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https://github.com/R0m1k3/CollectFlow.git
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feat: Introduce AI Copilot for bulk product analysis, including a dedicated store, UI components, and business logic.
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@@ -4,38 +4,37 @@ import React, { useState, useRef } from "react";
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import { useGridStore } from "@/features/grid/store/use-grid-store";
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import { useAiCopilotStore } from "@/features/ai-copilot/store/use-ai-copilot-store";
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import { Sparkles, Loader2, CheckCircle2, XCircle } from "lucide-react";
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import { ProductAnalysisInput, AnalysisResult } from "@/features/ai-copilot/models/ai-analysis.types";
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import { ProductRow, GammeCode } from "@/types/grid";
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import { ProductAnalysisInput } from "@/features/ai-copilot/models/ai-analysis.types";
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export function BulkAiAnalyzer() {
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const [isAnalyzing, setIsAnalyzing] = useState(false);
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const [progress, setProgress] = useState({ current: 0, total: 0, message: "", errors: 0 });
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const isCancelledRef = useRef(false);
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const setDraftGamme = useGridStore(state => state.setDraftGamme);
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const setInsight = useAiCopilotStore(state => state.setInsight);
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const setDraftGamme = useGridStore((s) => s.setDraftGamme);
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const { setInsight, setLoading, setError } = useAiCopilotStore();
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const handleStop = () => {
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isCancelledRef.current = true;
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setProgress(prev => ({ ...prev, message: "Arrêt en cours..." }));
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setProgress((prev) => ({ ...prev, message: "Arrêt en cours..." }));
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};
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const handleAnalyze = async () => {
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const { rows } = useGridStore.getState();
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isCancelledRef.current = false;
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const totalQtySum = rows.reduce((sum: number, r: any) => sum + (r.totalQuantite || 0), 0);
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const totalQtySum = rows.reduce((sum: number, r: ProductRow) => sum + (r.totalQuantite || 0), 0);
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const avgQty = rows.length > 0 ? totalQtySum / rows.length : 0;
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// Benchmarks par groupe de magasins
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const rowsGroup1 = rows.filter(r => (r.workingStores?.length || 1) === 1);
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const rowsGroup2 = rows.filter(r => (r.workingStores?.length || 1) >= 2);
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const rowsGroup1 = rows.filter((r) => (r.workingStores?.length || 1) === 1);
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const rowsGroup2 = rows.filter((r) => (r.workingStores?.length || 1) >= 2);
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const avgQty1 = rowsGroup1.length > 0 ? rowsGroup1.reduce((s: number, r: any) => s + (r.totalQuantite || 0), 0) / rowsGroup1.length : 0;
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const avgQty2 = rowsGroup2.length > 0 ? rowsGroup2.reduce((s: number, r: any) => s + (r.totalQuantite || 0), 0) / rowsGroup2.length : 0;
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const avgQty1 = rowsGroup1.length > 0 ? rowsGroup1.reduce((s: number, r: ProductRow) => s + (r.totalQuantite || 0), 0) / rowsGroup1.length : 0;
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const avgQty2 = rowsGroup2.length > 0 ? rowsGroup2.reduce((s: number, r: ProductRow) => s + (r.totalQuantite || 0), 0) / rowsGroup2.length : 0;
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// Benchmarks par Rayon (Nomenclature Niveau 2)
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const rayonStats = new Map<string, { qty1: number[], qty2: number[] }>();
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rows.forEach((r: any) => {
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const rayonStats = new Map<string, { qty1: number[]; qty2: number[] }>();
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rows.forEach((r: ProductRow) => {
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const rayon = r.libelleNiveau2 || "Général";
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if (!rayonStats.has(rayon)) rayonStats.set(rayon, { qty1: [], qty2: [] });
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const stats = rayonStats.get(rayon)!;
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@@ -44,23 +43,41 @@ export function BulkAiAnalyzer() {
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else stats.qty2.push(r.totalQuantite || 0);
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});
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const rayonBenchmarks = new Map<string, { avg1: number, avg2: number }>();
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const rayonBenchmarks = new Map<string, { avg1: number; avg2: number }>();
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rayonStats.forEach((stats, rayon) => {
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const totalCount = stats.qty1.length + stats.qty2.length;
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// Seuil de 3 produits pour justifier un benchmark spécifique par rayon
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if (totalCount >= 3) {
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rayonBenchmarks.set(rayon, {
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avg1: stats.qty1.length > 0 ? stats.qty1.reduce((a, b) => a + b, 0) / stats.qty1.length : 0,
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avg2: stats.qty2.length > 0 ? stats.qty2.reduce((a, b) => a + b, 0) / stats.qty2.length : 0
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avg2: stats.qty2.length > 0 ? stats.qty2.reduce((a, b) => a + b, 0) / stats.qty2.length : 0,
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});
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}
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});
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const productPayloads: ProductAnalysisInput[] = rows.map(r => {
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const productPayloads: ProductAnalysisInput[] = rows.map((r) => {
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const sc = r.workingStores?.length || 1;
