feat: auto-build training dataset from DB annotations before training

- Add build_dataset_from_db() that exports candles from DB, runs feature
  engineering, and ingests span annotations into labeled CSV
- Call it automatically in _run_training_background before training starts
- Add POST /training/build-dataset endpoint for standalone use
- Add Next.js proxy route /api/training/build-dataset
- Update TrainingPanel: remove dataset-missing block on Start Training,
  show informational message that dataset builds automatically

src/app/api/training/build-dataset/route.ts

@@ -0,0 +1,34 @@
+import { NextResponse } from 'next/server';
+
+const INFERENCE_API_URL = process.env.INFERENCE_API_URL || 'http://localhost:8001';
+const INFERENCE_API_TIMEOUT = parseInt(process.env.INFERENCE_API_TIMEOUT || '120000', 10);
+
+export async function POST() {
+  const controller = new AbortController();
+  const timeoutId = setTimeout(() => controller.abort(), INFERENCE_API_TIMEOUT);
+
+  try {
+    const response = await fetch(`${INFERENCE_API_URL}/training/build-dataset`, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      signal: controller.signal,
+    });
+    clearTimeout(timeoutId);
+
+    const data = await response.json();
+    if (!response.ok) {
+      return NextResponse.json({ error: data.detail || 'Failed to build dataset' }, { status: response.status });
+    }
+    return NextResponse.json(data);
+  } catch (error: any) {
+    clearTimeout(timeoutId);
+    if (error.name === 'AbortError') {
+      return NextResponse.json({ error: 'Dataset build timed out' }, { status: 504 });
+    }
+    if (error.cause?.code === 'ECONNREFUSED' || error.message?.includes('fetch failed')) {
+      return NextResponse.json({ error: 'Inference service unavailable' }, { status: 503 });
+    }
+    console.error('training/build-dataset proxy error:', error);
+    return NextResponse.json({ error: 'Internal server error' }, { status: 500 });
+  }
+}

src/components/TrainingPanel.tsx

@@ -228,8 +228,7 @@ export default function TrainingPanel() {
     }
   };
 
-  const datasetMissing = datasetInfo !== null && !datasetInfo.exists;
-  const canTrain = !isTraining && !datasetMissing && datasetInfo !== null;
+  const canTrain = !isTraining;
 
   return (
     <div>
@@ -272,8 +271,8 @@ export default function TrainingPanel() {
                 )}
               </>
             ) : (
-              <p className="text-orange-500">
-                No training dataset found. Export annotations first.
+              <p className="text-muted-foreground">
+                No cached dataset. It will be built automatically from your annotations when training starts.
               </p>
             )}
           </div>