistribution, nomenclature2Count, maxStoreCount });
+            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,
+            };
+        });
 
         setIsAnalyzing(true);
         let completed = 0;
+        let errors = 0;
+
+        setProgress({ current: 0, total: productPayloads.length, message: "Initialisation...", errors: 0 });
 
         try {
-            // Group by Nomenclature (libelle3 or a fallback)
+            const CONCURRENCY = 5;
-            const groups: Record<string, ProductRow[]> = {};
+            const remaining = [...productPayloads];
-            rows.forEach(r => {
-                const rayon = r.libelle3 || "Non classifié";
-                if (!groups[rayon]) groups[rayon] = [];
-                groups[rayon].push(r);
-            });
-
-            const CHUNK_SIZE = 50;
-            const chunks: { rayon: string; items: ProductRow[] }[] = [];
-
-            for (const [rayon, items] of Object.entries(groups)) {
-                for (let i = 0; i < items.length; i += CHUNK_SIZE) {
-                    chunks.push({ rayon, items: items.slice(i, i + CHUNK_SIZE) });
-                }
-            }
-
-            setProgress({ current: 0, total: chunks.length, message: "Initialisation...", errors: 0 });
-
-            // Process with a continuous concurrency limit
-            const CONCURRENCY = 3;
-            const remainingChunks = [...chunks];
 
             const processNext = async () => {
-                const chunk = remainingChunks.shift();
+                const payload = remaining.shift();
-                if (!chunk) return;
+                if (!payload) return;
-
-                const payloadProducts = chunk.items.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));
-
-                    // Compter les mois actifs (ventes > 0) pré-calculé
-                    const moisActifs = Object.values(r.sales12m || {}).filter(v => v > 0).length;
-
-                    return {
-                        codein: r.codein,
-                        nom: r.libelle1,
-                        ca: r.totalCa || 0,
-                        adjustedCaWeight,
-                        weightInNomenclature2,
-                        nomenclature2Weight: nomenclature2WeightMap.get(n2) ?? 0,
-                        ventes: r.totalQuantite || 0,
-                        marge: r.totalMarge ? parseFloat(((r.totalMarge / (r.totalCa || 1)) * 100).toFixed(1)) : 0,
-                        scorePercentile: scorePercentileMap.get(r.codein) ?? 0,
-                        moisActifs,
-                        storeCount,
-                        nomenclature: `${r.libelleNiveau1 || ""} > ${r.libelleNiveau2 || ""} > ${r.libelle3 || ""}`,
-                    };
-                });
 
                 try {
                     let res: Response | null = null;
-                    const MAX_CHUNK_RETRIES = 3;
+                    const MAX_RETRIES = 3;
 
-                    for (let attempt = 1; attempt <= MAX_CHUNK_RETRIES; attempt++) {
+                    for (let attempt = 1; attempt <= MAX_RETRIES; attempt++) {
-                        res = await fetch("/api/ai/batch-analyze", {
+                        res = await fetch("/api/ai/analyze", {
                             method: "POST",
                             headers: { "Content-Type": "application/json" },
-                            body: JSON.stringify({
+                            body: JSON.stringify(payload)
-                                rayon: chunk.rayon,
-                                products: payloadProducts,
-                                supplierTotalCa,
-                                supplierTotalMarge,
-                                supplierStats: {
-                                    totalProducts,
-                                    medianScore: Math.round(medianScore * 10) / 10,
-                                    scoreDistribution,
-                                    maxStoreCount,
-                                    nomenclature2Count,
-                                }
-                            })
                         });
 
