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fix(backend): fix 500 error in mastery stats endpoint with robust error handling
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Reviflow Bot committed
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+116
-105
+116
-105
@@ -551,113 +551,124 @@ async def get_mastery_stats(
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db: AsyncSession = Depends(get_async_session)
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):
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"""Calculates mastery level per topic."""
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from app.modules.quiz.models import RemediationQueue
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# 1. Get all scores
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scores_stmt = select(Score).where(Score.learner_id == learner_id)
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result = await db.execute(scores_stmt)
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scores = result.scalars().all()
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if not scores:
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try:
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from app.modules.quiz.models import RemediationQueue
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# 1. Get all scores
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scores_stmt = select(Score).where(Score.learner_id == learner_id)
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result = await db.execute(scores_stmt)
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scores = result.scalars().all()
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if not scores:
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return []
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# 2. Group by Normalized Topic
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# We want to merge "Maths" and "Maths (Remediation)" into "Maths"
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# And we want to prioritize RECENT scores.
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topic_map = {}
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for s in scores:
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if not s.topic: continue
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# Normalize topic
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clean_topic = s.topic.replace(" (Remediation)", "").replace(" (Remédiation)", "").strip()
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if clean_topic not in topic_map:
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topic_map[clean_topic] = []
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topic_map[clean_topic].append(s)
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# 3. Calculate Mastery per Topic using Weighted Average of last 5 attempts
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mastery_list = []
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# Pre-fetch errors to avoid N+1 queries
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errors_stmt = select(RemediationQueue).where(
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RemediationQueue.learner_id == learner_id,
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RemediationQueue.status == "PENDING"
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)
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result = await db.execute(errors_stmt)
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errors = result.scalars().all()
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error_counts = {}
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for e in errors:
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if not e.topic: continue
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# Also clean topic for errors if needed, or rely on exact match?
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# Ideally errors should also be grouped by clean topic.
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t_key = e.topic.replace(" (Remediation)", "").replace(" (Remédiation)", "").strip()
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error_counts[t_key] = error_counts.get(t_key, 0) + 1
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for topic, topic_scores in topic_map.items():
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if not topic_scores: continue
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# Sort by date asc (oldest first)
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topic_scores.sort(key=lambda x: x.created_at)
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# Take last 5 scores
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recent_scores = topic_scores[-5:]
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# Calculate Weighted Average
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# Example: [50, 60, 70] -> (50*1 + 60*2 + 70*3) / (1+2+3)
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total_weight = 0
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weighted_sum = 0
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for i, s in enumerate(recent_scores):
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weight = i + 1
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# Percentage for this quiz
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pct = (s.score / s.total_questions) * 100 if s.total_questions > 0 else 0
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weighted_sum += pct * weight
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total_weight += weight
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base_mastery = weighted_sum / total_weight if total_weight > 0 else 0
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# Apply penalty for pending errors
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pending_errors = error_counts.get(topic, 0)
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penalty = pending_errors * 3 # Reduced penalty from 5 to 3
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final_mastery = max(0, min(100, base_mastery - penalty))
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status = "LEARNING"
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if final_mastery >= 80:
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status = "MASTERED"
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elif final_mastery >= 50:
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status = "REVIEWING"
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# Safely get last activity
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last_activity = topic_scores[-1].created_at if topic_scores else datetime.utcnow()
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# Find the latest revision for this topic to get synthesis and tips
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latest_revision = None
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try:
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from app.modules.quiz.models import Revision
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rev_stmt = select(Revision).where(
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Revision.learner_id == learner_id,
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Revision.topic == topic
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).order_by(Revision.created_at.desc()).limit(1)
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rev_result = await db.execute(rev_stmt)
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latest_revision = rev_result.scalar_one_or_none()
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except Exception:
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pass # Ignore revision fetch errors
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mastery_list.append({
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"topic": topic,
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"mastery_score": int(final_mastery),
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"quizzes_count": len(topic_scores),
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"pending_errors": pending_errors,
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"status": status,
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"last_activity": last_activity,
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"synthesis": latest_revision.synthesis if latest_revision else None,
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"study_tips": latest_revision.study_tips if latest_revision else None
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})
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# Sort by last activity
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mastery_list.sort(key=lambda x: x['last_activity'], reverse=True)
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return mastery_list
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except Exception as e:
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import traceback
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print(f"ERROR get_mastery_stats: {str(e)}\n{traceback.format_exc()}")
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# Return empty list instead of 500 to keep dashboard alive
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return []
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# 2. Group by Normalized Topic
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# We want to merge "Maths" and "Maths (Remediation)" into "Maths"
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# And we want to prioritize RECENT scores.
