Progress Tracker

def calculate_grade_trends(grade_history: str) -> str:
    """
    Calculate grade trends over time to identify patterns and predict outcomes.
    
    Args:
        grade_history: JSON string with historical grade data
        Example: {"student_id": "12345", "grades": [{"date": "2026-01-15", "subject": "Math", "score": 85}, ...]}
    
    Returns:
        Grade trend analysis with trajectory predictions
    """
    try:
        if isinstance(grade_history, str):
            try:
                data = json.loads(grade_history)
            except json.JSONDecodeError:
                return "Error: Invalid JSON format for grade history"
        else:
            data = grade_history
        
        student_id = data.get('student_id', 'Unknown')
        grades = data.get('grades', [])
        
        if not grades:
            return "Error: No grade data provided"
        
        "color: #6b7280;"># Organize by subject
        by_subject = {}
        for grade in grades:
            subject = grade.get('subject', 'Unknown')
            score = grade.get('score', 0)
            if subject not in by_subject:
                by_subject[subject] = []
            by_subject[subject].append(score)
        
        report = f"=== GRADE TREND ANALYSIS ===\n\n"
        report += f"Student ID: {student_id}\n"
        report += f"Analysis Period: {len(grades)} grade entries\n\n"
        
        report += f"{'Subject':<20} {'Average':>10} {'Trend':>10} {'Risk Level':>15}\n"
        report += "-" * 65 + "\n"
        
        overall_at_risk = False
        
        for subject, scores in by_subject.items():
            avg = sum(scores) / len(scores)
            
            "color: #6b7280;"># Calculate trend
            if len(scores) >= 2:
                recent_avg = sum(scores[-3:]) / len(scores[-3:]) if len(scores) >= 3 else scores[-1]
                early_avg = sum(scores[:3]) / len(scores[:3]) if len(scores) >= 3 else scores[0]
                
                if recent_avg > early_avg + 5:
                    trend = "↑ Rising"
                elif recent_avg < early_avg - 5:
                    trend = "↓ Falling"
                else:
                    trend = "→ Stable"
            else:
                trend = "→ Limited"
            
            "color: #6b7280;"># Risk assessment
            if avg < 60:
                risk = "🔴 Critical"
                overall_at_risk = True
            elif avg < 70:
                risk = "⚠️ Warning"
                overall_at_risk = True
            elif avg < 80:
                risk = "○ Monitor"
            else:
                risk = "✓ Good"
            
            report += f"{subject:<20} {avg:>9.1f}% {trend:>10} {risk:>15}\n"
        
        report += "\n=== RECOMMENDATIONS ===\n"
        if overall_at_risk:
            report += "⚠️ Student requires academic intervention\n"
            report += "Actions:\n"
            report += "  1. Schedule parent-teacher conference\n"
            report += "  2. Develop individualized support plan\n"
            report += "  3. Increase monitoring frequency to weekly\n"
            report += "  4. Consider tutoring or peer mentoring\n"
        else:
            report += "✓ Student is performing adequately\n"
            report += "Continue current monitoring schedule\n"
        
        return report
        
    except Exception as e:
        return f"Error calculating grade trends: {str(e)}"

def track_attendance_patterns(attendance_data: str) -> str:
    """
    Track and analyze attendance patterns to identify chronic absenteeism.
    
    Args:
        attendance_data: JSON string with attendance records
        Example: {"student_id": "12345", "records": [{"date": "2026-01-15", "status": "present"}, ...]}
    
    Returns:
        Attendance pattern analysis with intervention recommendations
    """
    try:
        if isinstance(attendance_data, str):
            try:
                data = json.loads(attendance_data)
            except json.JSONDecodeError:
                return "Error: Invalid JSON format for attendance data"
        else:
            data = attendance_data
        
        student_id = data.get('student_id', 'Unknown')
        records = data.get('records', [])
        
        if not records:
            return "Error: No attendance records provided"
        
        total_days = len(records)
        present = sum(1 for r in records if r.get('status') == 'present')
        absent = sum(1 for r in records if r.get('status') == 'absent')
        excused = sum(1 for r in records if r.get('status') == 'excused')
        tardy = sum(1 for r in records if r.get('status') == 'tardy')
        
        attendance_rate = (present / total_days) * 100 if total_days > 0 else 0
        
        report = f"=== ATTENDANCE PATTERN ANALYSIS ===\n\n"
        report += f"Student ID: {student_id}\n"
        report += f"Analysis Period: {total_days} school days\n\n"
        
        report += f"{'Status':<20} {'Count':>10} {'Percentage':>12}\n"
        report += "-" * 45 + "\n"
        report += f"{'Present':<20} {present:>10} {(present/total_days)*100:>11.1f}%\n"
        report += f"{'Absent(Unexcused)':<20} {absent:>10} {(absent/total_days)*100:>11.1f}%\n"
        report += f"{'Excused':<20} {excused:>10} {(excused/total_days)*100:>11.1f}%\n"
        report += f"{'Tardy':<20} {tardy:>10} {(tardy/total_days)*100:>11.1f}%\n"
        
        report += f"\n=== ATTENDANCE RATE ===\n"
        report += f"Overall: {attendance_rate:.1f}%\n\n"
        
        "color: #6b7280;"># Risk assessment
        report += "=== RISK ASSESSMENT ===\n"
        if attendance_rate < 85:
            report += "🔴 CRITICAL: Chronic absenteeism detected\n"
            report += "Student is at high risk for academic failure\n\n"
            report += "IMMEDIATE ACTIONS REQUIRED:\n"
            report += "  1. Contact family to identify barriers to attendance\n"
            report += "  2. Develop attendance improvement plan\n"
            report += "  3. Connect family with support services\n"
            report += "  4. Daily attendance monitoring\n"
            report += "  5. Consider truancy intervention if pattern continues\n"
        elif attendance_rate < 90:
            report += "⚠️ WARNING: Below-target attendance\n"
            report += "Actions:\n"
            report += "  1. Parent notification and conference\n"
            report += "  2. Identify attendance barriers\n"
            report += "  3. Weekly monitoring\n"
        elif attendance_rate < 95:
            report += "○ MONITOR: Acceptable but room for improvement\n"
            report += "Continue standard monitoring\n"
        else:
            report += "✓ EXCELLENT: Strong attendance pattern\n"
            report += "Student demonstrates strong commitment to learning\n"
        
        return report
        
    except Exception as e:
        return f"Error tracking attendance patterns: {str(e)}"