Improve the frontend

services/training/app/api/training_operations.py

@@ -9,6 +9,7 @@ import structlog
 from datetime import datetime, timezone
 import uuid
 import shared.redis_utils
+from sqlalchemy.ext.asyncio import AsyncSession
 
 from shared.routing import RouteBuilder
 from shared.monitoring.decorators import track_execution_time
@@ -39,6 +40,7 @@ from app.services.training_events import (
     publish_training_failed
 )
 from app.core.config import settings
+from app.core.database import get_db
 
 logger = structlog.get_logger()
 route_builder = RouteBuilder('training')
@@ -86,7 +88,8 @@ async def start_training_job(
     request_obj: Request = None,
     current_user: Dict[str, Any] = Depends(get_current_user_dep),
     enhanced_training_service: EnhancedTrainingService = Depends(get_enhanced_training_service),
-    rate_limiter = Depends(get_rate_limiter)
+    rate_limiter = Depends(get_rate_limiter),
+    db: AsyncSession = Depends(get_db)
 ):
     """
     Start a new training job for all tenant products (Admin+ only, quota enforced).
@@ -169,9 +172,7 @@ async def start_training_job(
         # We don't know exact product count yet, so use historical average or estimate
         try:
             # Try to get historical average for this tenant
-            from app.core.database import get_db
-            db = next(get_db())
-            historical_avg = get_historical_average_estimate(db, tenant_id)
+            historical_avg = await get_historical_average_estimate(db, tenant_id)
 
             # If no historical data, estimate based on typical product count (10-20 products)
             estimated_products = 15  # Conservative estimate

services/training/app/ml/trainer.py

@@ -129,9 +129,7 @@ class EnhancedBakeryMLTrainer:
 
                 # Try to get historical average for more accurate estimates
                 try:
-                    historical_avg = await asyncio.get_event_loop().run_in_executor(
-                        None,
-                        get_historical_average_estimate,
+                    historical_avg = await get_historical_average_estimate(
                         db_session,
                         tenant_id
                     )

services/training/app/utils/time_estimation.py

@@ -9,6 +9,8 @@ Provides intelligent time estimation for training jobs based on:
 from typing import List, Optional
 from datetime import datetime, timedelta, timezone
 import structlog
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
 
 logger = structlog.get_logger()
 
@@ -256,8 +258,8 @@ def format_time_remaining(seconds: int) -> str:
     return f"{hours} hour{'s' if hours > 1 else ''}"
 
 
-def get_historical_average_estimate(
-    db_session,
+async def get_historical_average_estimate(
+    db_session: AsyncSession,
     tenant_id: str,
     lookback_days: int = 30,
     limit: int = 10
@@ -269,7 +271,7 @@ def get_historical_average_estimate(
     recent historical data and calculate an average.
 
     Args:
-        db_session: Database session
+        db_session: Async database session
         tenant_id: Tenant UUID
         lookback_days: How many days back to look
         limit: Maximum number of historical records to consider
@@ -283,13 +285,19 @@ def get_historical_average_estimate(
 
         cutoff = datetime.now(timezone.utc) - timedelta(days=lookback_days)
 
-        # Query recent training performance metrics
-        metrics = db_session.query(TrainingPerformanceMetrics).filter(
-            TrainingPerformanceMetrics.tenant_id == tenant_id,
-            TrainingPerformanceMetrics.completed_at >= cutoff
-        ).order_by(
-            TrainingPerformanceMetrics.completed_at.desc()
-        ).limit(limit).all()
+        # Query recent training performance metrics using SQLAlchemy 2.0 async pattern
+        query = (
+            select(TrainingPerformanceMetrics)
+            .where(
+                TrainingPerformanceMetrics.tenant_id == tenant_id,
+                TrainingPerformanceMetrics.completed_at >= cutoff
+            )
+            .order_by(TrainingPerformanceMetrics.completed_at.desc())
+            .limit(limit)
+        )
+
+        result = await db_session.execute(query)
+        metrics = result.scalars().all()
 
         if not metrics:
             logger.info(