Fix multiple critical bugs in onboarding training step

This commit addresses all identified bugs and issues in the training code path:

## Critical Fixes:
- Add get_start_time() method to TrainingLogRepository and fix non-existent method call
- Remove duplicate training.started event from API endpoint (trainer publishes the accurate one)
- Add missing progress events for 80-100% range (85%, 92%, 94%) to eliminate progress "dead zone"

## High Priority Fixes:
- Fix division by zero risk in time estimation with double-check and max() safety
- Remove unreachable exception handler in training_operations.py
- Simplify WebSocket token refresh logic to only reconnect on actual user session changes

## Medium Priority Fixes:
- Fix auto-start training effect with useRef to prevent duplicate starts
- Add HTTP polling debounce delay (5s) to prevent race conditions with WebSocket
- Extract all magic numbers to centralized constants files:
  - Backend: services/training/app/core/training_constants.py
  - Frontend: frontend/src/constants/training.ts
- Standardize error logging with exc_info=True on critical errors

## Code Quality Improvements:
- All progress percentages now use named constants
- All timeouts and intervals now use named constants
- Improved code maintainability and readability
- Better separation of concerns

## Files Changed:
- Backend: training_service.py, trainer.py, training_events.py, progress_tracker.py
- Backend: training_operations.py, training_log_repository.py, training_constants.py (new)
- Frontend: training.ts (hooks), MLTrainingStep.tsx, training.ts (constants, new)

All training progress events now properly flow from 0% to 100% with no gaps.

services/training/app/services/training_events.py

@@ -135,6 +135,61 @@ async def publish_data_analysis(
     return success
 
 
+async def publish_training_progress(
+    job_id: str,
+    tenant_id: str,
+    progress: int,
+    current_step: str,
+    step_details: Optional[str] = None,
+    estimated_time_remaining_seconds: Optional[int] = None,
+    estimated_completion_time: Optional[str] = None
+) -> bool:
+    """
+    Generic Training Progress Event (for any progress percentage)
+
+    Args:
+        job_id: Training job identifier
+        tenant_id: Tenant identifier
+        progress: Progress percentage (0-100)
+        current_step: Current step name
+        step_details: Details about the current step
+        estimated_time_remaining_seconds: Estimated time remaining in seconds
+        estimated_completion_time: ISO timestamp of estimated completion
+    """
+    event_data = {
+        "service_name": "training-service",
+        "event_type": "training.progress",
+        "timestamp": datetime.now().isoformat(),
+        "data": {
+            "job_id": job_id,
+            "tenant_id": tenant_id,
+            "progress": progress,
+            "current_step": current_step,
+            "step_details": step_details or current_step,
+            "estimated_time_remaining_seconds": estimated_time_remaining_seconds,
+            "estimated_completion_time": estimated_completion_time
+        }
+    }
+
+    success = await training_publisher.publish_event(
+        exchange_name="training.events",
+        routing_key="training.progress",
+        event_data=event_data
+    )
+
+    if success:
+        logger.info("Published training progress event",
+                   job_id=job_id,
+                   progress=progress,
+                   current_step=current_step)
+    else:
+        logger.error("Failed to publish training progress event",
+                    job_id=job_id,
+                    progress=progress)
+
+    return success
+
+
 async def publish_product_training_completed(
     job_id: str,
     tenant_id: str,