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const weight = sc === 1 ? 2 : 1; // On ramène tout sur une base 2 magasins
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const weight = sc === 1 ? 2 : 1;
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const rb = rayonBenchmarks.get(r.libelleNiveau2 || "Général");
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const allMonths = Object.keys(r.sales12m || {});
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const referenceMonth = allMonths.length > 0 ? Math.max(...allMonths.map((m) => parseInt(m))).toString() : "";
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const salesMonths = Object.entries(r.sales12m || {})
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.filter(([_, qty]) => qty > 0)
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.map(([m]) => parseInt(m))
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.sort((a, b) => b - a);
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const lastMonth = salesMonths.length > 0 ? salesMonths[0].toString() : "";
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let inactivity = 0;
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if (referenceMonth && lastMonth) {
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const refY = parseInt(referenceMonth.substring(0, 4));
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const refM = parseInt(referenceMonth.substring(4, 6));
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const lastY = parseInt(lastMonth.substring(0, 4));
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const lastM = parseInt(lastMonth.substring(4, 6));
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inactivity = (refY - lastY) * 12 + (refM - lastM);
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}
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const regScore = Object.values(r.sales12m || {}).filter((v) => v > 0).length;
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return {
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codein: r.codein,
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libelle1: r.libelle1 || "",
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@@ -76,124 +93,92 @@ export function BulkAiAnalyzer() {
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avgQtyRayon1: rb ? rb.avg1 : avgQty1,
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avgQtyRayon2: rb ? rb.avg2 : avgQty2,
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storeCount: sc,
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sales12m: Object.fromEntries(
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Object.entries(r.sales12m || {}).map(([month, val]) => [month, val * weight])
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),
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sales12m: Object.fromEntries(Object.entries(r.sales12m || {}).map(([month, val]) => [month, val * weight])),
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codeGamme: r.codeGamme || null,
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score: r.score || 0,
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regularityScore: Object.values(r.sales12m || {}).filter(v => v > 0).length,
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projectedTotalQuantite: (Object.values(r.sales12m || {}).filter(v => v > 0).length > 0)
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? ((r.totalQuantite || 0) * weight * (12 / Object.values(r.sales12m || {}).filter(v => v > 0).length))
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: ((r.totalQuantite || 0) * weight),
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projectedTotalCa: (Object.values(r.sales12m || {}).filter(v => v > 0).length > 0)
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? ((r.totalCa || 0) * weight * (12 / Object.values(r.sales12m || {}).filter(v => v > 0).length))
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: ((r.totalCa || 0) * weight),
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regularityScore: regScore,
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projectedTotalQuantite: regScore > 0 ? (r.totalQuantite || 0) * weight * (12 / regScore) : (r.totalQuantite || 0) * weight,
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projectedTotalCa: regScore > 0 ? (r.totalCa || 0) * weight * (12 / regScore) : (r.totalCa || 0) * weight,
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lastMonthWithSale: lastMonth,
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inactivityMonths: inactivity,
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};
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});
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setIsAnalyzing(true);
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let completed = 0;
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let errors = 0;
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let errorsCount = 0;
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setProgress({ current: 0, total: productPayloads.length, message: "Initialisation...", errors: 0 });
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const totalItems = productPayloads.length;
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setProgress({ current: 0, total: totalItems, message: "Initialisation...", errors: 0 });
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try {
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const CONCURRENCY = 3; // Réduit pour éviter les 429 trop fréquents
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const remaining = [...productPayloads];
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const CONCURRENCY = 3;
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const remaining = [...productPayloads];
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const processNext = async () => {
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if (isCancelledRef.current) return;
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const processBatch = async () => {
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const workers = Array.from({ length: Math.min(CONCURRENCY, remaining.length) }, async () => {
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while (remaining.length > 0 && !isCancelledRef.current) {
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const payload = remaining.shift();
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if (!payload) break;
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const payload = remaining.shift();
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if (!payload) return;
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setLoading(payload.codein);
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setDraftGamme(payload.codein, "Aucune");
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// Marquer temporairement comme loading dans le store pour le feedback visuel individuel
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setInsight(payload.codein, "", false);
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// Vider aussi la recommandation (A, C, Z) pour montrer que c'est en cours de recalcul
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setDraftGamme(payload.codein, "Aucune");
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// Note: setInsight dans le store actuel met le status à "done".
