feat: complete pragativeer v2 frontend and backend, docker setup, and cbt engine

backend/analytics.py

@@ -0,0 +1,63 @@
+from sqlmodel import Session, select
+from datetime import datetime, timedelta
+from collections import Counter
+from uuid import UUID
+import models
+
+def generate_parent_report(student_id: UUID, session: Session) -> dict:
+    """
+    Aggregates granular performance data for a specific student 
+    into a clean, simple, parent-friendly schema.
+    """
+    # 1. Fetch Student Baseline Info
+    student = session.get(models.User, student_id)
+    if not student:
+        return {"error": "Student record not found."}
+
+    # 2. Compile Last 7 Days Time & Attempts Data
+    seven_days_ago = datetime.utcnow() - timedelta(days=7)
+    attempts = session.exec(
+        select(models.Attempt)
+        .where(models.Attempt.user_id == student_id)
+        .where(models.Attempt.submitted_at >= seven_days_ago)
+    ).all()
+
+    total_time_spent_seconds = sum((a.time_taken or 0) for a in attempts)
+    total_time_spent_minutes = round(total_time_spent_seconds / 60, 1)
+
+    # 3. Calculate 7-Day Accuracy Trend Line Data
+    # Group accuracy by date strings for simple frontend parsing
+    trend_data = []
+    for i in range(6, -1, -1):
+        target_date = (datetime.utcnow() - timedelta(days=i)).date()
+        daily_attempts = [a for a in attempts if a.submitted_at.date() == target_date]
+        
+        if daily_attempts:
+            avg_accuracy = sum(a.accuracy for a in daily_attempts) / len(daily_attempts)
+            trend_data.append({"date": target_date.strftime("%b %d"), "accuracy": round(avg_accuracy, 1)})
+        else:
+            trend_data.append({"date": target_date.strftime("%b %d"), "accuracy": 0.0})
+
+    # 4. Extract Top "Areas of Concern" (Active un-mastered mistakes)
+    active_mistakes = session.exec(
+        select(models.Mistake)
+        .where(models.Mistake.user_id == student_id)
+        .where(models.Mistake.is_mastered == False)
+    ).all()
+
+    # Aggregate topic counts to find where they struggle most
+    topic_counter = Counter([m.topic for m in active_mistakes])
+    top_concerns = [
+        {"topic": topic, "pending_errors": count}
+        for topic, count in topic_counter.most_common(3)
+    ]
+
+    return {
+        "student_name": student.name,
+        "streak_count": student.streak_count,
+        "weekly_minutes_spent": total_time_spent_minutes,
+        "total_tests_attempted_this_week": len(attempts),
+        "accuracy_trend": trend_data,
+        "areas_of_concern": top_concerns,
+        "last_active": student.last_active.strftime("%Y-%m-%d %H:%M:%S")
+    }