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https://github.com/R0m1k3/CollectFlow.git
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feat(ai): synchronize batch and individual analysis payloads and models, optimize concurrency
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@@ -12,8 +12,8 @@ const BatchAnalyzeSchema = z.object({
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ventes: z.number().nullable().optional(),
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marge: z.number().nullable().optional(),
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score: z.number().nullable().optional(),
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gammeInit: z.string().nullable().optional(),
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historique: z.string().nullable().optional(),
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codeGamme: z.string().nullable().optional(),
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sales12m: z.record(z.string(), z.number()).nullable().optional(),
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nomenclature: z.string().nullable().optional(),
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})),
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});
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@@ -22,7 +22,7 @@ export async function POST(req: NextRequest) {
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// Read the API key from DB config (same pattern as /api/ai/analyze)
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const config = await getSavedDatabaseConfig();
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const apiKey = process.env.OPENROUTER_API_KEY || config?.openRouterKey;
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const model = config?.openRouterModel || "meta-llama/llama-3.3-70b-instruct:free";
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const model = config?.openRouterModel || "google/gemini-flash-1.5";
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if (!apiKey) {
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console.error("[batch-analyze] API key manquante.");
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@@ -39,20 +39,17 @@ export async function POST(req: NextRequest) {
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const { rayon, products } = parsed.data;
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// Optimized System Prompt: Merged from analyze/route.ts as requested by user
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// Optimized System Prompt: Perfectly aligned with analyze/route.ts
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const systemPrompt = `Tu es un expert en analyse de gammes de produits B2B pour un acheteur retail professionnel.
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En analysant les données de ventes fournies (CA, Marge, Volume, Historique) et surtout le SCORE de performance relative (0-100), génère pour chaque produit une recommandation de gamme (A=Permanent, C=Saisonnier, Z=Sortie).
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En analysant les données de ventes fournies (CA, Marge, Volume, Historique mensuel) et surtout le SCORE de performance relative (0-100), génère pour chaque produit une recommandation de gamme (A=Permanent, C=Saisonnier, Z=Sortie).
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CRITÈRES PRIORITAIRES :
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- SCORE < 20 : Recommandation "Z" (Sortie) quasi-obligatoire. Même si le volume semble correct, le produit est un boulet par rapport au reste du fournisseur.
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- A (Permanent) : Produit avec une rotation régulière ET un score satisfaisant (> 30-40).
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- C (Saisonnier) : Pics de ventes concentrés sur l'historique. Aide-toi de la nomenclature.
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- SCORE < 20 : Recommandation "Z" (Sortie) quasi-obligatoire. Un score faible signifie que le produit est un fardeau par rapport aux autres produits du fournisseur.
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- A (Permanent) : Produit avec une rotation régulière ET un score satisfaisant (> 35).
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- C (Saisonnier) : Pics de ventes concentrés sur l'historique (sales12m). Aide-toi de la nomenclature.
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- Z (Sortie) : Ventes nulles, rotation insuffisante ou score médiocre.
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DÉTECTION :
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Marque 'isDuplicate: true' si le produit semble être un doublon dans le lot.
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IMPORTANT : RÉPONDS UNIQUEMENT EN JSON VALIDE.
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Format:
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{
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@@ -66,9 +63,7 @@ Format:
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]
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}
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INTERDICTION FORMELLE : N'utilise jamais la phrase "Justification courte basée sur les données" comme réponse. Tu dois rédiger une analyse réelle.
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Données : codein, nom, ca (€), ventes (unités), marge (%), gammeInit, historique, nomenclature.`;
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Données fournies : codein, nom, ca (€), ventes (unités), marge (%), score (0-100), codeGamme (actuel), sales12m (historique par mois), nomenclature.`;
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const userPrompt = `Analyse cette liste de produits: \n${JSON.stringify(products, null, 2)} `;
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@@ -24,6 +24,7 @@ export function AiInsightBlock({ row }: AiInsightBlockProps) {
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totalQuantite: row.totalQuantite,
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sales12m: row.sales12m,
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codeGamme: row.codeGamme,
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score: row.score,
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});
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};
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@@ -17,6 +17,7 @@ export function BulkAiAnalyzer() {
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console.log("[BulkAiAnalyzer] Starting analysis on", rows.length, "rows");
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setIsAnalyzing(true);
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let completed = 0;
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try {
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// Group by Nomenclature (libelle3 or a fallback)
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@@ -27,39 +28,34 @@ export function BulkAiAnalyzer() {
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groups[rayon].push(r);
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});
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// Create chunks of up to 50 products per rayon
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const CHUNK_SIZE = 50;
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const chunks: { rayon: string; items: ProductRow[] }[] = [];
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for (const [rayon, items] of Object.entries(groups)) {
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for (let i = 0; i < items.length; i += CHUNK_SIZE) {
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chunks.push({
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rayon,
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items: items.slice(i, i + CHUNK_SIZE)
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});
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chunks.push({ rayon, items: items.slice(i, i + CHUNK_SIZE) });
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}
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}
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setProgress({ current: 0, total: chunks.length, message: "Initialisation...", errors: 0 });
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// Process sequentially
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let completed = 0;
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for (const chunk of chunks) {
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setProgress(prev => ({ ...prev, current: completed, total: chunks.length, message: `Analyse du rayon: ${chunk.rayon}` }));
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// Process with a continuous concurrency limit
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const CONCURRENCY = 3;
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const remainingChunks = [...chunks];
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const processNext = async () => {
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const chunk = remainingChunks.shift();
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if (!chunk) return;
