fix: WebSocket race condition, data source indicators, auto-start pose detection (#96)

* feat: RVF training pipeline & UI integration (ADR-036)

Implement full model training, management, and inference pipeline:

Backend (Rust):
- recording.rs: CSI recording API (start/stop/list/download/delete)
- model_manager.rs: RVF model loading, LoRA profile switching, model library
- training_api.rs: Training API with WebSocket progress streaming, simulated
  training mode with realistic loss curves, auto-RVF export on completion
- main.rs: Wire new modules, recording hooks in all CSI paths, data dirs

UI (new components):
- ModelPanel.js: Dark-mode model library with load/unload, LoRA dropdown
- TrainingPanel.js: Recording controls, training config, live Canvas charts
- model.service.js: Model REST API client with events
- training.service.js: Training + recording API client with WebSocket progress

UI (enhancements):
- LiveDemoTab: Model selector, LoRA profile switcher, A/B split view toggle,
  training quick-panel with 60s recording shortcut
- SettingsPanel: Full dark mode conversion (issue #92), model configuration
  (device, threads, auto-load), training configuration (epochs, LR, patience)
- PoseDetectionCanvas: 10-frame pose trail with ghost keypoints and motion
  trajectory lines, cyan trail toggle button
- pose.service.js: Model-inference confidence thresholds

UI (plumbing):
- index.html: Training tab (8th tab)
- app.js: Panel initialization and tab routing
- style.css: ~250 lines of training/model panel dark-mode styles

191 Rust tests pass, 0 failures. Closes #92.

Refs: ADR-036, #93

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: real RuVector training pipeline + UI service fixes

Training pipeline (training_api.rs):
- Replace simulated training with real signal-based training loop
- Load actual CSI data from .csi.jsonl recordings or live frame history
- Extract 180 features per frame: subcarrier amplitudes, temporal variance,
  Goertzel frequency analysis (9 bands), motion gradients, global stats
- Train calibrated linear CSI-to-pose mapping via mini-batch gradient descent
  with L2 regularization (ridge regression), Xavier init, cosine LR decay
- Self-supervised: teacher targets from derive_pose_from_sensing() heuristics
- Real validation metrics: MSE and PCK@0.2 on 80/20 train/val split
- Export trained .rvf with real weights, feature normalization stats, witness
- Add infer_pose_from_model() for live inference from trained model
- 16 new tests covering features, training, inference, serialization

UI fixes:
- Fix double-URL bug in model.service.js and training.service.js
  (buildApiUrl was called twice — once in service, once in apiService)
- Fix route paths to match Rust backend (/api/v1/train/*, /api/v1/recording/*)
- Fix request body formats (session_name, nested config object)
- Fix top-level await in LiveDemoTab.js blocking module graph
- Dynamic imports for ModelPanel/TrainingPanel in app.js
- Center nav tabs with flex-wrap for 8-tab layout

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: WebSocket onOpen race condition, data source indicators, auto-start pose detection

- Fix WebSocket onOpen race condition in websocket.service.js where
  setupEventHandlers replaced onopen after socket was already open,
  preventing pose service from receiving connection signal
- Add 4-state data source indicator (LIVE/SIMULATED/RECONNECTING/OFFLINE)
  across Dashboard, Sensing, and Live Demo tabs via sensing.service.js
- Add hot-plug ESP32 auto-detection in sensing server (auto mode runs
  both UDP listener and simulation, switches on ESP32_TIMEOUT)
- Auto-start pose detection when backend is reachable
- Hide duplicate PoseDetectionCanvas controls when enableControls=false
- Add standalone Demo button in LiveDemoTab for offline animated demo
- Add data source banner and status styling

Co-Authored-By: claude-flow <ruv@ruv.net>

ui/components/SensingTab.js

@@ -216,9 +216,10 @@ export class SensingTab {
       // Map the service's dataSource to banner text and CSS modifier class.
       const dataSource = sensingService.dataSource;
       const bannerConfig = {
-        live:         { text: 'LIVE - ESP32',     cls: 'sensing-source-live' },
-        reconnecting: { text: 'RECONNECTING...', cls: 'sensing-source-reconnecting' },
-        simulated:    { text: 'SIMULATED DATA',  cls: 'sensing-source-simulated' },
+        'live':              { text: 'LIVE \u2014 ESP32 HARDWARE',           cls: 'sensing-source-live' },
+        'server-simulated':  { text: 'SIMULATED \u2014 NO HARDWARE',        cls: 'sensing-source-server-sim' },
+        'reconnecting':      { text: 'RECONNECTING...',                    cls: 'sensing-source-reconnecting' },
+        'simulated':         { text: 'OFFLINE \u2014 CLIENT SIMULATION',    cls: 'sensing-source-simulated' },
       };
       const cfg = bannerConfig[dataSource] || bannerConfig.reconnecting;
       banner.textContent = cfg.text;
@@ -256,7 +257,8 @@ export class SensingTab {
     // Details
     this._setText('valDomFreq', (f.dominant_freq_hz || 0).toFixed(3) + ' Hz');
     this._setText('valChangePoints', String(f.change_points || 0));
-    this._setText('valSampleRate', data.source === 'simulated' ? 'sim' : 'live');
+    const srcLabel = (data.source === 'simulated' || data.source === 'simulate') ? 'sim' : data.source || 'live';
+    this._setText('valSampleRate', srcLabel);
 
     // Sparkline
     this._drawSparkline();