fix: defensive evaluation import to prevent startup crash (#15)
Problem: Backend crashed on startup due to evaluation import failure Solution: Wrap evaluation_helper import in try/except Changes: - Import evaluation_helper with error handling - Add EVALUATION_AVAILABLE flag - All evaluation calls now check flag before executing - System remains functional even if evaluation system unavailable This prevents backend crashes if: - Migrations haven't run yet - Dependencies are missing - Import errors occur Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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@ -16,7 +16,15 @@ from auth import require_auth, check_feature_access, increment_feature_usage
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from models import ActivityEntry
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from routers.profiles import get_pid
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from feature_logger import log_feature_usage
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from evaluation_helper import evaluate_and_save_activity
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# Evaluation import with error handling (Phase 1.2)
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try:
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from evaluation_helper import evaluate_and_save_activity
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EVALUATION_AVAILABLE = True
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except Exception as e:
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logger.warning(f"[AUTO-EVAL] Evaluation system not available: {e}")
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EVALUATION_AVAILABLE = False
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evaluate_and_save_activity = None
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router = APIRouter(prefix="/api/activity", tags=["activity"])
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logger = logging.getLogger(__name__)
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@ -66,24 +74,25 @@ def create_activity(e: ActivityEntry, x_profile_id: Optional[str]=Header(default
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d['rpe'],d['source'],d['notes']))
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# Phase 1.2: Auto-evaluation after INSERT
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# Load the activity data to evaluate
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cur.execute("""
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SELECT id, profile_id, date, training_type_id, duration_min,
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hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
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rpe, pace_min_per_km, cadence, elevation_gain
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FROM activity_log
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WHERE id = %s
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""", (eid,))
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activity_row = cur.fetchone()
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if activity_row:
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activity_dict = dict(activity_row)
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training_type_id = activity_dict.get("training_type_id")
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if training_type_id:
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try:
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evaluate_and_save_activity(cur, eid, activity_dict, training_type_id, pid)
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logger.info(f"[AUTO-EVAL] Evaluated activity {eid} on INSERT")
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except Exception as eval_error:
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logger.error(f"[AUTO-EVAL] Failed to evaluate activity {eid}: {eval_error}")
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if EVALUATION_AVAILABLE:
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# Load the activity data to evaluate
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cur.execute("""
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SELECT id, profile_id, date, training_type_id, duration_min,
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hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
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rpe, pace_min_per_km, cadence, elevation_gain
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FROM activity_log
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WHERE id = %s
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""", (eid,))
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activity_row = cur.fetchone()
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if activity_row:
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activity_dict = dict(activity_row)
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training_type_id = activity_dict.get("training_type_id")
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if training_type_id:
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try:
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evaluate_and_save_activity(cur, eid, activity_dict, training_type_id, pid)
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logger.info(f"[AUTO-EVAL] Evaluated activity {eid} on INSERT")
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except Exception as eval_error:
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logger.error(f"[AUTO-EVAL] Failed to evaluate activity {eid}: {eval_error}")
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# Phase 2: Increment usage counter (always for new entries)
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increment_feature_usage(pid, 'activity_entries')
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@ -102,24 +111,25 @@ def update_activity(eid: str, e: ActivityEntry, x_profile_id: Optional[str]=Head
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list(d.values())+[eid,pid])
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# Phase 1.2: Auto-evaluation after UPDATE
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# Load the updated activity data to evaluate
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cur.execute("""
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SELECT id, profile_id, date, training_type_id, duration_min,
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hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
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rpe, pace_min_per_km, cadence, elevation_gain
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FROM activity_log
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WHERE id = %s
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""", (eid,))
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activity_row = cur.fetchone()
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if activity_row:
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activity_dict = dict(activity_row)
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training_type_id = activity_dict.get("training_type_id")
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if training_type_id:
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try:
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evaluate_and_save_activity(cur, eid, activity_dict, training_type_id, pid)
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logger.info(f"[AUTO-EVAL] Re-evaluated activity {eid} on UPDATE")
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except Exception as eval_error:
