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

RAZNO/annotations.csv

@@ -0,0 +1 @@
+timestamp,label_type,price

services/ml/app/main.py

@@ -907,9 +907,82 @@ class DatasetInfoResponse(BaseModel):
     row_count: Optional[int] = None
 
 
+def build_dataset_from_db(config: PipelineConfig) -> dict:
+    """
+    Build the labeled training dataset directly from the database.
+
+    Steps:
+    1. Export candles from PostgreSQL to raw CSV
+    2. Run feature engineering (TA-Lib indicators, candle features)
+    3. Run annotation ingestion from DB (span_annotations -> labeled CSV)
+
+    Returns:
+        dict with keys: chart_name, n_candles, n_annotations, n_samples, labeled_path
+    """
+    from app.data_access import DataAccess
+    from app.annotation_ingestion import AnnotationIngestion
+    from features.engineer import run_feature_engineering_stage
+
+    data_access = DataAccess()
+
+    # Find all charts, use the first one (single-chart app)
+    charts_df = data_access.get_all_charts()
+    if charts_df.empty:
+        raise ValueError("No charts found in database. Upload candle data first.")
+
+    chart = charts_df.iloc[0]
+    chart_name = chart["name"]
+    chart_id = int(chart["id"])
+    logger.info(f"Building dataset for chart: {chart_name} (id={chart_id})")
+
+    # Step 1: Export candles to raw CSV
+    candles_df = data_access.get_candles(chart_id)
+    if candles_df.empty:
+        raise ValueError(f"No candles found for chart: {chart_name}")
+
+    raw_path = Path(config.data.raw_path)
+    raw_path.parent.mkdir(parents=True, exist_ok=True)
+
+    # Ensure 'time' column is suitable for feature engineering
+    export_df = candles_df[["time", "open", "high", "low", "close"]].copy()
+    export_df.to_csv(raw_path, index=False)
+    logger.info(f"Exported {len(export_df)} candles to {raw_path}")
+
+    # Step 2: Run feature engineering
+    run_feature_engineering_stage(config)
+    enriched_path = Path(config.data.enriched_path)
+    logger.info(f"Feature engineering complete: {enriched_path}")
+
+    # Step 3: Run annotation ingestion from database
+    enriched_df = pd.read_csv(enriched_path, parse_dates=["time"])
+    ingestion = AnnotationIngestion(config.stages.annotation_ingestion)
+    labeled_df = ingestion.process_from_db(enriched_df, chart_name, source="human")
+
+    if labeled_df.empty:
+        raise ValueError(
+            f"No labeled samples produced. "
+            f"Ensure you have span annotations on chart '{chart_name}'."
+        )
+
+    # Write labeled dataset
+    labeled_path = Path(config.data.labeled_path)
+    labeled_path.parent.mkdir(parents=True, exist_ok=True)
+    labeled_df.to_csv(labeled_path, index=False)
+
+    result = {
+        "chart_name": chart_name,
+        "n_candles": len(export_df),
+        "n_samples": len(labeled_df),
+        "n_features": len([c for c in labeled_df.columns if c != "label"]),
+        "labeled_path": str(labeled_path),
+    }
+    logger.info(f"Dataset built: {result}")
+    return result
+
+
 def _run_training_background(run_id: str, model_type: str, config: PipelineConfig) -> None:
     """
-    Background thread target: train a model, update DB on completion or failure.
+    Background thread target: build dataset then train a model.
 
     Uses the pre-inserted TrainingRun record identified by ``run_id``.
     """
@@ -920,6 +993,10 @@ def _run_training_background(run_id: str, model_type: str, config: PipelineConfi
         from training.train import create_model, temporal_split
         from sklearn.metrics import accuracy_score, f1_score
 
+        # Build dataset from database (feature engineering + annotation ingestion)
+        logger.info("Building dataset from database...")
+        build_dataset_from_db(config)
+
         labeled_path = Path(config.data.labeled_path)
         if not labeled_path.exists():
             raise FileNotFoundError(f"Labeled dataset not found: {labeled_path}")
@@ -1256,6 +1333,38 @@ async def training_dataset_info():
         )
 
 
+class BuildDatasetResponse(BaseModel):
+    """Response model for POST /training/build-dataset."""
+    chart_name: str
+    n_candles: int
+    n_samples: int
+    n_features: int
+    labeled_path: str
+
+
+@app.post("/training/build-dataset", response_model=BuildDatasetResponse)
+async def training_build_dataset():
+    """
+    Build the labeled training dataset from database annotations.
+
+    Exports candles, runs feature engineering, and ingests span annotations
+    into a labeled CSV ready for training.
+    """
+    config = state.pipeline_config or get_default_config()
+
+    try:
+        result = build_dataset_from_db(config)
+        return BuildDatasetResponse(**result)
+    except ValueError as exc:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=str(exc))
+    except Exception as exc:
+        logger.error(f"Failed to build dataset: {exc}", exc_info=True)
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Failed to build dataset: {exc}",
+        )
+
+
 # ---------------------------------------------------------------------------
 # Model Loading Endpoint
 # ---------------------------------------------------------------------------

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