f7af5b933d
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Refactors PredictionsChart to fetch all prediction data in a single batch for instant client-side switching between horizons and simulation factors. Updates state management and effects to utilize the pre-cached batch data, reducing API calls and improving responsiveness. Minor UI text update to remove TensorFlow.js mention.
841 lines
34 KiB
TypeScript
841 lines
34 KiB
TypeScript
"use client";
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import { useState, useEffect, memo, useMemo, useCallback } from "react";
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import {
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Card,
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CardContent,
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CardDescription,
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CardHeader,
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CardTitle,
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} from "@/components/ui/card";
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import { Badge } from "@/components/ui/badge";
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import { Button } from "@/components/ui/button";
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import {
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Select,
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SelectContent,
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SelectItem,
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SelectTrigger,
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SelectValue,
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} from "@/components/ui/select";
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import {
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TrendingUp,
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TrendingDown,
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Package,
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DollarSign,
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AlertTriangle,
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RefreshCw,
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Calendar,
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BarChart3,
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Brain,
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Layers,
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Zap,
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Info,
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Download,
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} from "lucide-react";
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import { useToast } from "@/hooks/use-toast";
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import { Skeleton } from "@/components/ui/skeleton";
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import CountUp from "react-countup";
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import {
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getPredictionsOverviewWithStore,
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getStockPredictionsWithStore,
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type PredictionsOverview,
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type StockPredictionsResponse,
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} from "@/lib/services/analytics-service";
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import { formatGBP } from "@/utils/format";
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import {
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Table,
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TableBody,
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TableCell,
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TableHead,
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TableHeader,
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TableRow,
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} from "@/components/ui/table";
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import { format } from "date-fns";
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import {
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AreaChart,
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Area,
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XAxis,
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YAxis,
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CartesianGrid,
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Tooltip as RechartsTooltip,
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ResponsiveContainer,
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} from "recharts";
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import {
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Tooltip,
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TooltipContent,
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TooltipProvider,
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TooltipTrigger,
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} from "@/components/ui/tooltip";
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import { Alert, AlertDescription, AlertTitle } from "@/components/ui/alert";
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import { Slider } from "@/components/ui/slider";
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interface PredictionsChartProps {
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timeRange?: number;
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}
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export default function PredictionsChart({
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timeRange = 90,
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}: PredictionsChartProps) {
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const [predictions, setPredictions] = useState<PredictionsOverview | null>(
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null,
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);
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const [baselinePredictions, setBaselinePredictions] = useState<PredictionsOverview | null>(
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null,
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);
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// Batch data holds all pre-cached predictions for instant switching
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const [batchData, setBatchData] = useState<{
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[horizon: string]: {
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[simulationFactor: string]: PredictionsOverview;
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};
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} | null>(null);
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const [stockPredictions, setStockPredictions] =
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useState<StockPredictionsResponse | null>(null);
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const [loading, setLoading] = useState(true);
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const [isSimulating, setIsSimulating] = useState(false);
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const [daysAhead, setDaysAhead] = useState(7);
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const [activeTab, setActiveTab] = useState<"overview" | "stock">("overview");
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const [simulationFactor, setSimulationFactor] = useState(0);
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const [committedSimulationFactor, setCommittedSimulationFactor] = useState(0);
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const { toast } = useToast();
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// Fetch all predictions in batch (for instant client-side switching)
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const fetchBatchData = async () => {
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try {
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setLoading(true);
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const { getBatchPredictionsWithStore } = await import("@/lib/services/analytics-service");
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const [batchResponse, stock] = await Promise.all([
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getBatchPredictionsWithStore(timeRange),
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getStockPredictionsWithStore(timeRange),
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]);
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if (batchResponse.success && batchResponse.predictions) {
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setBatchData(batchResponse.predictions);
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// Set initial predictions from batch
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const horizonData = batchResponse.predictions[daysAhead.toString()];
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if (horizonData) {
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const baseline = horizonData["0"];
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if (baseline) {
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setBaselinePredictions(baseline);
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setPredictions(baseline);
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}
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}
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} else {
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// Fallback to single request if batch not available
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const overview = await getPredictionsOverviewWithStore(daysAhead, timeRange, 0);
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setBaselinePredictions(overview);
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setPredictions(overview);
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}
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setStockPredictions(stock);
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} catch (error) {
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console.error("Error fetching predictions:", error);
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toast({
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title: "Error",
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description: "Failed to load predictions",
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variant: "destructive",
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});
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} finally {
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setLoading(false);
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}
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};
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// Switch predictions from batch data (no API call!)
