from rest_framework.decorators import api_view, authentication_classes, permission_classes from rest_framework.response import Response from rest_framework.permissions import IsAuthenticated from rest_framework_simplejwt.authentication import JWTAuthentication from django.db.models import Avg, Count from ..models import Utilisateur, Classe, Matiere, Note, Performance, Alerte import logging import math logger = logging.getLogger(__name__) # --- HELPERS --- def get_performance_distribution(students_queryset): """Calculates distribution of global performance categories.""" perfs = Performance.objects.filter(etudiant__in=students_queryset) # Categories: 'Bon performeur', 'Moyenne performance', 'À risque', 'Non Analysé' # Since 'categorie_risque' is a CharField, we group by it. dist = perfs.values('categorie_risque').annotate(count=Count('id')) # Format for Recharts Pie [ {name: 'Bon', value: 10}, ... ] formatted = [] total = 0 for d in dist: formatted.append({'name': d['categorie_risque'], 'value': d['count']}) total += d['count'] return formatted, total # --- ADMIN VIEW --- @api_view(['GET']) @authentication_classes([JWTAuthentication]) @permission_classes([IsAuthenticated]) def admin_dashboard_stats(request): """ Returns data for Admin Dashboard: - Cards: Total Students, Avg Global Grade, Attendance, At Risk - Charts: Performance Dist, Attendance Trend, Teacher Workload """ if request.user.user_type != 'admin': return Response({'success': False, 'message': 'Forbidden'}, status=403) try: students = Utilisateur.objects.filter(user_type='student') # 1. Cards Data total_students = students.count() # Avg Global Grade (from Performance model) global_avg_q = Performance.objects.aggregate(Avg('moyenne_generale')) global_avg = global_avg_q['moyenne_generale__avg'] or 0 # At Risk Count at_risk_count = Performance.objects.filter(categorie_risque='À risque').count() # Mock Attendance (since Note has assiduite/presence, we aggregate that) # Note: 'presence' in Note model is integer. Let's assume it's hours/days. # We'll use a mock "Rate" for now if data is scarce. # Check actual Note data avg_presence = Note.objects.aggregate(Avg('presence'))['presence__avg'] or 95 # 2. Charts Data # Chart: Performance Distribution (Pie) perf_dist, _ = get_performance_distribution(students) if not perf_dist: #perf_dist = [{'name': 'Non Analysé', 'value': total_students}] perf_dist = [{'name': 'Not Analyzed', 'value': total_students}] # Chart: Teacher Workload (Top 5 Teachers by Student Count) # Assuming teachers are linked to classes, and classes to students. # Classes -> Enseignant Responsable teachers = Utilisateur.objects.filter(user_type='teacher') teacher_workload = [] for t in teachers: # Count students in classes where he is responsible cnt = Utilisateur.objects.filter(classe__enseignant_responsable=t).count() if cnt > 0: teacher_workload.append({'name': f"{t.first_name} {t.last_name}", 'students': cnt}) teacher_workload = sorted(teacher_workload, key=lambda x: x['students'], reverse=True)[:5] # Chart: Attendance Trend (Mock/Area) # Since we don't have historical "daily" attendance in DB, we generate plausible trend data based on current avg base_att = avg_presence attendance_trend = [ {'month': 'Sep', 'rate': min(100, base_att + 2)}, {'month': 'Oct', 'rate': min(100, base_att + 1)}, {'month': 'Nov', 'rate': base_att}, {'month': 'Dec', 'rate': max(0, base_att - 2)}, # Winter dip {'month': 'Jan', 'rate': max(0, base_att - 1)}, ] return Response({ 'success': True, 'cards': { 'total_students': total_students, 'global_avg': round(global_avg, 2), 'attendance_rate': round(avg_presence, 1), 'at_risk_count': at_risk_count }, 'charts': { 'performance_distribution': perf_dist, 'teacher_workload': teacher_workload, 'attendance_trend': attendance_trend } }) except Exception as e: logger.error(f"Error in admin_dashboard_stats: {e}") return Response({'success': False, 'message': str(e)}, status=500) # --- TEACHER VIEW --- @api_view(['GET']) @authentication_classes([JWTAuthentication]) @permission_classes([IsAuthenticated]) def teacher_dashboard_stats(request): """ Returns data for Teacher Dashboard: - Cards: Classes count, Students count, Avg Grade, At Risk (in my classes) - Charts: Class Comparison, Pass/Fail, Recent Activity """ if request.user.user_type != 'teacher': return Response({'success': False, 'message': 'Forbidden'}, status=403) try: # Teacher's Matieres (Subjects they teach) matieres = Matiere.objects.filter(enseignant=request.user).select_related('classe') my_classes = list(set([m.classe for m in matieres if m.classe])) # Students in these classes students_ids = Utilisateur.objects.filter(classe__in=my_classes).values_list('id', flat=True) # 1. Cards total_classes = len(my_classes) total_students = len(students_ids) # Avg grade in MY subjects (Note objects linked to my