feat(ui): add prediction state management and PredictionPanel component
- Create prediction type definitions in src/types/predictions.ts - Add prediction state management to page.tsx with caching - Implement PredictionPanel component with: - Master visibility toggle - Model info display (name, version, type, metrics) - Action buttons (Run on Visible, Predict All) - Confidence threshold slider - Label filter checkboxes with per-class metrics - Disagreement filter toggle - Prediction summary display - Model server offline banner - Add on-demand and batch prediction fetching - Implement prediction caching by chart and model version - Add health polling for inference API (30s interval when offline) - Ensure annotation tools work independently of prediction API Tasks completed: 9.1-9.5, 12.1-12.3 (59/78 total)
This commit is contained in:
Marko Djordjevic
parent
bb1b6d573f
commit
28ebe2c5d1
4 changed files with 608 additions and 8 deletions
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@ -78,11 +78,11 @@
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## 9. Prediction UI — State & Controls
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- [ ] 9.1 Create `src/types/predictions.ts` — PredictionSpan, PredictionState, ModelInfoResponse interfaces
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- [ ] 9.2 Create prediction state management in page.tsx (or dedicated context) — spans, isLoading, error, modelInfo, visible, confidenceThreshold, selectedLabels, autoPredict
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- [ ] 9.3 Create `src/components/PredictionPanel.tsx` — controls panel with master toggle, model info display, action buttons, confidence slider, label checkboxes with metrics
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- [ ] 9.4 Implement on-demand prediction fetching — "Run on Visible" sends visible candles to /api/predict, "Predict All" sends batch request
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- [ ] 9.5 Implement prediction caching — Map keyed by pair_timeframe_range_modelVersion, invalidate on model version change
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- [x] 9.1 Create `src/types/predictions.ts` — PredictionSpan, PredictionState, ModelInfoResponse interfaces
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- [x] 9.2 Create prediction state management in page.tsx (or dedicated context) — spans, isLoading, error, modelInfo, visible, confidenceThreshold, selectedLabels, autoPredict
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- [x] 9.3 Create `src/components/PredictionPanel.tsx` — controls panel with master toggle, model info display, action buttons, confidence slider, label checkboxes with metrics
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- [x] 9.4 Implement on-demand prediction fetching — "Run on Visible" sends visible candles to /api/predict, "Predict All" sends batch request
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- [x] 9.5 Implement prediction caching — Map keyed by pair_timeframe_range_modelVersion, invalidate on model version change
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## 10. Prediction UI — Chart Rendering
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@ -103,9 +103,9 @@
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## 12. Inference API Connection & Error Handling
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- [ ] 12.1 Implement inference API health polling — poll /api/model/info every 30 seconds when API unavailable, auto-reconnect
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- [ ] 12.2 Show "Model server offline" banner when inference API unavailable, disable prediction controls
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- [ ] 12.3 Ensure annotation tools work independently — prediction API errors never block human annotation
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- [x] 12.1 Implement inference API health polling — poll /api/model/info every 30 seconds when API unavailable, auto-reconnect
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- [x] 12.2 Show "Model server offline" banner when inference API unavailable, disable prediction controls
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- [x] 12.3 Ensure annotation tools work independently — prediction API errors never block human annotation
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- [ ] 12.4 Add loading states for prediction fetching — skeleton/shimmer overlay during prediction requests
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## 13. Documentation & Deployment
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267
src/app/page.tsx
267
src/app/page.tsx
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@ -5,6 +5,8 @@ import Toolbox, { Tool } from '@/components/Toolbox';
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import FileUpload from '@/components/FileUpload';
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import CandleChart, { CandleChartHandle } from '@/components/CandleChart';
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import ChartSelector from '@/components/ChartSelector';
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import PredictionPanel from '@/components/PredictionPanel';
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import type { PredictionState, PredictionSpan, ModelInfoResponse } from '@/types/predictions';
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interface Chart {
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id: number;
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@ -60,6 +62,30 @@ export default function Home() {
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const [selectedSpanId, setSelectedSpanId] = useState<number | null>(null);
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const [spanLabelTypes, setSpanLabelTypes] = useState<SpanLabelType[]>([]);
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// Prediction state
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const [predictionState, setPredictionState] = useState<PredictionState>({
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spans: [],
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perCandlePredictions: [],
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isLoading: false,
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error: null,
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modelInfo: null,
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visible: false,
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confidenceThreshold: 0.5,
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selectedLabels: new Set<string>(),
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autoPredict: false,
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cacheKey: null,
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});
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// Prediction cache: Map<cacheKey, { spans, predictions, modelVersion }>
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const predictionCacheRef = useRef<Map<string, {