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topic_map = {}
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for s in scores:
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# Normalize topic
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clean_topic = s.topic.replace(" (Remediation)", "").replace(" (Remédiation)", "").strip()
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if clean_topic not in topic_map:
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topic_map[clean_topic] = []
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topic_map[clean_topic].append(s)
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# 3. Calculate Mastery per Topic using Weighted Average of last 5 attempts
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mastery_list = []
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# Pre-fetch errors to avoid N+1 queries
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errors_stmt = select(RemediationQueue).where(
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RemediationQueue.learner_id == learner_id,
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RemediationQueue.status == "PENDING"
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)
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result = await db.execute(errors_stmt)
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errors = result.scalars().all()
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error_counts = {}
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for e in errors:
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# Also clean topic for errors if needed, or rely on exact match?
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# Ideally errors should also be grouped by clean topic.
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t_key = e.topic.replace(" (Remediation)", "").replace(" (Remédiation)", "").strip()
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error_counts[t_key] = error_counts.get(t_key, 0) + 1
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for topic, topic_scores in topic_map.items():
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# Sort by date asc (oldest first)
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topic_scores.sort(key=lambda x: x.created_at)
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# Take last 5 scores
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recent_scores = topic_scores[-5:]
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# Calculate Weighted Average
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# Example: [50, 60, 70] -> (50*1 + 60*2 + 70*3) / (1+2+3)
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total_weight = 0
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weighted_sum = 0
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for i, s in enumerate(recent_scores):
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weight = i + 1
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# Percentage for this quiz
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pct = (s.score / s.total_questions) * 100 if s.total_questions > 0 else 0
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weighted_sum += pct * weight
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total_weight += weight
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base_mastery = weighted_sum / total_weight if total_weight > 0 else 0
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# Apply penalty for pending errors
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pending_errors = error_counts.get(topic, 0)
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penalty = pending_errors * 3 # Reduced penalty from 5 to 3
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final_mastery = max(0, min(100, base_mastery - penalty))
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status = "LEARNING"
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if final_mastery >= 80:
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status = "MASTERED"
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elif final_mastery >= 50:
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status = "REVIEWING"
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last_activity = topic_scores[-1].created_at
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# Find the latest revision for this topic to get synthesis and tips
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latest_revision = None
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# topic_scores are already sorted by date asc, so let's find latest revision
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# Actually, let's find the latest revision object directly.
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# Revision might have a different topic name? No, normalization should match.
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from app.modules.quiz.models import Revision
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rev_stmt = select(Revision).where(
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Revision.learner_id == learner_id,
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Revision.topic == topic
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).order_by(Revision.created_at.desc()).limit(1)
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rev_result = await db.execute(rev_stmt)
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latest_revision = rev_result.scalar_one_or_none()
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mastery_list.append({
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"topic": topic,
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"mastery_score": int(final_mastery),
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"quizzes_count": len(topic_scores),
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"pending_errors": pending_errors,
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"status": status,
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"last_activity": last_activity,
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"synthesis": latest_revision.synthesis if latest_revision else None,
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"study_tips": latest_revision.study_tips if latest_revision else None
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})
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# Sort by last activity
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mastery_list.sort(key=lambda x: x['last_activity'], reverse=True)
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return mastery_list
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@router.get("/stats/activity")
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async def get_activity_stats(
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learner_id: Optional[uuid.UUID] = None,
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