Improve the frontend

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(