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const switchPredictions = useCallback((horizon: number, simFactor: number) => {
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if (!batchData) return;
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const horizonData = batchData[horizon.toString()];
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if (!horizonData) return;
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// Simulation factor is stored as decimal (e.g., 0.1 for 10%)
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const simKey = (simFactor / 100).toString();
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const newPrediction = horizonData[simKey];
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if (newPrediction) {
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setPredictions(newPrediction);
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if (simFactor === 0) {
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setBaselinePredictions(newPrediction);
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}
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}
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}, [batchData]);
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// Fetch batch data on initial load or when timeRange changes
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useEffect(() => {
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fetchBatchData();
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setCommittedSimulationFactor(0);
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setSimulationFactor(0);
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}, [timeRange]);
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// Switch predictions when daysAhead changes (instant, from batch)
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useEffect(() => {
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if (batchData) {
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switchPredictions(daysAhead, committedSimulationFactor);
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}
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}, [daysAhead, batchData, switchPredictions]);
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// Switch predictions when simulation factor changes (instant, from batch)
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useEffect(() => {
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if (batchData) {
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switchPredictions(daysAhead, committedSimulationFactor);
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}
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}, [committedSimulationFactor, batchData, switchPredictions]);
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const getConfidenceColor = (confidence: string) => {
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switch (confidence) {
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case "very_high":
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return "bg-emerald-600/20 text-emerald-700 dark:text-emerald-400 border-emerald-600/30";
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case "high":
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return "bg-green-500/10 text-green-700 dark:text-green-400";
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case "medium":
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return "bg-yellow-500/10 text-yellow-700 dark:text-yellow-400";
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case "low":
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return "bg-red-500/10 text-red-700 dark:text-red-400";
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default:
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return "bg-gray-500/10 text-gray-700 dark:text-gray-400";
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}
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};
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const getConfidenceLabel = (confidence: string) => {
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switch (confidence) {
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case "very_high":
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return "Very High";
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case "high":
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return "High";
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case "medium":
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return "Medium";
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case "low":
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return "Low";
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default:
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return confidence;
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}
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};
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// Combine baseline and simulated data for overlay chart
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const chartData = useMemo(() => {
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if (!predictions?.sales?.dailyPredictions) return [];
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const baselineData = baselinePredictions?.sales?.dailyPredictions || [];
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const simulatedDailyData = predictions.sales.dailyPredictions;
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return simulatedDailyData.map((d: any, idx: number) => ({
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...d,
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formattedDate: format(new Date(d.date), "MMM d"),
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simulated: d.predicted,
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baseline: baselineData[idx]?.predicted ?? d.predicted,
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orders: d.predictedOrders || 0,
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}));
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}, [predictions, baselinePredictions]);
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// Keep simulatedData for export compatibility
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const simulatedData = chartData;
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const handleExportCSV = () => {
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if (!simulatedData.length) return;
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// Create CSV headers
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const headers = ["Date", "Baseline Revenue", "Simulated Revenue", "Orders", "Confidence"];
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// Create CSV rows
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const rows = simulatedData.map(d => [
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format(new Date(d.date), "yyyy-MM-dd"),
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d.baseline?.toFixed(2) || "",
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d.simulated?.toFixed(2) || "",
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d.orders || "",
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predictions?.sales?.confidence || "unknown"
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]);
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// Combine headers and rows
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const csvContent = [
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headers.join(","),
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...rows.map(row => row.join(","))
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].join("\n");
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// Create download link
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const blob = new Blob([csvContent], { type: "text/csv;charset=utf-8;" });
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const url = URL.createObjectURL(blob);
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const link = document.createElement("a");
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link.setAttribute("href", url);
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link.setAttribute("download", `predictions_export_${format(new Date(), "yyyyMMdd")}.csv`);
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link.style.visibility = "hidden";
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document.body.appendChild(link);
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link.click();
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document.body.removeChild(link);
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};
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if (loading) {
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return (
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<Card>
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<CardHeader>
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<Skeleton className="h-6 w-48" />
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<Skeleton className="h-4 w-72" />
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</CardHeader>
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<CardContent>
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<div className="space-y-4">
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<Skeleton className="h-32 w-full" />
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<Skeleton className="h-32 w-full" />
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<Skeleton className="h-32 w-full" />
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</div>
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</CardContent>
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</Card>
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);
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}
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if (!predictions) {
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return (
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<Card>
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<CardHeader>
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<CardTitle>Predictions</CardTitle>
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<CardDescription>
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Forecast future sales, demand, and stock levels
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</CardDescription>
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</CardHeader>
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<CardContent>
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<p className="text-muted-foreground text-center py-8">
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No prediction data available. Need more historical data.