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logger.error(f"[AUTO-EVAL] Failed to re-evaluate activity {eid}: {eval_error}")
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if EVALUATION_AVAILABLE:
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# Load the updated activity data to evaluate
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cur.execute("""
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SELECT id, profile_id, date, training_type_id, duration_min,
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hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
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rpe, pace_min_per_km, cadence, elevation_gain
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FROM activity_log
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WHERE id = %s
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""", (eid,))
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activity_row = cur.fetchone()
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if activity_row:
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activity_dict = dict(activity_row)
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training_type_id = activity_dict.get("training_type_id")
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if training_type_id:
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try:
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evaluate_and_save_activity(cur, eid, activity_dict, training_type_id, pid)
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logger.info(f"[AUTO-EVAL] Re-evaluated activity {eid} on UPDATE")
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except Exception as eval_error:
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logger.error(f"[AUTO-EVAL] Failed to re-evaluate activity {eid}: {eval_error}")
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return {"id":eid}
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@ -257,28 +267,29 @@ def bulk_categorize_activities(
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updated_count = cur.rowcount
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# Phase 1.2: Auto-evaluation after bulk categorization
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# Load all activities that were just updated and evaluate them
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cur.execute("""
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SELECT id, profile_id, date, training_type_id, duration_min,
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hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
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rpe, pace_min_per_km, cadence, elevation_gain
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FROM activity_log
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WHERE profile_id = %s
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AND activity_type = %s
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AND training_type_id = %s
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""", (pid, activity_type, training_type_id))
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if EVALUATION_AVAILABLE:
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# Load all activities that were just updated and evaluate them
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cur.execute("""
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SELECT id, profile_id, date, training_type_id, duration_min,
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hr_avg, hr_max, distance_km, kcal_active, kcal_resting,
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rpe, pace_min_per_km, cadence, elevation_gain
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FROM activity_log
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WHERE profile_id = %s
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AND activity_type = %s
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AND training_type_id = %s
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""", (pid, activity_type, training_type_id))
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activities_to_evaluate = cur.fetchall()
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evaluated_count = 0
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for activity_row in activities_to_evaluate:
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activity_dict = dict(activity_row)
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try:
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evaluate_and_save_activity(cur, activity_dict["id"], activity_dict, training_type_id, pid)
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evaluated_count += 1
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except Exception as eval_error:
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logger.warning(f"[AUTO-EVAL] Failed to evaluate bulk-categorized activity {activity_dict['id']}: {eval_error}")
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activities_to_evaluate = cur.fetchall()
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evaluated_count = 0
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for activity_row in activities_to_evaluate:
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activity_dict = dict(activity_row)
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try:
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evaluate_and_save_activity(cur, activity_dict["id"], activity_dict, training_type_id, pid)
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evaluated_count += 1
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except Exception as eval_error:
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logger.warning(f"[AUTO-EVAL] Failed to evaluate bulk-categorized activity {activity_dict['id']}: {eval_error}")
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logger.info(f"[AUTO-EVAL] Evaluated {evaluated_count}/{updated_count} bulk-categorized activities")
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logger.info(f"[AUTO-EVAL] Evaluated {evaluated_count}/{updated_count} bulk-categorized activities")
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# Save mapping for future imports (upsert)
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cur.execute("""
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@ -369,7 +380,7 @@ async def import_activity_csv(file: UploadFile=File(...), x_profile_id: Optional
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skipped += 1 # Count as skipped (not newly inserted)
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# Phase 1.2: Auto-evaluation after CSV import UPDATE
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if training_type_id:
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if EVALUATION_AVAILABLE and training_type_id:
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try:
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# Build activity dict for evaluation
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activity_dict = {
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@ -408,7 +419,7 @@ async def import_activity_csv(file: UploadFile=File(...), x_profile_id: Optional
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inserted+=1
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# Phase 1.2: Auto-evaluation after CSV import INSERT
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if training_type_id:
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if EVALUATION_AVAILABLE and training_type_id:
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try:
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# Build activity dict for evaluation
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activity_dict = {
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