feat(ml): add TA-Lib annotation generation and import workflow

Add complete workflow for using TA-Lib to bootstrap training data:

- generate_talib_annotations.py: Python script to run TA-Lib CDL* functions
  and output span annotations in UI-compatible format
- import_talib_annotations.ts: TypeScript script to import generated
  annotations into the UI database with auto-label-type creation
- npm script 'import-annotations' for easy execution
- TALIB_WORKFLOW.md: Comprehensive guide covering the full cycle:
  * Generate patterns with TA-Lib
  * Import into UI
  * Review and edit in browser
  * Export and train model
  * Compare predictions with TA-Lib detections
  * Iterate for improvement

This enables the intended workflow: use TA-Lib for initial annotations,
manually refine them, then train a model that learns from corrections.

.env

@@ -0,0 +1,9 @@
+NODE_ENV=production
+PORT=3000
+DATABASE_PATH=/app/data/candles.db
+
+# ML Inference Service Configuration
+INFERENCE_API_URL=http://localhost:8001
+INFERENCE_API_TIMEOUT=30000
+INFERENCE_BATCH_TIMEOUT=120000
+NEXT_PUBLIC_PREDICTIONS_ENABLED=true