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</p>
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</CardContent>
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</Card>
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);
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}
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return (
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<Card>
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<CardHeader>
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<div className="flex items-center justify-between">
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<div>
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<CardTitle className="flex items-center gap-2">
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<BarChart3 className="h-5 w-5" />
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Predictions & Forecasting
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</CardTitle>
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<CardDescription>
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{predictions.sales.aiModel?.used
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? "AI neural network + statistical models for sales, demand, and inventory"
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: "AI-powered predictions for sales, demand, and inventory"}
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</CardDescription>
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</div>
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<div className="flex items-center gap-2">
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<Select
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value={daysAhead.toString()}
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onValueChange={(value) => setDaysAhead(parseInt(value))}
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>
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<SelectTrigger className="w-32">
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<SelectValue />
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</SelectTrigger>
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<SelectContent>
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<SelectItem value="7">7 days</SelectItem>
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<SelectItem value="14">14 days</SelectItem>
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<SelectItem value="30">30 days</SelectItem>
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<SelectItem value="60">60 days</SelectItem>
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<SelectItem value="90">90 days</SelectItem>
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</SelectContent>
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</Select>
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<Button
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variant="outline"
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size="icon"
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onClick={fetchBatchData}
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disabled={loading || isSimulating}
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>
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<RefreshCw
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className={`h-4 w-4 ${loading || isSimulating ? "animate-spin" : ""}`}
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/>
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</Button>
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</div>
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</div>
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</CardHeader>
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<CardContent>
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<div className="flex gap-2 mb-6">
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<Button
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variant={activeTab === "overview" ? "default" : "outline"}
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onClick={() => setActiveTab("overview")}
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size="sm"
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>
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Overview
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</Button>
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<Button
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variant={activeTab === "stock" ? "default" : "outline"}
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onClick={() => setActiveTab("stock")}
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size="sm"
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>
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Stock Predictions
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</Button>
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</div>
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{activeTab === "overview" && (
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<div className="space-y-6">
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{/* Sales Predictions */}
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<div className="grid grid-cols-1 md:grid-cols-2 gap-4">
