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")
    }