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// Enrich payload with more context to improve AI decision quality
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const payloadProducts = chunk.items.map(r => ({
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codein: r.codein,
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nom: r.libelle1,
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ca: r.totalCa || 0,
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ventes: r.totalQuantite || 0,
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marge: r.totalMarge ? parseFloat(((r.totalMarge / (r.totalCa || 1)) * 100).toFixed(1)) : 0,
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gammeInit: r.codeGammeInit || "N/A",
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// Detailed history: "YYYYMM: Qty, YYYYMM: Qty..." to help AI see seasonality
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historique: Object.entries(r.sales12m || {})
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.map(([month, qty]) => `${month}:${qty}`)
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.join(", "),
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// Contextual nomenclature
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score: r.score || 0,
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codeGamme: r.codeGamme || "N/A",
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sales12m: r.sales12m || {}, // Send raw object for better AI analysis
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nomenclature: `${r.libelleNiveau1 || ""} > ${r.libelleNiveau2 || ""} > ${r.libelle3 || ""}`
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}));
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@@ -74,66 +70,58 @@ export function BulkAiAnalyzer() {
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body: JSON.stringify({ rayon: chunk.rayon, products: payloadProducts })
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});
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if (res.status === 429) {
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if (res.status === 429 && attempt < MAX_CHUNK_RETRIES) {
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const errData = await res.json().catch(() => ({}));
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const waitSecs = errData.retryAfter ?? (30 * attempt);
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console.warn(`[BulkAiAnalyzer] Rate limited. Waiting ${waitSecs}s before retry ${attempt}/${MAX_CHUNK_RETRIES}`);
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for (let t = waitSecs; t > 0; t--) {
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setProgress(prev => ({ ...prev, message: `⏳ Lot ${completed + 1}/${chunks.length} — Rate limit, reprise dans ${t}s...` }));
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setProgress(prev => ({ ...prev, message: `⏳ Lot ${completed + 1}/${chunks.length} — Limite API, reprise dans ${t}s...` }));
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await new Promise(r => setTimeout(r, 1000));
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}
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continue; // retry
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continue;
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}
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break; // success or non-retryable error
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break;
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}
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if (!res || !res.ok) {
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const errText = await res?.text().catch(() => "");
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console.error(`[BulkAiAnalyzer] Erreur HTTP ${res?.status} sur le lot ${chunk.rayon}:`, errText);
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console.error(`[BulkAiAnalyzer] Erreur HTTP sur le lot ${chunk.rayon}:`, errText);
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setProgress(prev => ({ ...prev, errors: prev.errors + 1 }));
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} else {
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const data = await res.json();
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console.log(`[BulkAiAnalyzer] Response for ${chunk.rayon}:`, data);
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if (data && Array.isArray(data.results)) {
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let applied = 0;
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data.results.forEach((reco: any) => {
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if (reco.codein && reco.recommandationGamme) {
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setDraftGamme(reco.codein, reco.recommandationGamme);
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const baseJustification = reco.justificationCourte || "Aucune explication.";
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const justification = `Gamme ${reco.recommandationGamme} — ${baseJustification}`;
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const justification = `Gamme ${reco.recommandationGamme} — ${reco.justificationCourte || "Analyse effectuée."}`;
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setInsight(reco.codein, justification, reco.isDuplicate ?? false);
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applied++;
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}
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});
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console.log(`[BulkAiAnalyzer] Applied ${applied} recommendations for ${chunk.rayon}`);
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} else {
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console.warn(`[BulkAiAnalyzer] Unexpected response shape for ${chunk.rayon}:`, data);
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}
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}
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} catch (e) {
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console.error(`[BulkAiAnalyzer] Failed fetch for chunk ${chunk.rayon}:`, e);
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setProgress(prev => ({ ...prev, errors: prev.errors + 1 }));
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} finally {
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completed++;
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setProgress(prev => ({
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...prev,
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current: completed,
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message: completed === chunks.length ? "Analyse terminée" : `Analyse: ${completed}/${chunks.length} lots`
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}));
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// Process next chunk in the same worker
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await processNext();
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}
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};
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completed++;
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// Always wait 20s between chunks — free tier allows ~3 req/min
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if (completed < chunks.length) {
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setProgress(prev => ({ ...prev, current: completed, message: `En attente... (lot ${completed}/${chunks.length})` }));
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await new Promise(resolve => setTimeout(resolve, 20000));
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}
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}
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// Start workers
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const workers = Array.from({ length: Math.min(CONCURRENCY, chunks.length) }, () => processNext());
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await Promise.all(workers);
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setProgress(prev => ({ ...prev, current: completed, total: chunks.length, message: `Analyse terminée ! (${prev.errors > 0 ? prev.errors + " erreurs" : "succès"})` }));
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setTimeout(() => {
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setIsAnalyzing(false);
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}, 3000);
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setTimeout(() => setIsAnalyzing(false), 3000);
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} catch (error) {
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console.error("Erreur globale lors de l'analyse:", error);
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alert("Une erreur inattendue s'est produite pendant l'analyse globale.");
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setIsAnalyzing(false);
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}
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};
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