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<TooltipProvider delayDuration={0}>
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<Card>
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<CardHeader className="pb-3">
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<CardTitle className="text-sm font-medium flex items-center gap-2">
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<DollarSign className="h-4 w-4" />
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Revenue Prediction
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</CardTitle>
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</CardHeader>
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<CardContent>
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{predictions.sales.predicted !== null ? (
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<div className="space-y-2">
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<Tooltip>
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<TooltipTrigger asChild>
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<div className="text-2xl font-bold w-fit cursor-help">
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<CountUp
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end={predictions.sales.predicted}
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duration={1.5}
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separator=","
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decimals={2}
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prefix="£"
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/>
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</div>
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</TooltipTrigger>
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<TooltipContent>
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<p>Predicted daily average revenue for the next {daysAhead} days</p>
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</TooltipContent>
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</Tooltip>
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<div className="flex items-center gap-2 flex-wrap">
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<Tooltip>
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<TooltipTrigger asChild>
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<span className="inline-flex cursor-help">
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<Badge
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className={getConfidenceColor(
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predictions.sales.confidence,
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)}
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>
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{getConfidenceLabel(predictions.sales.confidence)} Confidence
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{predictions.sales.confidenceScore !== undefined && (
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<span className="ml-1 opacity-75">
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({Math.round(predictions.sales.confidenceScore * 100)}%)
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</span>
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)}
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</Badge>
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</span>
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</TooltipTrigger>
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<TooltipContent>
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<p>Based on data consistency, historical accuracy, and model agreement</p>
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</TooltipContent>
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</Tooltip>
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{predictions.sales.aiModel?.used && (
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<Tooltip>
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<TooltipTrigger asChild>
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<span className="inline-flex cursor-help">
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<Badge variant="outline" className="bg-purple-500/10 text-purple-700 dark:text-purple-400 border-purple-500/30">
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🤖 AI Powered
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{predictions.sales.aiModel.modelAccuracy !== undefined && (
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<span className="ml-1 opacity-75">
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({Math.round(predictions.sales.aiModel.modelAccuracy * 100)}%)
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</span>
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)}
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</Badge>
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</span>
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</TooltipTrigger>
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<TooltipContent>
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<p>Predictions generated using a Deep Learning Ensemble Model</p>
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</TooltipContent>
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</Tooltip>