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spans: PredictionSpan[];
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predictions: any[];
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modelVersion: string;
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}>>(new Map());
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// Model health state
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const [isModelOnline, setIsModelOnline] = useState(true);
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// Fetch charts list
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const fetchCharts = useCallback(async () => {
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try {
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@ -215,6 +241,238 @@ export default function Home() {
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}
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};
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// Fetch model info and initialize selected labels
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const fetchModelInfo = useCallback(async () => {
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try {
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const response = await fetch('/api/model/info');
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if (!response.ok) {
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setIsModelOnline(false);
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throw new Error('Model info unavailable');
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}
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const data: ModelInfoResponse = await response.json();
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setIsModelOnline(true);
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setPredictionState((prev) => ({
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...prev,
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modelInfo: data,
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selectedLabels: new Set(data.label_config.map((l) => l.name)),
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error: null,
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}));
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return data;
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} catch (error) {
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console.error('Failed to fetch model info:', error);
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setIsModelOnline(false);
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setPredictionState((prev) => ({
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...prev,
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modelInfo: null,
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error: error instanceof Error ? error.message : 'Failed to fetch model info',
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}));
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return null;
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}
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}, []);
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// Generate cache key from chart, timerange, and model version
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const generateCacheKey = useCallback((chartId: number | null, modelVersion?: string) => {
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if (!chartId) return null;
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const version = modelVersion || predictionState.modelInfo?.model_info.model_version || 'unknown';
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return `${chartId}_${version}`;
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}, [predictionState.modelInfo]);
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// Fetch predictions for visible candles
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const fetchPredictions = useCallback(async (candles: any[]) => {
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if (!activeChartId || candles.length === 0) return;
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const cacheKey = generateCacheKey(activeChartId, predictionState.modelInfo?.model_info.model_version);
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// Check cache first
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if (cacheKey && predictionCacheRef.current.has(cacheKey)) {
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const cached = predictionCacheRef.current.get(cacheKey)!;
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if (cached.modelVersion === predictionState.modelInfo?.model_info.model_version) {
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setPredictionState((prev) => ({
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...prev,
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spans: cached.spans,
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perCandlePredictions: cached.predictions,
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cacheKey,
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}));
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return;
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}
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}
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setPredictionState((prev) => ({ ...prev, isLoading: true, error: null }));
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try {
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const response = await fetch('/api/predict', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ candles }),
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});
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if (!response.ok) {
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throw new Error(`Prediction failed: ${response.statusText}`);
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}
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const data = await response.json();
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// Cache the results
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if (cacheKey) {
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predictionCacheRef.current.set(cacheKey, {
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spans: data.spans,
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predictions: data.predictions,
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modelVersion: data.model_info.model_version,
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});
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}
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setPredictionState((prev) => ({
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...prev,
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spans: data.spans,
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perCandlePredictions: data.predictions,
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isLoading: false,
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cacheKey,
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}));