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// Pour bien faire, il faudrait une action setStatusLoading dans le store.
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// Ici, on va au moins vider l'insight pour montrer que ça bouge.
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try {
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let res: Response | null = null;
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const MAX_RETRIES = 2;
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for (let attempt = 1; attempt <= MAX_RETRIES; attempt++) {
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if (isCancelledRef.current) break;
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res = await fetch("/api/ai/analyze", {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify(payload)
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});
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if (res.status === 429 && attempt < MAX_RETRIES) {
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const errData = await res.json().catch(() => ({}));
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const waitSecs = errData.retryAfter ?? (15 * attempt);
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for (let t = waitSecs; t > 0; t--) {
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if (isCancelledRef.current) break;
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setProgress(prev => ({ ...prev, message: `⏳ ${completed}/${productPayloads.length} — Limite API, reprise dans ${t}s...` }));
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await new Promise(r => setTimeout(r, 1000));
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try {
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let res: Response | null = null;
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for (let i = 0; i < 3; i++) {
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if (isCancelledRef.current) break;
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try {
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res = await fetch("/api/ai/analyze", {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify(payload),
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});
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if (res.ok) break;
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if (res.status === 429) {
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await new Promise((resolve) => setTimeout(resolve, 2000 * (i + 1)));
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}
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} catch (e) {
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console.error(`Retry ${i} failed for ${payload.codein}`, e);
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}
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continue;
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}
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break;
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}
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if (isCancelledRef.current) return;
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if (!res || !res.ok) {
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errors++;
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} else {
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const data: AnalysisResult = await res.json();
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// On met à jour l'insight dans tous les cas pour qu'il s'affiche
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setInsight(data.codein, data.insight, false);
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// Si une recommandation est trouvée, on l'applique à la gamme
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if (data.recommandation) {
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setDraftGamme(data.codein, data.recommandation);
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if (!res || !res.ok) {
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setError(payload.codein, "Erreur API");
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errorsCount++;
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} else {
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const data = await res.json();
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setInsight(payload.codein, data.insight, data.isDuplicate);
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if (data.recommandation) {
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setDraftGamme(payload.codein, data.recommandation as GammeCode);
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}
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}
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}
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} catch {
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errors++;
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} finally {
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if (!isCancelledRef.current) {
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} catch (err) {
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console.error(`Error processing ${payload.codein}`, err);
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setError(payload.codein, "Erreur");
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errorsCount++;
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} finally {
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completed++;
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setProgress({
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setProgress((prev) => ({
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...prev,
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current: completed,
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total: productPayloads.length,
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message: completed === productPayloads.length
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? "Analyse terminée"
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: `Analyse: ${completed}/${productPayloads.length} produits`,
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errors,
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});
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await processNext();
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message: `Analyse: ${completed}/${totalItems}`,
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errors: errorsCount,
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}));
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}
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}
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};
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});
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const workers = Array.from(
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{ length: Math.min(CONCURRENCY, productPayloads.length) },
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() => processNext()
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);
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await Promise.all(workers);
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};
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setProgress(prev => ({
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...prev,
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current: completed,
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total: productPayloads.length,
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message: isCancelledRef.current
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? `Analyse interrompue (${completed} traités)`
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: `Analyse terminée ! (${errors > 0 ? errors + " erreurs" : "succès"})`,
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}));
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await processBatch();
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setTimeout(() => setIsAnalyzing(false), 3000);
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setProgress((prev) => ({
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...prev,
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message: isCancelledRef.current ? `Analyse interrompue (${completed}/${totalItems})` : `Analyse terminée ! (${errorsCount > 0 ? errorsCount + " erreurs" : "succès"})`,
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}));
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} catch (error) {
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console.error("Erreur globale lors de l'analyse:", error);
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setIsAnalyzing(false);
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}
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setTimeout(() => setIsAnalyzing(false), 3000);
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};
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if (isAnalyzing) {
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@@ -227,10 +212,7 @@ export function BulkAiAnalyzer() {
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<div className="flex items-center gap-2 h-10 px-4 rounded-xl bg-[var(--accent-success-bg)] border border-[var(--accent-success)] shadow-sm cursor-default">
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<CheckCircle2 className="w-4 h-4 text-[var(--accent-success)]" />
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<span className="text-xs font-bold text-[var(--accent-success)]">
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{progress.errors > 0
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? `Terminé (${progress.current - progress.errors}/${progress.total})`
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: `Analyse Terminée`
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}
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{progress.errors > 0 ? `Terminé (${progress.current - progress.errors}/${progress.total})` : `Analyse Terminée`}
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</span>
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</div>
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);
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