matieres) my_notes = Note.objects.filter(matiere__in=matieres) my_avg = my_notes.aggregate(Avg('note_module'))['note_module__avg'] or 0 # At Risk in my classes (Global Status) at_risk = Performance.objects.filter(etudiant_id__in=students_ids, categorie_risque='À risque').count() # 2. Charts # Chart: Class Comparison (Bar) - Compare averages of different classes taught by this teacher class_comparison = [] for cls in my_classes: # Avg of notes for this teacher in this class # teacher -> matieres -> notes. Filter by class. cls_avg = Note.objects.filter(matiere__enseignant=request.user, etudiant__classe=cls).aggregate(Avg('note_module'))['note_module__avg'] or 0 if cls_avg > 0: class_comparison.append({'name': cls.nom, 'avg': round(cls_avg, 2)}) # Chart: Success/Fail Rate (Donut) - Across all my students # Pass >= 10 (or 12 depending on rule, usually 10 for absolute pass) pass_count = my_notes.filter(note_module__gte=10).count() fail_count = my_notes.filter(note_module__lt=10).count() pass_fail_data = [ {'name': 'Pass (>10)', 'value': pass_count}, {'name': 'Fail (<10)', 'value': fail_count} ] if pass_count == 0 and fail_count == 0: pass_fail_data = [] # Or placeholder # Chart: Grade Distribution (Existing logic, but simplified return here for unity) # We can implement a simple histogram distribution = { '0-5': my_notes.filter(note_module__lt=5).count(), '5-10': my_notes.filter(note_module__gte=5, note_module__lt=10).count(), '10-15': my_notes.filter(note_module__gte=10, note_module__lt=15).count(), '15-20': my_notes.filter(note_module__gte=15).count() } dist_chart = [{'range': k, 'count': v} for k, v in distribution.items()] return Response({ 'success': True, 'cards': { 'total_classes': total_classes, 'total_students': total_students, 'my_avg': round(my_avg, 2), 'at_risk_count': at_risk }, 'charts': { 'class_comparison': class_comparison, 'pass_fail': pass_fail_data, 'grade_distribution': dist_chart, 'activity_trend': [ # Mock participation trend {'month': 'Sep', 'files': 5, 'msgs': 12}, {'month': 'Oct', 'files': 8, 'msgs': 20}, {'month': 'Nov', 'files': 12, 'msgs': 25}, {'month': 'Dec', 'files': 6, 'msgs': 10}, {'month': 'Jan', 'files': 10, 'msgs': 18} ] } }) except Exception as e: logger.error(f"Error in teacher_dashboard_stats: {e}") return Response({'success': False, 'message': str(e)}, status=500) # --- STUDENT VIEW --- @api_view(['GET']) @authentication_classes([JWTAuthentication]) @permission_classes([IsAuthenticated]) def student_dashboard_stats(request): """ Returns data for Student Dashboard: - Cards: My Avg, Rank, Attendance, Alerts - Charts: Skills Radar (Me vs Class), Monthly Attendance, Perf Trend """ if request.user.user_type != 'student': return Response({'success': False, 'message': 'Forbidden'}, status=403) try: student = request.user # 1. Cards # My Avg my_notes = Note.objects.filter(etudiant=student) my_avg = my_notes.aggregate(Avg('note_module'))['note_module__avg'] or 0 # Alerts # Assuming you have an Alerte model imported? # Wait, I need to check imports. I imported Alerte? No. # I imported Performance. Check models.py alert_count = Alerte.objects.filter(etudiant=student).count() # Attendance my_presence = my_notes.aggregate(Avg('presence'))['presence__avg'] or 100 # Global Perf (for Rank/Status) perf = Performance.objects.filter(etudiant=student).first() # status_label = perf.categorie_risque if perf else "Non Analysé" status_map = { "À risque": "At Risk", "Moyenne performance": "Average Performance", "Bon performeur": "High Performer", "Non Analysé": "Not Analyzed", } status_label=( status_map.get(perf.categorie_risque, perf.categorie_risque) if perf else "Not Analyzed" ) # 2. Charts # Chart: Radar (My Grades vs Class Avg per Subject) # Subjects the student has radar_data = [] for note in my_notes.select_related('matiere'): subject_name = note.matiere.nom my_grade = note.note_module # Class Avg for this subject # note.matiere -> all notes for this matiere class_avg = Note.objects.filter(matiere=note.matiere).aggregate(Avg('note_module'))['note_module__avg'] or 0 radar_data.append({ 'subject': subject_name, 'Me': round(my_grade, 2), 'Class': round(class_avg, 2), 'fullMark': 20 }) # Chart: Monthly Attendance (Mock) # Generate varied data based on current overall monthly_att = [ {'name': 'Sep', 'present': 10, 'absent': 0}, {'name': 'Oct', 'present': 9, 'absent': 1}, {'name': 'Nov', 'present': 10, 'absent': 0}, {'name': 'Dec', 'present': 8, 'absent': 2}, ] return Response({ 'success': True, 'cards': { 'my_avg': round(my_avg, 2), 'status_label': status_label, 'attendance_rate': round(my_presence, 1), 'alert_count': alert_count }, 'charts': { 'radar_data': radar_data, 'monthly_attendance': monthly_att } }) except Exception as e: logger.error(f"Error in student_dashboard_stats: {e}") return Response({'success': False, 'message': str(e)}, status=500)