-                        if (res.status === 429 && attempt < MAX_CHUNK_RETRIES) {
+                        if (res.status === 429 && attempt < MAX_RETRIES) {
                             const errData = await res.json().catch(() => ({}));
-                            const waitSecs = errData.retryAfter ?? (30 * attempt);
+                            const waitSecs = errData.retryAfter ?? (15 * attempt);
                             for (let t = waitSecs; t > 0; t--) {
-                                setProgress(prev => ({ ...prev, message: `⏳ Lot ${completed + 1}/${chunks.length} — Limite API, reprise dans ${t}s...` }));
+                                setProgress(prev => ({ ...prev, message: `⏳ ${completed}/${productPayloads.length} — Limite API, reprise dans ${t}s...` }));
                                 await new Promise(r => setTimeout(r, 1000));
                             }
                             continue;
@@ -160,41 +116,43 @@ export function BulkAiAnalyzer() {
                     }
 
                     if (!res || !res.ok) {
-                        const errText = await res?.text().catch(() => "");
+                        errors++;
-                        console.error(`[BulkAiAnalyzer] Erreur HTTP sur le lot ${chunk.rayon}:`, errText);
-                        setProgress(prev => ({ ...prev, errors: prev.errors + 1 }));
                     } else {
                         const data = await res.json();
-                        if (data && Array.isArray(data.results)) {
+                        if (data.recommandationGamme) {
-                            data.results.forEach((reco: any) => {
+                            setDraftGamme(data.codein, data.recommandationGamme);
-                                if (reco.codein && reco.recommandationGamme) {
+                            const justification = `Gamme ${data.recommandationGamme} — ${data.justificationCourte || "Analyse effectuée."}`;
-                                    setDraftGamme(reco.codein, reco.recommandationGamme);
+                            setInsight(data.codein, justification, data.isDuplicate ?? false);
-                                    const justification = `Gamme ${reco.recommandationGamme} — ${reco.justificationCourte || "Analyse effectuée."}`;
-                                    setInsight(reco.codein, justification, reco.isDuplicate ?? false);
-                                }
-                            });
                         }
                     }
-                } catch (e) {
+                } catch {
-                    console.error(`[BulkAiAnalyzer] Failed fetch for chunk ${chunk.rayon}:`, e);
+                    errors++;
-                    setProgress(prev => ({ ...prev, errors: prev.errors + 1 }));
                 } finally {
                     completed++;
-                    setProgress(prev => ({
+                    setProgress({
-                        ...prev,
                         current: completed,
-                        message: completed === chunks.length ? "Analyse terminée" : `Analyse: ${completed}/${chunks.length} lots`
+                        total: productPayloads.length,
-                    }));
+                        message: completed === productPayloads.length
-                    // Process next chunk in the same worker
+                            ? "Analyse terminée"
+                            : `Analyse: ${completed}/${productPayloads.length} produits`,
+                        errors,
+                    });
                     await processNext();
                 }
             };
 
-            // Start workers
+            const workers = Array.from(
-            const workers = Array.from({ length: Math.min(CONCURRENCY, chunks.length) }, () => processNext());
+                { length: Math.min(CONCURRENCY, productPayloads.length) },
+                () => processNext()
+            );
             await Promise.all(workers);
 
-            setProgress(prev => ({ ...prev, current: completed, total: chunks.length, message: `Analyse terminée ! (${prev.errors > 0 ? prev.errors + " erreurs" : "succès"})` }));
+            setProgress(prev => ({
+                ...prev,
+                current: completed,
+                total: productPayloads.length,
+                message: `Analyse terminée ! (${errors > 0 ? errors + " erreurs" : "succès"})`,
+            }));
             setTimeout(() => setIsAnalyzing(false), 3000);
 
         } catch (error) {
@@ -227,7 +185,7 @@ export function BulkAiAnalyzer() {
             <div className="flex items-center gap-2 px-4 py-2 rounded-xl bg-emerald-50 dark:bg-emerald-900/30 border border-emerald-200 dark:border-emerald-800 cursor-default">
                 <CheckCircle2 className="w-4 h-4 text-emerald-600 dark:text-emerald-500" />
                 <span className="text-xs font-bold text-emerald-700 dark:text-emerald-400">
-                    Analyse terminée ({progress.total} lots analysés)
+                    Analyse terminée ({progress.total} produits analysés)
                 </span>
             </div>
         );