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)}
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{predictions.sales.trend && (
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<Tooltip>
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<TooltipTrigger asChild>
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<span className="inline-flex cursor-help">
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<Badge
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variant="outline"
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className={
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predictions.sales.trend.direction === "up"
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? "text-green-600 border-green-600"
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: predictions.sales.trend.direction === "down"
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? "text-red-600 border-red-600"
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: ""
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}
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>
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{predictions.sales.trend.direction === "up" && (
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<TrendingUp className="h-3 w-3 mr-1" />
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)}
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{predictions.sales.trend.direction === "down" && (
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<TrendingDown className="h-3 w-3 mr-1" />
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)}
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{predictions.sales.trend.direction === "up"
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? "Trending Up"
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: predictions.sales.trend.direction === "down"
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? "Trending Down"
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: "Stable"}
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</Badge>
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</span>
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</TooltipTrigger>
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<TooltipContent>
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<p>Direction of the recent sales trend (slope analysis)</p>
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</TooltipContent>
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</Tooltip>
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)}
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<span className="text-xs text-muted-foreground">
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Next {daysAhead} days
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</span>
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</div>
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{predictions.sales.predictedOrders && (
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<div className="text-sm text-muted-foreground">
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~{Math.round(predictions.sales.predictedOrders)}{" "}
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orders
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</div>
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)}
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{!predictions.sales.confidenceIntervals &&
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predictions.sales.minPrediction &&
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predictions.sales.maxPrediction && (
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<div className="text-xs text-muted-foreground">
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Range: {formatGBP(predictions.sales.minPrediction)} -{" "}
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{formatGBP(predictions.sales.maxPrediction)}
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</div>
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)}
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</div>
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) : (
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<div className="text-sm text-muted-foreground">
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{predictions.sales.message ||
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"Insufficient data for prediction"}
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</div>
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)}
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</CardContent>
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</Card>
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|
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{/* Model Intelligence Card */}
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|
<Card>
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<CardHeader className="pb-3">
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<CardTitle className="text-sm font-medium flex items-center gap-2">
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<Brain className="h-4 w-4" />
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Model Intelligence
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<Tooltip>
