fix: Change scheduler watchdog and loop logs from DEBUG to INFO level

- Change watchdog periodic status log from DEBUG to INFO
- Change scheduler loop status logs from DEBUG to INFO
- Add next_run_time to watchdog and scheduler loop logs for better visibility

This ensures scheduler activity is visible in production logs, making it
easier to diagnose if scheduler stops running. Previously, these logs were
at DEBUG level and not visible in production, making it hard to track
scheduler state between cycles.

Fixes: Missing logs between cycles making it unclear if scheduler is running

Author: anhmtk

backend/api/main.py

@@ -978,7 +978,7 @@ async def scheduler_watchdog():
                     # Scheduler is running - log status periodically (every 30 minutes = 6 checks)
                     import time
                     if int(time.time()) % 1800 < 300:  # Log roughly every 30 minutes
-                        logger.debug(f"✅ Learning scheduler watchdog: Scheduler is running (is_running={is_running}, task_done={task_done}, cycle_count={learning_scheduler.cycle_count})")
+                        logger.info(f"✅ Learning scheduler watchdog: Scheduler is running (is_running={is_running}, task_done={task_done}, cycle_count={learning_scheduler.cycle_count}, next_run={learning_scheduler.next_run_time.isoformat() if learning_scheduler.next_run_time else 'N/A'})")
 
             except asyncio.CancelledError:
                 logger.info("Scheduler watchdog cancelled")

backend/services/learning_scheduler.py

@@ -262,7 +262,7 @@ async def _scheduler_loop(self):
                 # Wait for next interval
                 wait_seconds = self.interval_hours * 3600
                 logger.info(f"⏳ Waiting {self.interval_hours} hours until next learning cycle...")
-                logger.debug(f"🔍 Scheduler loop: is_running={self.is_running}, wait_seconds={wait_seconds}")
+                logger.info(f"🔍 Scheduler loop: is_running={self.is_running}, wait_seconds={wait_seconds}, next_run_time={self.next_run_time.isoformat() if self.next_run_time else 'N/A'}")
 
                 # Use stop event to allow immediate cancellation
                 try:
@@ -285,7 +285,7 @@ async def _scheduler_loop(self):
 
                 logger.info("🔄 Scheduler loop: About to run next learning cycle...")
                 await self.run_learning_cycle()
-                logger.debug("✅ Scheduler loop: Learning cycle completed, continuing loop...")
+                logger.info("✅ Scheduler loop: Learning cycle completed, continuing loop...")
 
             except asyncio.CancelledError:
                 logger.info("🛑 Learning scheduler task cancelled")