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/app.js

@@ -130,6 +130,9 @@ class WiFiDensePoseApp {
       this.components.sensing = new SensingTab(sensingContainer);
     }
 
+    // Training tab - lazy load to avoid breaking other tabs if import fails
+    this.initTrainingTab();
+
     // Architecture tab - static content, no component needed
 
     // Performance tab - static content, no component needed
@@ -137,6 +140,28 @@ class WiFiDensePoseApp {
     // Applications tab - static content, no component needed
   }
 
+  // Lazy-load Training tab panels (dynamic import so failures don't break other tabs)
+  async initTrainingTab() {
+    try {
+      const [{ default: TrainingPanel }, { default: ModelPanel }] = await Promise.all([
+        import('./components/TrainingPanel.js'),
+        import('./components/ModelPanel.js')
+      ]);
+
+      const trainingContainer = document.getElementById('training-panel-container');
+      if (trainingContainer) {
+        this.components.trainingPanel = new TrainingPanel(trainingContainer);
+      }
+
+      const modelContainer = document.getElementById('model-panel-container');
+      if (modelContainer) {
+        this.components.modelPanel = new ModelPanel(modelContainer);
+      }
+    } catch (error) {
+      console.error('Failed to load Training tab components:', error);
+    }
+  }
+
   // Handle tab changes
   handleTabChange(newTab, oldTab) {
     console.log(`Tab changed from ${oldTab} to ${newTab}`);
@@ -168,6 +193,16 @@ class WiFiDensePoseApp {
           });
         }
         break;
+
+      case 'training':
+        // Refresh panels when training tab becomes visible
+        if (this.components.trainingPanel && typeof this.components.trainingPanel.refresh === 'function') {
+          this.components.trainingPanel.refresh();
+        }
+        if (this.components.modelPanel && typeof this.components.modelPanel.refresh === 'function') {
+          this.components.modelPanel.refresh();
+        }
+        break;
     }
   }