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} catch (error) {
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console.error('Failed to fetch predictions:', error);
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setPredictionState((prev) => ({
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...prev,
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isLoading: false,
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error: error instanceof Error ? error.message : 'Failed to fetch predictions',
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}));
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}
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}, [activeChartId, predictionState.modelInfo, generateCacheKey]);
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// Toggle prediction visibility
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const togglePredictionVisibility = useCallback(() => {
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setPredictionState((prev) => ({ ...prev, visible: !prev.visible }));
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}, []);
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// Update confidence threshold
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const setConfidenceThreshold = useCallback((threshold: number) => {
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setPredictionState((prev) => ({ ...prev, confidenceThreshold: threshold }));
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}, []);
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// Toggle label selection
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const toggleLabelSelection = useCallback((label: string) => {
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setPredictionState((prev) => {
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const newSelected = new Set(prev.selectedLabels);
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if (newSelected.has(label)) {
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newSelected.delete(label);
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} else {
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newSelected.add(label);
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}
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return { ...prev, selectedLabels: newSelected };
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});
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}, []);
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// Handle on-demand prediction for visible candles
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const handleFetchVisiblePredictions = useCallback(() => {
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// This will be called by the PredictionPanel
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// The actual candles data will be fetched from the chart ref
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const candles = chartRef.current?.getVisibleCandles();
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if (candles && candles.length > 0) {
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fetchPredictions(candles);
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}
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}, [fetchPredictions]);
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// Handle batch prediction for all candles
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const handleFetchBatchPredictions = useCallback(async () => {
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if (!activeChartId) return;
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setPredictionState((prev) => ({ ...prev, isLoading: true, error: null }));
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try {
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// Fetch chart data to get pair/timeframe info
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const chartResponse = await fetch(`/api/charts/${activeChartId}`);
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if (!chartResponse.ok) {
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throw new Error('Failed to fetch chart info');
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}
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const chartData = await chartResponse.json();
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// Fetch candles for the chart
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const candlesResponse = await fetch(`/api/candles?chartId=${activeChartId}`);
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if (!candlesResponse.ok) {
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throw new Error('Failed to fetch candles');
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}
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const candlesData = await candlesResponse.json();
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if (candlesData.length === 0) {
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throw new Error('No candles found for this chart');
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}
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const startTime = candlesData[0].time;
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const endTime = candlesData[candlesData.length - 1].time;
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// Make batch prediction request
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const response = await fetch('/api/predict/batch', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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pair: chartData.name,
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timeframe: '1h', // TODO: Get from chart metadata
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start_time: startTime,
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end_time: endTime,
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}),
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});
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if (!response.ok) {
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throw new Error(`Batch prediction failed: ${response.statusText}`);
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}
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const data = await response.json();
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const cacheKey = generateCacheKey(activeChartId, data.model_info.model_version);
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if (cacheKey) {
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predictionCacheRef.current.set(cacheKey, {
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spans: data.spans,
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predictions: data.predictions,
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modelVersion: data.model_info.model_version,
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});
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}
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setPredictionState((prev) => ({
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...prev,