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<TooltipTrigger>
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<Info className="h-3 w-3 text-muted-foreground cursor-help" />
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</TooltipTrigger>
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<TooltipContent>
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<p>Technical details about the active prediction model</p>
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</TooltipContent>
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</Tooltip>
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</CardTitle>
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</CardHeader>
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<CardContent>
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<div className="space-y-4">
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<div className="flex justify-between items-center">
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<span className="text-sm text-muted-foreground">Architecture</span>
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<Tooltip>
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<TooltipTrigger asChild>
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<span className="text-sm font-medium flex items-center gap-1 cursor-help">
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<Layers className="h-3 w-3 text-purple-500" />
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Hybrid Ensemble (Deep Learning)
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</span>
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</TooltipTrigger>
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<TooltipContent>
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<p>Combines LSTM Neural Networks with Statistical Methods (Holt-Winters, ARIMA)</p>
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</TooltipContent>
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</Tooltip>
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</div>
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{stockPredictions?.predictions && (
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<div className="flex justify-between items-center">
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<span className="text-sm text-muted-foreground">Features</span>
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<span className="text-xs font-medium bg-secondary px-2 py-1 rounded-md">
|
|
Multi-Feature Enabled
|
|
</span>
|
|
</div>
|
|
)}
|
|
<div className="flex justify-between items-center">
|
|
<span className="text-sm text-muted-foreground">Optimization</span>
|
|
<span className="text-sm font-medium flex items-center gap-1">
|
|
<Zap className="h-3 w-3 text-amber-500" />
|
|
Performance Tuned
|
|
</span>
|
|
</div>
|
|
<div className="pt-2 border-t text-xs text-muted-foreground">
|
|
Model automatically retrains with new sales data.
|
|
</div>
|
|
</div>
|
|
</CardContent>
|
|
</Card>
|
|
</TooltipProvider>
|
|
</div>
|
|
|
|
<Alert className="bg-yellow-500/10 border-yellow-500/30 text-yellow-700 dark:text-yellow-400">
|
|
<AlertTriangle className="h-4 w-4 stroke-yellow-700 dark:stroke-yellow-400" />
|
|
<AlertTitle>Prediction Accuracy Warning</AlertTitle>
|
|
<AlertDescription>
|
|
These predictions are estimates based on historical sales data. Actual results may vary due to external factors, market conditions, and unforeseen events. Use these insights as a guide, not a guarantee.
|
|
</AlertDescription>
|
|
</Alert>
|
|
|
|
{/* Daily Predictions Chart */}
|
|
{predictions.sales.dailyPredictions &&
|
|
predictions.sales.dailyPredictions.length > 0 && (
|
|
<Card>
|
|
<CardHeader className="pb-3 flex flex-row items-center justify-between space-y-0">
|
|
<CardTitle className="text-sm font-medium">
|
|
Daily Revenue Forecast
|
|
</CardTitle>
|
|
<div className="flex items-center gap-4">
|
|
<div className="flex flex-col items-end">
|
|
<span className="text-xs font-medium text-muted-foreground">
|
|
Simulate Traffic:{" "}
|
|
<span className={simulationFactor > 0 ? "text-green-600" : simulationFactor < 0 ? "text-red-600" : ""}>
|
|
{simulationFactor > 0 ? "+" : ""}
|
|
{simulationFactor}%
|
|
</span>
|
|
</span>
|
|
<Slider
|
|
value={[simulationFactor]}
|
|
min={-50}
|
|
max={50}
|
|
step={10}
|
|
onValueChange={(val) => setSimulationFactor(val[0])}
|
|
onValueCommit={(val) => setCommittedSimulationFactor(val[0])}
|
|
className="w-[150px] mt-1.5"
|
|
/>
|
|
</div>
|
|
{simulationFactor !== 0 && (
|
|
<Button
|
|
variant="ghost"
|
|
size="icon"
|
|
className="h-6 w-6"
|
|
onClick={() => {
|
|
setSimulationFactor(0);
|
|
setCommittedSimulationFactor(0);
|
|
}}
|
|
title="Reset simulation"
|
|
>
|
|
<RefreshCw className="h-3 w-3" />
|
|
</Button>
|
|
)}
|
|
</div>
|
|
<Button variant="outline" size="sm" onClick={handleExportCSV} title="Export to CSV">
|
|
<Download className="mr-2 h-4 w-4" />
|
|
Export
|
|
</Button>
|
|
</CardHeader>
|
|
<CardContent>
|
|
<div className="h-[300px] w-full mt-4 relative">
|
|
{isSimulating && (
|
|
<div className="absolute inset-0 flex items-center justify-center bg-background/50 z-10">
|
|
<RefreshCw className="h-6 w-6 animate-spin text-muted-foreground" />
|
|
</div>
|
|
)}
|
|
<ResponsiveContainer width="100%" height="100%">
