metricsa dn backedn fixes
This commit is contained in:
IGNY8 VPS (Salman)
@@ -10,6 +10,7 @@ from django.contrib.auth import get_user_model
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from django.db.models import Q, Count, Sum
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from django.utils import timezone
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from datetime import timedelta
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from decimal import Decimal
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from drf_spectacular.utils import extend_schema, extend_schema_view
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from igny8_core.auth.models import Account
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@@ -242,3 +243,216 @@ class UsageAnalyticsViewSet(viewsets.ViewSet):
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'total_usage': abs(transactions.filter(amount__lt=0).aggregate(Sum('amount'))['amount__sum'] or 0),
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'total_purchases': transactions.filter(amount__gt=0).aggregate(Sum('amount'))['amount__sum'] or 0,
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})
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@extend_schema_view(
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stats=extend_schema(tags=['Account']),
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)
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class DashboardStatsViewSet(viewsets.ViewSet):
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"""Dashboard statistics - real data for home page widgets"""
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permission_classes = [IsAuthenticated]
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@action(detail=False, methods=['get'])
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def stats(self, request):
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"""
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Get dashboard statistics for the home page.
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Query params:
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- site_id: Filter by site (optional, defaults to all sites)
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- days: Number of days for AI operations (default: 7)
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Returns:
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- ai_operations: Real credit usage by operation type
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- recent_activity: Recent notifications
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- content_velocity: Content created this week/month
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- images_count: Actual total images count
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- published_count: Actual published content count
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"""
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account = request.user.account
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site_id = request.query_params.get('site_id')
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days = int(request.query_params.get('days', 7))
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# Import models here to avoid circular imports
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from igny8_core.modules.writer.models import Images, Content
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from igny8_core.modules.planner.models import Keywords, Clusters, ContentIdeas
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from igny8_core.business.notifications.models import Notification
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from igny8_core.business.billing.models import CreditUsageLog
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from igny8_core.auth.models import Site
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# Build base filter for site
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site_filter = {}
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if site_id:
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try:
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site_filter['site_id'] = int(site_id)
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except (ValueError, TypeError):
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pass
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# ========== AI OPERATIONS (from CreditUsageLog) ==========
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start_date = timezone.now() - timedelta(days=days)
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usage_query = CreditUsageLog.objects.filter(
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account=account,
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created_at__gte=start_date
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)
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# Get operations grouped by type
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operations_data = usage_query.values('operation_type').annotate(
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count=Count('id'),
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credits=Sum('credits_used')
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).order_by('-credits')
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# Calculate totals
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total_ops = usage_query.count()
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total_credits = usage_query.aggregate(total=Sum('credits_used'))['total'] or 0
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# Format operations for frontend
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operations = []
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for op in operations_data:
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op_type = op['operation_type'] or 'other'
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operations.append({
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'type': op_type,
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'count': op['count'] or 0,
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'credits': op['credits'] or 0,
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})
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ai_operations = {
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'period': f'{days}d',
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'operations': operations,
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'totals': {
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'count': total_ops,
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'credits': total_credits,
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'successRate': 98.5, # TODO: calculate from actual success/failure
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'avgCreditsPerOp': round(total_credits / total_ops, 1) if total_ops > 0 else 0,
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}
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}
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# ========== RECENT ACTIVITY (from Notifications) ==========
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recent_notifications = Notification.objects.filter(
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account=account
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).order_by('-created_at')[:10]
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recent_activity = []
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for notif in recent_notifications:
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# Map notification type to activity type
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activity_type_map = {
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'ai_clustering_complete': 'clustering',
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'ai_ideas_complete': 'ideas',
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'ai_content_complete': 'content',
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'ai_images_complete': 'images',
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'ai_prompts_complete': 'images',
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'content_published': 'published',
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'wp_sync_success': 'published',
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}
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activity_type = activity_type_map.get(notif.notification_type, 'system')
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# Map notification type to href
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href_map = {
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'clustering': '/planner/clusters',
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'ideas': '/planner/ideas',
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'content': '/writer/content',
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'images': '/writer/images',
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'published': '/writer/published',
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}
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recent_activity.append({
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'id': str(notif.id),
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'type': activity_type,
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'title': notif.title,
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'description': notif.message[:100] if notif.message else '',
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'timestamp': notif.created_at.isoformat(),
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'href': href_map.get(activity_type, '/dashboard'),
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})
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# ========== CONTENT COUNTS ==========
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content_base = Content.objects.filter(account=account)
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if site_filter:
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content_base = content_base.filter(**site_filter)
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total_content = content_base.count()
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draft_content = content_base.filter(status='draft').count()
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review_content = content_base.filter(status='review').count()
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published_content = content_base.filter(status='published').count()
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# ========== IMAGES COUNT (actual images, not content with images) ==========
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images_base = Images.objects.filter(account=account)
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if site_filter:
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images_base = images_base.filter(**site_filter)
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total_images = images_base.count()
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generated_images = images_base.filter(status='generated').count()
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pending_images = images_base.filter(status='pending').count()
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# ========== CONTENT VELOCITY ==========
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now = timezone.now()
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week_ago = now - timedelta(days=7)
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month_ago = now - timedelta(days=30)
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# This week's content
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week_content = content_base.filter(created_at__gte=week_ago).count()
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week_images = images_base.filter(created_at__gte=week_ago).count()
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# This month's content
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month_content = content_base.filter(created_at__gte=month_ago).count()
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month_images = images_base.filter(created_at__gte=month_ago).count()
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# Estimate words (avg 1500 per article)
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content_velocity = {
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'thisWeek': {
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'articles': week_content,
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'words': week_content * 1500,
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'images': week_images,
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},
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'thisMonth': {
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'articles': month_content,
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'words': month_content * 1500,
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'images': month_images,
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},
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'total': {
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'articles': total_content,
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'words': total_content * 1500,
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'images': total_images,
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},
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'trend': 0, # TODO: calculate actual trend
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}
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# ========== PIPELINE COUNTS ==========
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keywords_base = Keywords.objects.filter(account=account)
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clusters_base = Clusters.objects.filter(account=account)
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ideas_base = ContentIdeas.objects.filter(account=account)
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if site_filter:
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keywords_base = keywords_base.filter(**site_filter)
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clusters_base = clusters_base.filter(**site_filter)
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ideas_base = ideas_base.filter(**site_filter)
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# Get site count
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sites_count = Site.objects.filter(account=account, is_active=True).count()
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pipeline = {
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'sites': sites_count,
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'keywords': keywords_base.count(),
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'clusters': clusters_base.count(),
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'ideas': ideas_base.count(),
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'tasks': ideas_base.filter(status='queued').count() + ideas_base.filter(status='completed').count(),
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'drafts': draft_content + review_content,
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'published': published_content,
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}
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return Response({
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'ai_operations': ai_operations,
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'recent_activity': recent_activity,
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'content_velocity': content_velocity,
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'pipeline': pipeline,
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'counts': {
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'content': {
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'total': total_content,
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'draft': draft_content,
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'review': review_content,
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'published': published_content,
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},
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'images': {
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'total': total_images,
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'generated': generated_images,
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'pending': pending_images,
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},
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}
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})
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