Ajout de la vue stats_charts avec graphiques détaillés pour les superadministrateurs, mise à jour des templates et des routes associées, et configuration supplémentaire pour l'email backend.

home/urls.py

@@ -7,4 +7,5 @@ urlpatterns = [
     path('premium/<int:course_id>', views.premium, name='premium'),
     # Tableau de bord statistiques (réservé superadministrateurs)
     path('dashboard/stats/', views.stats_dashboard, name='stats_dashboard'),
+    path('dashboard/stats/charts/', views.stats_charts, name='stats_charts'),
 ]

home/views.py

@@ -199,3 +199,97 @@ def stats_dashboard(request):
     context['labels_json'] = json.dumps(context['series']['days'])
 
     return render(request, 'home/stats_dashboard.html', context)
+
+
+@user_passes_test(lambda u: u.is_superuser)
+@cache_page(60 * 15)
+def stats_charts(request):
+    """Page dédiée aux graphiques (réservée superadmins)."""
+    # Période
+    period_options = [7, 30, 90, 180]
+    try:
+        p = int(request.GET.get('p', 30))
+    except ValueError:
+        p = 30
+    if p not in period_options:
+        p = 30
+
+    now = timezone.now()
+    start_dt = now - timezone.timedelta(days=p-1)
+    period_start_date = start_dt.date()
+    period_end_date = now.date()
+
+    # Trafic par jour (visiteurs uniques)
+    visits_qs = (
+        Visit.objects
+        .filter(date__gte=period_start_date, date__lte=period_end_date)
+        .values('date')
+        .annotate(c=Count('visitor_id', distinct=True))
+        .order_by('date')
+    )
+
+    # Conversions par jour (visiteurs devenus utilisateurs)
+    conversions_qs = (
+        Visit.objects
+        .filter(
+            became_user_at__isnull=False,
+            became_user_at__date__gte=period_start_date,
+            became_user_at__date__lte=period_end_date,
+        )
+        .extra(select={'day': "date(became_user_at)"})
+        .values('day')
+        .annotate(c=Count('visitor_id', distinct=True))
+        .order_by('day')
+    )
+
+    def build_series_dict(qs, date_key='date', count_key='c'):
+        counts = {str(item[date_key]): item[count_key] for item in qs}
+        days = []
+        values = []
+        d = period_start_date
+        while d <= period_end_date:
+            key = str(d)
+            days.append(key)
+            values.append(counts.get(key, 0))
+            d += timezone.timedelta(days=1)
+        return days, values
+
+    labels, visitors_series = build_series_dict(visits_qs, date_key='date')
+    _, conversions_series = build_series_dict(conversions_qs, date_key='day')
+
+    # Sources & Pays (sur la période)
+    period_visits = Visit.objects.filter(date__gte=period_start_date, date__lte=period_end_date)
+    top_sources_qs = (
+        period_visits
+        .values('source')
+        .annotate(c=Count('visitor_id', distinct=True))
+        .order_by('-c')[:10]
+    )
+    top_countries_qs = (
+        period_visits
+        .exclude(country='')
+        .values('country')
+        .annotate(c=Count('visitor_id', distinct=True))
+        .order_by('-c')[:10]
+    )
+
+    sources_labels = [(row['source'] or 'Direct/Unknown') for row in top_sources_qs]
+    sources_values = [row['c'] for row in top_sources_qs]
+    countries_labels = [row['country'] for row in top_countries_qs]
+    countries_values = [row['c'] for row in top_countries_qs]
+
+    context = {
+        'period_options': period_options,
+        'p': p,
+        'start_date': period_start_date,
+        'end_date': period_end_date,
+        'labels_json': json.dumps(labels),
+        'visitors_series_json': json.dumps(visitors_series),
+        'conversions_series_json': json.dumps(conversions_series),
+        'sources_labels_json': json.dumps(sources_labels),
+        'sources_values_json': json.dumps(sources_values),
+        'countries_labels_json': json.dumps(countries_labels),
+        'countries_values_json': json.dumps(countries_values),
+    }
+
+    return render(request, 'home/stats_charts.html', context)