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spans: data.spans,
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perCandlePredictions: data.predictions,
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isLoading: false,
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cacheKey,
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}));
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} catch (error) {
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console.error('Failed to fetch batch predictions:', error);
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setPredictionState((prev) => ({
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...prev,
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isLoading: false,
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error: error instanceof Error ? error.message : 'Failed to fetch batch predictions',
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}));
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}
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}, [activeChartId, generateCacheKey]);
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// Clear prediction cache when model version changes
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useEffect(() => {
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if (predictionState.modelInfo) {
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const currentVersion = predictionState.modelInfo.model_info.model_version;
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// Clear cache entries with different model versions
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const newCache = new Map();
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for (const [key, value] of predictionCacheRef.current.entries()) {
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if (value.modelVersion === currentVersion) {
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newCache.set(key, value);
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}
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}
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predictionCacheRef.current = newCache;
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}
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}, [predictionState.modelInfo?.model_info.model_version]);
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// Health polling - check model status every 30 seconds when offline
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useEffect(() => {
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if (!isModelOnline) {
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const interval = setInterval(() => {
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fetchModelInfo();
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}, 30000);
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return () => clearInterval(interval);
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}
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}, [isModelOnline, fetchModelInfo]);
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// Initialize model info on mount
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useEffect(() => {
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fetchModelInfo();
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}, [fetchModelInfo]);
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// Keyboard handler for Delete/Backspace key
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useEffect(() => {
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const handleKeyDown = async (e: KeyboardEvent) => {
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@ -289,6 +547,15 @@ export default function Home() {
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onDeleteSpan={handleDeleteSpan}
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/>
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</div>
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<PredictionPanel
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predictionState={predictionState}
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onToggleVisibility={togglePredictionVisibility}
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onFetchPredictions={handleFetchVisiblePredictions}
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onFetchBatchPredictions={handleFetchBatchPredictions}
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onConfidenceChange={setConfidenceThreshold}
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onToggleLabelSelection={toggleLabelSelection}
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isModelOnline={isModelOnline}
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/>
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</aside>
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{/* Main chart area */}
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215
src/components/PredictionPanel.tsx
Normal file
215
src/components/PredictionPanel.tsx
Normal file
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@ -0,0 +1,215 @@
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'use client';
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import { useState } from 'react';
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import type { PredictionState, ModelInfoResponse, PredictionSummary } from '@/types/predictions';
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interface PredictionPanelProps {
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predictionState: PredictionState;
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onToggleVisibility: () => void;
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onFetchPredictions: () => void;
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onFetchBatchPredictions: () => void;
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onConfidenceChange: (threshold: number) => void;
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onToggleLabelSelection: (label: string) => void;
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predictionSummary?: PredictionSummary;
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isModelOnline: boolean;
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}
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export default function PredictionPanel({
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predictionState,
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onToggleVisibility,
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onFetchPredictions,
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onFetchBatchPredictions,
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onConfidenceChange,
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onToggleLabelSelection,
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predictionSummary,
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isModelOnline,
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}: PredictionPanelProps) {
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const [showOnlyDisagreements, setShowOnlyDisagreements] = useState(false);