|
|
<AreaChart
|
|
data={chartData}
|
|
margin={{
|
|
top: 5,
|
|
right: 10,
|
|
left: 0,
|
|
bottom: 0,
|
|
}}
|
|
>
|
|
<defs>
|
|
<linearGradient id="colorBaseline" x1="0" y1="0" x2="0" y2="1">
|
|
<stop
|
|
offset="5%"
|
|
stopColor="#8884d8"
|
|
stopOpacity={0.6}
|
|
/>
|
|
<stop
|
|
offset="95%"
|
|
stopColor="#8884d8"
|
|
stopOpacity={0}
|
|
/>
|
|
</linearGradient>
|
|
<linearGradient id="colorSimulated" x1="0" y1="0" x2="0" y2="1">
|
|
<stop
|
|
offset="5%"
|
|
stopColor="#10b981"
|
|
stopOpacity={0.8}
|
|
/>
|
|
<stop
|
|
offset="95%"
|
|
stopColor="#10b981"
|
|
stopOpacity={0}
|
|
/>
|
|
</linearGradient>
|
|
</defs>
|
|
<CartesianGrid strokeDasharray="3 3" vertical={false} stroke="hsl(var(--border))" />
|
|
<XAxis
|
|
dataKey="formattedDate"
|
|
stroke="hsl(var(--muted-foreground))"
|
|
fontSize={12}
|
|
tickLine={false}
|
|
axisLine={false}
|
|
/>
|
|
<YAxis
|
|
stroke="hsl(var(--muted-foreground))"
|
|
fontSize={12}
|
|
tickLine={false}
|
|
axisLine={false}
|
|
tickFormatter={(value) => `£${value}`}
|
|
/>
|
|
<RechartsTooltip
|
|
contentStyle={{
|
|
backgroundColor: "hsl(var(--background))",
|
|
borderColor: "hsl(var(--border))",
|
|
borderRadius: "var(--radius)",
|
|
}}
|
|
formatter={(value: number, name: string) => [
|
|
formatGBP(value),
|
|
name === "baseline" ? "Baseline" : "Simulated"
|
|
]}
|
|
/>
|
|
{/* Always show baseline as solid line */}
|
|
<Area
|
|
type="monotone"
|
|
dataKey="baseline"
|
|
stroke="#8884d8"
|
|
fillOpacity={committedSimulationFactor !== 0 ? 0.3 : 1}
|
|
fill="url(#colorBaseline)"
|
|
strokeWidth={committedSimulationFactor !== 0 ? 1 : 2}
|
|
/>
|
|
{/* Show simulated line when simulation is active */}
|
|
{committedSimulationFactor !== 0 && (
|
|
<Area
|
|
type="monotone"
|
|
dataKey="simulated"
|
|
stroke="#10b981"
|
|
fillOpacity={0.6}
|
|
fill="url(#colorSimulated)"
|
|
strokeDasharray="5 5"
|
|
strokeWidth={2}
|
|
/>
|
|
)}
|
|
</AreaChart>
|
|
</ResponsiveContainer>
|
|
</div>
|
|
</CardContent>
|
|
</Card>
|
|
)}
|
|
</div>
|
|
)}
|
|
|
|
{activeTab === "stock" && (
|
|
<div className="space-y-4">
|
|
{stockPredictions && stockPredictions.predictions.length > 0 ? (
|
|
<>
|
|
<div className="flex items-center justify-between">
|
|
<div>
|
|
<p className="text-sm text-muted-foreground">
|
|
{stockPredictions.totalProducts} products tracked
|
|
</p>
|
|
{stockPredictions.productsNeedingRestock > 0 && (
|
|
<p className="text-sm font-medium text-orange-600 dark:text-orange-400 mt-1">
|
|
{stockPredictions.productsNeedingRestock} products
|
|
need restocking soon
|
|
</p>
|
|
)}
|
|
</div>
|
|
</div>
|
|
|
|
<div className="rounded-md border">
|
|
<Table>
|
|
<TableHeader>
|
|
<TableRow>
|
|
<TableHead>Product</TableHead>
|
|
<TableHead>Current Stock</TableHead>
|
|
<TableHead>Days Until Out</TableHead>
|
|
<TableHead>Estimated Date</TableHead>
|
|
<TableHead>Confidence</TableHead>
|
|
<TableHead>Status</TableHead>
|
|
</TableRow>
|
|
</TableHeader>
|
|
<TableBody>
|
|
{stockPredictions.predictions.map((prediction) => (
|
|
<TableRow key={prediction.productId}>
|
|
<TableCell className="font-medium">
|
|
{prediction.productName}
|
|
</TableCell>
|
|
<TableCell>
|
|
{prediction.currentStock} {prediction.unitType}
|
|
</TableCell>
|
|
<TableCell>
|
|
{prediction.prediction.daysUntilOutOfStock !== null ? (
|
|
<div className="space-y-1">
|
|
<div className="font-medium">
|
|
{prediction.prediction.daysUntilOutOfStock} days
|
|
</div>
|
|
{prediction.prediction.optimisticDays !== null &&
|
|
prediction.prediction.pessimisticDays && (
|
|
<div className="text-xs text-muted-foreground">
|
|
{prediction.prediction.optimisticDays} -{" "}
|
|
{prediction.prediction.pessimisticDays} days
|
|
</div>
|
|
)}
|
|
</div>
|
|
) : (
|
|
"N/A"
|
|
)}
|
|
</TableCell>
|
|
<TableCell>
|
|
{prediction.prediction.estimatedDate ? (
|
|
<div className="space-y-1">
|
|
<div>
|
|
{format(
|
|
new Date(
|
|
prediction.prediction.estimatedDate,
|
|
),
|
|
"MMM d, yyyy",
|
|
)}
|
|
</div>
|
|
{prediction.prediction.optimisticDate &&
|
|
prediction.prediction.pessimisticDate && (
|
|
<div className="text-xs text-muted-foreground">
|
|
{format(
|
|
new Date(
|
|
prediction.prediction.optimisticDate,
|
|
),
|
|
"MMM d",
|
|
)}{" "}
|
|
-{" "}
|
|
{format(
|
|
new Date(
|
|
prediction.prediction.pessimisticDate,
|
|
),
|
|
"MMM d",
|
|
)}
|
|
</div>
|
|
)}
|
|
</div>
|
|
) : (
|
|
"N/A"
|
|
)}
|
|
</TableCell>
|
|
<TableCell>
|
|
<Badge
|
|
className={getConfidenceColor(
|
|
prediction.prediction.confidence,
|
|
)}
|
|
>
|
|
{getConfidenceLabel(prediction.prediction.confidence)}
|
|
</Badge>
|
|
</TableCell>
|
|
<TableCell>
|
|
{prediction.needsRestock ? (
|
|
<Badge
|
|
variant="destructive"
|
|
className="flex items-center gap-1 w-fit"
|
|
>
|
|
<AlertTriangle className="h-3 w-3" />
|
|
Restock Soon
|
|
</Badge>
|
|
) : (
|
|
<Badge variant="outline">OK</Badge>
|
|
)}
|
|
</TableCell>
|
|
</TableRow>
|
|
))}
|
|
</TableBody>
|
|
</Table>
|
|
</div>
|
|
</>
|
|
) : (
|
|
<div className="text-center py-8 text-muted-foreground">
|
|
<Package className="h-12 w-12 mx-auto mb-2 opacity-50" />
|
|
<p>No stock predictions available</p>
|
|
<p className="text-sm mt-1">
|
|
Enable stock tracking on products to get predictions
|
|
</p>
|
|
</div>
|
|
)}
|
|
</div>
|
|
)}
|
|
</CardContent>
|
|
</Card >
|
|
);
|
|
}
|