Grading Assistant

def apply_rubric_grading(assignment_data: str, rubric: str) -> str:
    """
    Apply a grading rubric to student work and generate detailed feedback.
    
    Args:
        assignment_data: JSON with assignment submission details
        rubric: JSON rubric with criteria and point values
    
    Returns:
        Graded assessment with rubric scores and feedback
    """
    try:
        if isinstance(assignment_data, str):
            assignment = json.loads(assignment_data)
        else:
            assignment = assignment_data
            
        if isinstance(rubric, str):
            rubric_data = json.loads(rubric)
        else:
            rubric_data = rubric
        
        student_name = assignment.get('student_name', 'Unknown')
        assignment_title = assignment.get('title', 'Assignment')
        criteria_scores = assignment.get('criteria_scores', {})
        
        rubric_criteria = rubric_data.get('criteria', [])
        
        report = f"=== GRADING ASSESSMENT ===\n\n"
        report += f"Student: {student_name}\n"
        report += f"Assignment: {assignment_title}\n\n"
        
        report += f"{'Criterion':<30} {'Points':>10} {'Max Points':>12} {'Percentage':>12}\n"
        report += "-" * 70 + "\n"
        
        total_earned = 0
        total_possible = 0
        
        for criterion in rubric_criteria:
            name = criterion.get('name', 'Unknown')
            max_points = criterion.get('max_points', 0)
            earned = criteria_scores.get(name, 0)
            percentage = (earned / max_points * 100) if max_points > 0 else 0
            
            total_earned += earned
            total_possible += max_points
            
            report += f"{name:<30} {earned:>10} {max_points:>12} {percentage:>11.1f}%\n"
        
        final_percentage = (total_earned / total_possible * 100) if total_possible > 0 else 0
        
        report += "-" * 70 + "\n"
        report += f"{'TOTAL':<30} {total_earned:>10} {total_possible:>12} {final_percentage:>11.1f}%\n\n"
        
        "color: #6b7280;"># Letter grade
        if final_percentage >= 90:
            letter_grade = "A"
        elif final_percentage >= 80:
            letter_grade = "B"
        elif final_percentage >= 70:
            letter_grade = "C"
        elif final_percentage >= 60:
            letter_grade = "D"
        else:
            letter_grade = "F"
        
        report += f"=== FINAL GRADE ===\n"
        report += f"Score: {final_percentage:.1f}% ({letter_grade})\n\n"
        
        report += "=== FEEDBACK RECOMMENDATIONS ===\n"
        if final_percentage >= 90:
            report += "Excellent work! Consider highlighting specific strengths.\n"
        elif final_percentage >= 70:
            report += "Good effort. Provide specific suggestions for improvement in lower-scoring areas.\n"
        else:
            report += "⚠️ Below standards. Schedule student conference and provide detailed improvement plan.\n"
        
        return report
        
    except Exception as e:
        return f"Error applying rubric grading: {str(e)}"

def calculate_class_statistics(grades: str) -> str:
    """
    Calculate class-wide grade statistics and identify outliers.
    
    Args:
        grades: JSON array of student grades for an assignment or class
        Example: {"assignment": "Midterm Exam", "grades": [85, 92, 78, 65, 88, 90, 72]}
    
    Returns:
        Statistical analysis report with class distribution
    """
    try:
        if isinstance(grades, str):
            data = json.loads(grades)
        else:
            data = grades
        
        assignment = data.get('assignment', 'Assignment')
        grade_list = data.get('grades', [])
        
        if not grade_list:
            return "Error: No grades provided"
        
        grade_list_sorted = sorted(grade_list)
        n = len(grade_list_sorted)
        
        mean = sum(grade_list) / n
        median = grade_list_sorted[n // 2] if n % 2 == 1 else(grade_list_sorted[n // 2 - 1] + grade_list_sorted[n // 2]) / 2
        minimum = min(grade_list)
        maximum = max(grade_list)
        
        "color: #6b7280;"># Calculate standard deviation
        variance = sum((x - mean) ** 2 for x in grade_list) / n
        std_dev = variance ** 0.5
        
        "color: #6b7280;"># Grade distribution
        distribution = {'A': 0, 'B': 0, 'C': 0, 'D': 0, 'F': 0}
        for grade in grade_list:
            if grade >= 90:
                distribution['A'] += 1
            elif grade >= 80:
                distribution['B'] += 1
            elif grade >= 70:
                distribution['C'] += 1
            elif grade >= 60:
                distribution['D'] += 1
            else:
                distribution['F'] += 1
        
        report = f"=== CLASS GRADE STATISTICS ===\n\n"
        report += f"Assignment: {assignment}\n"
        report += f"Total Students: {n}\n\n"
        
        report += "=== DESCRIPTIVE STATISTICS ===\n"
        report += f"Mean(Average): {mean:.2f}%\n"
        report += f"Median: {median:.2f}%\n"
        report += f"Standard Deviation: {std_dev:.2f}\n"
        report += f"Minimum: {minimum:.2f}%\n"
        report += f"Maximum: {maximum:.2f}%\n"
        report += f"Range: {maximum - minimum:.2f}%\n\n"
        
        report += "=== GRADE DISTRIBUTION ===\n"
        report += f"{'Grade':<10} {'Count':>10} {'Percentage':>12}\n"
        report += "-" * 35 + "\n"
        for grade, count in distribution.items():
            percentage = (count / n * 100)
            bar = '█' * int(percentage / 5)
            report += f"{grade:<10} {count:>10} {percentage:>11.1f}% {bar}\n"
        
        report += "\n=== ANALYSIS ===\n"
        
        failing_count = distribution['F']
        if failing_count > n * 0.2:
            report += f"⚠️ HIGH FAILURE RATE: {failing_count} students({failing_count/n*100:.1f}%)\n"
            report += "Recommendations:\n"
            report += "  - Review assignment difficulty and clarity\n"
            report += "  - Consider offering reassessment opportunity\n"
            report += "  - Schedule review session for struggling students\n\n"
        
        if std_dev > 20:
            report += "⚠️ HIGH VARIABILITY in student performance\n"
            report += "Recommendations:\n"
            report += "  - Differentiated instruction may be needed\n"
            report += "  - Identify students needing additional support\n\n"
        
        if mean >= 85:
            report += "✓ Strong class performance overall\n"
        elif mean < 70:
            report += "⚠️ Below-target class average - instructional review recommended\n"
        
        return report
        
    except Exception as e:
        return f"Error calculating class statistics: {str(e)}"