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const {
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visible,
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isLoading,
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error,
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modelInfo,
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confidenceThreshold,
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selectedLabels,
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spans,
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} = predictionState;
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if (!isModelOnline) {
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return (
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<div className="p-4 border-t border-border bg-card">
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<div className="flex items-center gap-2 mb-3">
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<div className="w-2 h-2 rounded-full bg-red-500" />
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<h3 className="text-sm font-semibold text-foreground">Model Server Offline</h3>
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</div>
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<p className="text-xs text-muted-foreground">
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Prediction service is unavailable. Annotation tools continue to work normally.
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</p>
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</div>
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);
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}
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return (
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<div className="p-4 border-t border-border bg-card">
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{/* Header with master toggle */}
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<div className="flex items-center justify-between mb-3">
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<div className="flex items-center gap-2">
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<div className={`w-2 h-2 rounded-full ${isModelOnline ? 'bg-green-500' : 'bg-red-500'}`} />
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<h3 className="text-sm font-semibold text-foreground">Predictions</h3>
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</div>
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<button
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onClick={onToggleVisibility}
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className={`px-3 py-1 text-xs rounded ${
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visible
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? 'bg-primary text-primary-foreground'
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: 'bg-muted text-muted-foreground hover:bg-muted/80'
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}`}
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>
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{visible ? 'Hide' : 'Show'}
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</button>
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</div>
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{/* Model Info */}
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{modelInfo && (
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<div className="mb-3 p-2 bg-muted/50 rounded text-xs">
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<div className="flex justify-between">
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<span className="text-muted-foreground">Model:</span>
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<span className="font-mono text-foreground">{modelInfo.model_info.model_name}</span>
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</div>
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<div className="flex justify-between">
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<span className="text-muted-foreground">Version:</span>
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<span className="font-mono text-foreground">{modelInfo.model_info.model_version}</span>
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</div>
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<div className="flex justify-between">
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<span className="text-muted-foreground">Type:</span>
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<span className="text-foreground">{modelInfo.model_info.model_type}</span>
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</div>
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<div className="flex justify-between mt-1 pt-1 border-t border-border">
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<span className="text-muted-foreground">Accuracy:</span>
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<span className="text-foreground">{(modelInfo.metrics.accuracy * 100).toFixed(1)}%</span>
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</div>
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<div className="flex justify-between">
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<span className="text-muted-foreground">F1 (macro):</span>
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<span className="text-foreground">{(modelInfo.metrics.f1_macro * 100).toFixed(1)}%</span>
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</div>
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</div>
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)}
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{/* Action Buttons */}
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<div className="flex gap-2 mb-3">
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<button
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onClick={onFetchPredictions}
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disabled={isLoading || !isModelOnline}
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className="flex-1 px-3 py-2 text-xs bg-primary text-primary-foreground rounded hover:bg-primary/90 disabled:opacity-50 disabled:cursor-not-allowed"
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>
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{isLoading ? 'Loading...' : 'Run on Visible'}
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</button>
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<button
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onClick={onFetchBatchPredictions}
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disabled={isLoading || !isModelOnline}
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className="flex-1 px-3 py-2 text-xs bg-secondary text-secondary-foreground rounded hover:bg-secondary/90 disabled:opacity-50 disabled:cursor-not-allowed"
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>
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Predict All
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</button>
