Optimize PredictionsChart with batch data loading

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.

components/analytics/AnalyticsDashboard.tsx

@@ -49,8 +49,6 @@ import type { DateRange as ProfitDateRange } from "@/lib/services/profit-analyti
 import { MotionWrapper } from "@/components/ui/motion-wrapper";
 import { motion } from "framer-motion";
 
-// Lazy load chart components - already handled individually below
-
 const RevenueChart = dynamic(() => import("./RevenueChart"), {
   loading: () => <ChartSkeleton />,
 });

components/analytics/PredictionsChart.tsx

@@ -82,6 +82,12 @@ export default function PredictionsChart({
   const [baselinePredictions, setBaselinePredictions] = useState<PredictionsOverview | null>(
     null,
   );
+  // Batch data holds all pre-cached predictions for instant switching
+  const [batchData, setBatchData] = useState<{
+    [horizon: string]: {
+      [simulationFactor: string]: PredictionsOverview;
+    };
+  } | null>(null);
   const [stockPredictions, setStockPredictions] =
     useState<StockPredictionsResponse | null>(null);
   const [loading, setLoading] = useState(true);
@@ -92,16 +98,33 @@ export default function PredictionsChart({
   const [committedSimulationFactor, setCommittedSimulationFactor] = useState(0);
   const { toast } = useToast();
 
-  // Fetch baseline predictions (simulation factor = 0)
+  // Fetch all predictions in batch (for instant client-side switching)
-  const fetchBaseline = async () => {
+  const fetchBatchData = async () => {
     try {
       setLoading(true);
-      const [overview, stock] = await Promise.all([
+      const { getBatchPredictionsWithStore } = await import("@/lib/services/analytics-service");
-        getPredictionsOverviewWithStore(daysAhead, timeRange, 0),
+      const [batchResponse, stock] = await Promise.all([
+        getBatchPredictionsWithStore(timeRange),
         getStockPredictionsWithStore(timeRange),
       ]);
+
+      if (batchResponse.success && batchResponse.predictions) {
+        setBatchData(batchResponse.predictions);
+        // Set initial predictions from batch
+        const horizonData = batchResponse.predictions[daysAhead.toString()];
+        if (horizonData) {
+          const baseline = horizonData["0"];
+          if (baseline) {
+            setBaselinePredictions(baseline);
+            setPredictions(baseline);
+          }
+        }
+      } else {
+        // Fallback to single request if batch not available
+        const overview = await getPredictionsOverviewWithStore(daysAhead, timeRange, 0);
         setBaselinePredictions(overview);
         setPredictions(overview);
+      }
       setStockPredictions(stock);
     } catch (error) {
       console.error("Error fetching predictions:", error);
@@ -115,43 +138,46 @@ export default function PredictionsChart({
     }
   };
 
-  // Fetch simulated predictions (without full reload)
+  // Switch predictions from batch data (no API call!)
-  const fetchSimulation = async (factor: number) => {
+  const switchPredictions = useCallback((horizon: number, simFactor: number) => {
-    if (factor === 0) {
+    if (!batchData) return;
-      // Use cached baseline
-      setPredictions(baselinePredictions);
-      return;
-    }
 
-    try {
+    const horizonData = batchData[horizon.toString()];
-      setIsSimulating(true);
+    if (!horizonData) return;
-      const overview = await getPredictionsOverviewWithStore(daysAhead, timeRange, factor / 100);
-      setPredictions(overview);
-    } catch (error) {
-      console.error("Error fetching simulation:", error);
-      toast({
-        title: "Error",
-        description: "Failed to load simulation",
-        variant: "destructive",
-      });
-    } finally {
-      setIsSimulating(false);
-    }
-  };
 
-  // Fetch baseline on initial load or when daysAhead/timeRange changes
+    // Simulation factor is stored as decimal (e.g., 0.1 for 10%)
+    const simKey = (simFactor / 100).toString();
+    const newPrediction = horizonData[simKey];
+
+    if (newPrediction) {
+      setPredictions(newPrediction);
+      if (simFactor === 0) {
+        setBaselinePredictions(newPrediction);
+      }
+    }
+  }, [batchData]);
+
+  // Fetch batch data on initial load or when timeRange changes
   useEffect(() => {
-    fetchBaseline();
+    fetchBatchData();
     setCommittedSimulationFactor(0);
     setSimulationFactor(0);
-  }, [daysAhead, timeRange]);
+  }, [timeRange]);
 
-  // Fetch simulation when committed slider value changes
+  // Switch predictions when daysAhead changes (instant, from batch)
   useEffect(() => {
-    if (baselinePredictions) {
+    if (batchData) {
-      fetchSimulation(committedSimulationFactor);
+      switchPredictions(daysAhead, committedSimulationFactor);
     }
-  }, [committedSimulationFactor]);
+  }, [daysAhead, batchData, switchPredictions]);
+
+  // Switch predictions when simulation factor changes (instant, from batch)
+  useEffect(() => {
+    if (batchData) {
+      switchPredictions(daysAhead, committedSimulationFactor);
+    }
+  }, [committedSimulationFactor, batchData, switchPredictions]);
+
 
   const getConfidenceColor = (confidence: string) => {
     switch (confidence) {
@@ -305,7 +331,7 @@ export default function PredictionsChart({
             <Button
               variant="outline"
               size="icon"
-              onClick={fetchBaseline}
+              onClick={fetchBatchData}
               disabled={loading || isSimulating}
             >
               <RefreshCw
@@ -403,7 +429,7 @@ export default function PredictionsChart({
                                 </span>
                               </TooltipTrigger>
                               <TooltipContent>
-                                <p>Predictions generated using a Deep Learning Ensemble Model (TensorFlow.js)</p>
+                                <p>Predictions generated using a Deep Learning Ensemble Model</p>
                               </TooltipContent>
                             </Tooltip>
                           )}