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credits adn tokens final correct setup

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IGNY8 VPS (Salman)
2025-12-20 00:36:23 +00:00
parent e041cb8e65
commit c17b22e927
13 changed files with 1170 additions and 233 deletions
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64
backend/igny8_core/admin/reports.py
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@@ -82,6 +82,10 @@ def usage_report(request):
operation_count=Count('id')
).order_by('-total_credits')
# Format operation types as Title Case
for usage in usage_by_operation:
usage['operation_type'] = usage['operation_type'].replace('_', ' ').title() if usage['operation_type'] else 'Unknown'
# Top credit consumers
top_consumers = CreditUsageLog.objects.values(
'account__name'
@@ -269,11 +273,9 @@ def token_usage_report(request):
start_date = timezone.now() - timedelta(days=days)
# Base queryset - filter for records with token data
# Base queryset - include all records (tokens may be 0 for historical data)
logs = CreditUsageLog.objects.filter(
created_at__gte=start_date,
tokens_input__isnull=False,
tokens_output__isnull=False
created_at__gte=start_date
)
# Total statistics
@@ -310,7 +312,8 @@ def token_usage_report(request):
for func in token_by_function:
func['total_tokens'] = (func['total_tokens_input'] or 0) + (func['total_tokens_output'] or 0)
func['avg_tokens'] = func['total_tokens'] / func['call_count'] if func['call_count'] > 0 else 0
func['function'] = func['operation_type'] # Add alias for template
# Format operation_type as Title Case
func['function'] = func['operation_type'].replace('_', ' ').title() if func['operation_type'] else 'Unknown'
token_by_function = sorted(token_by_function, key=lambda x: x['total_tokens'], reverse=True)
# Token usage by account (top consumers)
@@ -366,7 +369,7 @@ def token_usage_report(request):
# Cost efficiency
total_cost = logs.aggregate(total=Sum('cost_usd'))['total'] or Decimal('0.00')
cost_per_1k_tokens = (total_cost / (total_tokens / 1000)) if total_tokens > 0 else Decimal('0.00')
cost_per_1k_tokens = float(total_cost) / (total_tokens / 1000) if total_tokens > 0 else 0.0
context = {
'title': 'Token Usage Report',
@@ -427,6 +430,20 @@ def ai_cost_analysis(request):
total_tokens_output = logs.aggregate(total=Sum('tokens_output'))['total'] or 0
total_tokens = total_tokens_input + total_tokens_output
# Revenue & Margin calculation
from igny8_core.business.billing.models import BillingConfiguration
billing_config = BillingConfiguration.get_config()
total_credits_charged = logs.aggregate(total=Sum('credits_used'))['total'] or 0
total_revenue = Decimal(total_credits_charged) * billing_config.default_credit_price_usd
total_margin = total_revenue - total_cost
margin_percentage = float((total_margin / total_revenue * 100) if total_revenue > 0 else 0)
# Per-unit margins
# Calculate per 1M tokens (margin per million tokens)
margin_per_1m_tokens = float(total_margin) / (total_tokens / 1_000_000) if total_tokens > 0 else 0
# Calculate per 1K credits (margin per thousand credits)
margin_per_1k_credits = float(total_margin) / (total_credits_charged / 1000) if total_credits_charged > 0 else 0
# Cost by model with efficiency metrics
cost_by_model = logs.values('model_used').annotate(
total_cost=Sum('cost_usd'),
@@ -436,7 +453,7 @@ def ai_cost_analysis(request):
total_tokens_output=Sum('tokens_output')
).order_by('-total_cost')
# Add cost efficiency (cost per 1K tokens) for each model
# Add cost efficiency and margin for each model
for model in cost_by_model:
model['total_tokens'] = (model['total_tokens_input'] or 0) + (model['total_tokens_output'] or 0)
model['avg_tokens'] = model['total_tokens'] / model['call_count'] if model['call_count'] > 0 else 0
@@ -445,6 +462,14 @@ def ai_cost_analysis(request):
model['cost_per_1k_tokens'] = float(model['total_cost']) / (model['total_tokens'] / 1000)
else:
model['cost_per_1k_tokens'] = 0
# Calculate margin for this model
model_credits = logs.filter(model_used=model['model_used']).aggregate(total=Sum('credits_used'))['total'] or 0
model_revenue = Decimal(model_credits) * billing_config.default_credit_price_usd
model_margin = model_revenue - model['total_cost']
model['revenue'] = float(model_revenue)
model['margin'] = float(model_margin)
model['margin_percentage'] = float((model_margin / model_revenue * 100) if model_revenue > 0 else 0)
# Cost by account (top spenders)
cost_by_account = logs.values('account__name', 'account_id').annotate(
@@ -468,10 +493,19 @@ def ai_cost_analysis(request):
total_tokens_output=Sum('tokens_output')
).order_by('-total_cost')[:10]
# Add total_tokens and function alias
# Add total_tokens, function alias, and margin
for func in cost_by_function:
func['total_tokens'] = (func['total_tokens_input'] or 0) + (func['total_tokens_output'] or 0)
func['function'] = func['operation_type'] # Add alias for template
# Format operation_type as Title Case
func['function'] = func['operation_type'].replace('_', ' ').title() if func['operation_type'] else 'Unknown'
# Calculate margin for this operation
func_credits = logs.filter(operation_type=func['operation_type']).aggregate(total=Sum('credits_used'))['total'] or 0
func_revenue = Decimal(func_credits) * billing_config.default_credit_price_usd
func_margin = func_revenue - func['total_cost']
func['revenue'] = float(func_revenue)
func['margin'] = float(func_margin)
func['margin_percentage'] = float((func_margin / func_revenue * 100) if func_revenue > 0 else 0)
# Daily cost trends (time series)
daily_cost_data = []
@@ -507,7 +541,8 @@ def ai_cost_analysis(request):
# Add aliases and calculate total tokens for each anomaly
for anomaly in anomalies:
anomaly['model'] = anomaly['model_used']
anomaly['function'] = anomaly['operation_type']
# Format operation_type as Title Case
anomaly['function'] = anomaly['operation_type'].replace('_', ' ').title() if anomaly['operation_type'] else 'Unknown'
anomaly['cost'] = anomaly['cost_usd']
anomaly['tokens'] = (anomaly['tokens_input'] or 0) + (anomaly['tokens_output'] or 0)
else:
@@ -544,9 +579,16 @@ def ai_cost_analysis(request):
efficiency_score = 100.0
context = {
'title': 'AI Cost Analysis',
'title': 'AI Cost & Margin Analysis',
'days_filter': days,
'total_cost': float(total_cost),
'total_revenue': float(total_revenue),
'total_margin': float(total_margin),
'margin_percentage': round(margin_percentage, 2),
'margin_per_1m_tokens': round(margin_per_1m_tokens, 4),
'margin_per_1k_credits': round(margin_per_1k_credits, 4),
'total_credits_charged': total_credits_charged,
'credit_price': float(billing_config.default_credit_price_usd),
'total_calls': total_calls,
'avg_cost_per_call': float(avg_cost_per_call),
'total_tokens': int(total_tokens),