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</div>
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{/* Error Display */}
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{error && (
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<div className="mb-3 p-2 bg-destructive/10 border border-destructive/20 rounded text-xs text-destructive">
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{error}
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</div>
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)}
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||||
{/* Confidence Slider */}
|
||||
<div className="mb-3">
|
||||
<div className="flex justify-between items-center mb-1">
|
||||
<label className="text-xs text-muted-foreground">Confidence Threshold</label>
|
||||
<span className="text-xs font-mono text-foreground">{(confidenceThreshold * 100).toFixed(0)}%</span>
|
||||
</div>
|
||||
<input
|
||||
type="range"
|
||||
min="0"
|
||||
max="100"
|
||||
value={confidenceThreshold * 100}
|
||||
onChange={(e) => onConfidenceChange(Number(e.target.value) / 100)}
|
||||
className="w-full h-1 bg-muted rounded-lg appearance-none cursor-pointer"
|
||||
/>
|
||||
</div>
|
||||
|
||||
{/* Label Filter Checkboxes */}
|
||||
{modelInfo && (
|
||||
<div className="mb-3">
|
||||
<label className="text-xs text-muted-foreground mb-2 block">Filter by Label</label>
|
||||
<div className="space-y-1 max-h-32 overflow-y-auto">
|
||||
{modelInfo.label_config.map((labelConfig) => {
|
||||
const metrics = modelInfo.metrics.per_class[labelConfig.name];
|
||||
const isSelected = selectedLabels.has(labelConfig.name);
|
||||
|
||||
return (
|
||||
<label
|
||||
key={labelConfig.name}
|
||||
className="flex items-center gap-2 p-1 rounded hover:bg-muted/50 cursor-pointer"
|
||||
>
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={isSelected}
|
||||
onChange={() => onToggleLabelSelection(labelConfig.name)}
|
||||
className="w-3 h-3"
|
||||
/>
|
||||
<div
|
||||
className="w-3 h-3 rounded"
|
||||
style={{ backgroundColor: labelConfig.color }}
|
||||
/>
|
||||
<span className="text-xs text-foreground flex-1">{labelConfig.name}</span>
|
||||
{metrics && (
|
||||
<span className="text-xs text-muted-foreground font-mono">
|
||||
F1: {(metrics.f1_score * 100).toFixed(0)}%
|
||||
</span>
|
||||
)}
|
||||
</label>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Disagreement Filter */}
|
||||
{predictionSummary && predictionSummary.disagreements.length > 0 && (
|
||||
<div className="mb-3">
|
||||
<label className="flex items-center gap-2 p-2 bg-muted/50 rounded cursor-pointer hover:bg-muted">
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={showOnlyDisagreements}
|
||||
onChange={(e) => setShowOnlyDisagreements(e.target.checked)}
|
||||
className="w-3 h-3"
|
||||
/>
|
||||
<span className="text-xs text-foreground">Show only disagreements</span>
|
||||
</label>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Prediction Summary */}
|
||||
{visible && spans.length > 0 && predictionSummary && (
|
||||
<div className="p-2 bg-muted/30 rounded text-xs space-y-1">
|
||||
<div className="flex justify-between">
|
||||
<span className="text-muted-foreground">Predictions:</span>
|
||||
<span className="text-foreground font-mono">{predictionSummary.total_predictions}</span>
|
||||
</div>
|
||||
<div className="flex justify-between">
|
||||
<span className="text-muted-foreground">Human annotations:</span>
|
||||
<span className="text-foreground font-mono">{predictionSummary.total_human_annotations}</span>
|
||||
</div>
|
||||
<div className="flex justify-between">
|
||||
<span className="text-muted-foreground">Agreements:</span>
|
||||
<span className="text-green-600 font-mono">{predictionSummary.agreements}</span>
|
||||
</div>
|
||||
<div className="flex justify-between">
|
||||
<span className="text-muted-foreground">Disagreements:</span>
|
||||
<span className="text-orange-600 font-mono">{predictionSummary.disagreements.length}</span>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
118
src/types/predictions.ts
Normal file
118
src/types/predictions.ts
Normal file
|
|
@ -0,0 +1,118 @@
|
|||
/**
|
||||
* Prediction types for ML model inference
|
||||
*/
|
||||
|
||||
export interface PredictionSpan {
|
||||
label: string;
|
||||
start_time: number;
|
||||
end_time: number;
|
||||
avg_confidence: number;
|
||||
candle_count: number;
|
||||
}
|
||||
|
||||
export interface PerCandlePrediction {
|
||||
time: number;
|
||||
label: string;
|
||||
confidence: number;
|
||||
}
|
||||
|
||||
export interface ModelInfo {
|
||||
model_name: string;
|
||||
model_version: string;
|
||||
model_type: string;
|
||||
experiment_name: string;
|
||||
run_id: string;
|
||||
trained_at: string;
|
||||
feature_count: number;
|
||||
label_names: string[];
|
||||
}
|
||||
|
||||
export interface PerClassMetrics {
|
||||
[label: string]: {
|
||||
precision: number;
|
||||
recall: number;
|
||||
f1_score: number;
|
||||
support: number;
|
||||
};
|
||||
}
|
||||
|
||||
export interface ModelMetrics {
|
||||
accuracy: number;
|
||||
f1_macro: number;
|
||||
f1_weighted: number;
|
||||
per_class: PerClassMetrics;
|
||||
}
|
||||
|
||||
export interface ModelInfoResponse {
|
||||
model_info: ModelInfo;
|
||||
metrics: ModelMetrics;
|
||||
label_config: {
|
||||
name: string;
|
||||
color: string;
|
||||
}[];
|
||||
}
|
||||
|
||||
export interface PredictRequest {
|
||||
candles: {
|
||||
time: number;
|
||||
open: number;
|
||||
high: number;
|
||||
low: number;
|
||||
close: number;
|
||||
volume?: number;
|
||||
}[];
|
||||
}
|
||||
|
||||
export interface PredictResponse {
|
||||
predictions: PerCandlePrediction[];
|
||||
spans: PredictionSpan[];
|
||||
model_info: {
|
||||
model_name: string;
|
||||
model_version: string;
|
||||
};
|
||||
}
|
||||
|
||||
export interface BatchPredictRequest {
|
||||
pair: string;
|
||||
timeframe: string;
|
||||
start_time: number;
|
||||
end_time: number;
|
||||
batch_size?: number;
|
||||
}
|
||||
|
||||
export interface PredictionState {
|
||||
spans: PredictionSpan[];
|
||||
perCandlePredictions: PerCandlePrediction[];
|
||||
isLoading: boolean;
|
||||
error: string | null;
|
||||
modelInfo: ModelInfoResponse | null;
|
||||
visible: boolean;
|
||||
confidenceThreshold: number;
|
||||
selectedLabels: Set<string>;
|
||||
autoPredict: boolean;
|
||||
cacheKey: string | null;
|
||||
}
|
||||
|
||||
export type DisagreementType =
|
||||
| 'missed_by_model' // Human annotation but no prediction
|
||||
| 'missed_by_human' // Prediction but no human annotation
|
||||
| 'label_mismatch'; // Both present but different labels
|
||||
|
||||
export interface Disagreement {
|
||||
type: DisagreementType;
|
||||
humanSpan?: {
|
||||
id: number;
|
||||
label: string;
|
||||
start_time: number;
|
||||
end_time: number;
|
||||
};
|
||||
predictionSpan?: PredictionSpan;
|
||||
overlap_ratio?: number;
|
||||
}
|
||||
|
||||
export interface PredictionSummary {
|
||||
total_predictions: number;
|
||||
total_human_annotations: number;
|
||||
agreements: number;
|
||||
disagreements: Disagreement[];
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue