New Model & tokens/credits updates
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IGNY8 VPS (Salman)
@@ -3,7 +3,9 @@ Credit Service for managing credit transactions and deductions
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"""
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from django.db import transaction
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from django.utils import timezone
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from igny8_core.business.billing.models import CreditTransaction, CreditUsageLog
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from decimal import Decimal
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import math
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from igny8_core.business.billing.models import CreditTransaction, CreditUsageLog, AIModelConfig
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from igny8_core.business.billing.constants import CREDIT_COSTS
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from igny8_core.business.billing.exceptions import InsufficientCreditsError, CreditCalculationError
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from igny8_core.auth.models import Account
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@@ -117,7 +119,10 @@ class CreditService:
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@staticmethod
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@transaction.atomic
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def deduct_credits(account, amount, operation_type, description, metadata=None, cost_usd=None, model_used=None, tokens_input=None, tokens_output=None, related_object_type=None, related_object_id=None):
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def deduct_credits(account, amount, operation_type, description, metadata=None,
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cost_usd_input=None, cost_usd_output=None, cost_usd_total=None,
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model_config=None, tokens_input=None, tokens_output=None,
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related_object_type=None, related_object_id=None):
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"""
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Deduct credits and log transaction.
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@@ -127,8 +132,10 @@ class CreditService:
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operation_type: Type of operation (from CreditUsageLog.OPERATION_TYPE_CHOICES)
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description: Description of the transaction
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metadata: Optional metadata dict
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cost_usd: Optional cost in USD
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model_used: Optional AI model used
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cost_usd_input: Optional input cost in USD
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cost_usd_output: Optional output cost in USD
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cost_usd_total: Optional total cost in USD
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model_config: Optional AIModelConfig instance
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tokens_input: Optional input tokens
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tokens_output: Optional output tokens
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related_object_type: Optional related object type
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@@ -154,25 +161,45 @@ class CreditService:
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metadata=metadata or {}
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)
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# Create CreditUsageLog
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CreditUsageLog.objects.create(
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account=account,
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operation_type=operation_type,
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credits_used=amount,
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cost_usd=cost_usd,
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model_used=model_used or '',
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tokens_input=tokens_input,
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tokens_output=tokens_output,
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related_object_type=related_object_type or '',
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related_object_id=related_object_id,
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metadata=metadata or {}
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)
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# Create CreditUsageLog with new model_config FK
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log_data = {
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'account': account,
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'operation_type': operation_type,
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'credits_used': amount,
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'tokens_input': tokens_input,
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'tokens_output': tokens_output,
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'related_object_type': related_object_type or '',
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'related_object_id': related_object_id,
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'metadata': metadata or {},
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}
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# Add model tracking (new FK)
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if model_config:
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log_data['model_config'] = model_config
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log_data['model_name'] = model_config.model_name
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# Add cost tracking (new fields)
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if cost_usd_input is not None:
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log_data['cost_usd_input'] = cost_usd_input
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if cost_usd_output is not None:
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log_data['cost_usd_output'] = cost_usd_output
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if cost_usd_total is not None:
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log_data['cost_usd_total'] = cost_usd_total
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# Legacy cost_usd field (backward compatibility)
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if cost_usd_total is not None:
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log_data['cost_usd'] = cost_usd_total
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CreditUsageLog.objects.create(**log_data)
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return account.credits
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@staticmethod
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@transaction.atomic
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def deduct_credits_for_operation(account, operation_type, amount=None, description=None, metadata=None, cost_usd=None, model_used=None, tokens_input=None, tokens_output=None, related_object_type=None, related_object_id=None):
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def deduct_credits_for_operation(account, operation_type, amount=None, description=None,
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metadata=None, cost_usd_input=None, cost_usd_output=None,
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cost_usd_total=None, model_config=None, tokens_input=None,
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tokens_output=None, related_object_type=None, related_object_id=None):
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"""
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Deduct credits for an operation (convenience method that calculates cost automatically).
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@@ -182,8 +209,10 @@ class CreditService:
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amount: Optional amount (word count, image count, etc.)
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description: Optional description (auto-generated if not provided)
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metadata: Optional metadata dict
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cost_usd: Optional cost in USD
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model_used: Optional AI model used
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cost_usd_input: Optional input cost in USD
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cost_usd_output: Optional output cost in USD
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cost_usd_total: Optional total cost in USD
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model_config: Optional AIModelConfig instance
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tokens_input: Optional input tokens
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tokens_output: Optional output tokens
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related_object_type: Optional related object type
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@@ -192,24 +221,33 @@ class CreditService:
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Returns:
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int: New credit balance
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"""
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# Calculate credit cost
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credits_required = CreditService.get_credit_cost(operation_type, amount)
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# Calculate credit cost - use token-based if tokens provided
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if tokens_input is not None and tokens_output is not None and model_config:
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credits_required = CreditService.calculate_credits_from_tokens(
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operation_type, tokens_input, tokens_output, model_config
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)
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else:
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credits_required = CreditService.get_credit_cost(operation_type, amount)
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# Check sufficient credits
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CreditService.check_credits(account, operation_type, amount)
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CreditService.check_credits_legacy(account, credits_required)
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# Auto-generate description if not provided
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if not description:
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model_name = model_config.display_name if model_config else "AI"
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if operation_type == 'clustering':
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description = f"Clustering operation"
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description = f"Clustering operation ({model_name})"
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elif operation_type == 'idea_generation':
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description = f"Generated {amount or 1} idea(s)"
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description = f"Generated {amount or 1} idea(s) ({model_name})"
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elif operation_type == 'content_generation':
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description = f"Generated content ({amount or 0} words)"
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if tokens_input and tokens_output:
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description = f"Generated content ({tokens_input + tokens_output} tokens, {model_name})"
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else:
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description = f"Generated content ({amount or 0} words, {model_name})"
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elif operation_type == 'image_generation':
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description = f"Generated {amount or 1} image(s)"
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description = f"Generated {amount or 1} image(s) ({model_name})"
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else:
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description = f"{operation_type} operation"
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description = f"{operation_type} operation ({model_name})"
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return CreditService.deduct_credits(
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account=account,
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@@ -217,8 +255,10 @@ class CreditService:
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operation_type=operation_type,
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description=description,
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metadata=metadata,
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cost_usd=cost_usd,
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model_used=model_used,
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cost_usd_input=cost_usd_input,
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cost_usd_output=cost_usd_output,
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cost_usd_total=cost_usd_total,
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model_config=model_config,
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tokens_input=tokens_input,
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tokens_output=tokens_output,
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related_object_type=related_object_type,
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@@ -292,3 +332,152 @@ class CreditService:
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return CreditService.get_credit_cost(operation_type, amount)
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@staticmethod
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def calculate_credits_from_tokens(operation_type, tokens_input, tokens_output, model_config):
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"""
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Calculate credits based on actual token usage and AI model configuration.
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This is the new token-aware calculation method.
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Args:
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operation_type: Type of operation (e.g., 'content_generation')
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tokens_input: Number of input tokens used
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tokens_output: Number of output tokens used
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model_config: AIModelConfig instance
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Returns:
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int: Number of credits to deduct
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Raises:
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CreditCalculationError: If calculation fails
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"""
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import logging
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logger = logging.getLogger(__name__)
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try:
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from igny8_core.business.billing.models import CreditCostConfig
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# Get operation config
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config = CreditCostConfig.objects.filter(
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operation_type=operation_type,
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is_active=True
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).first()
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if not config:
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raise CreditCalculationError(f"No active config found for operation: {operation_type}")
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# Check if operation uses token-based billing
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if config.unit in ['per_100_tokens', 'per_1000_tokens']:
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total_tokens = tokens_input + tokens_output
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# Get model's tokens-per-credit ratio
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tokens_per_credit = model_config.tokens_per_credit
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if tokens_per_credit <= 0:
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raise CreditCalculationError(f"Invalid tokens_per_credit: {tokens_per_credit}")
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# Calculate credits (float)
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credits_float = Decimal(total_tokens) / Decimal(tokens_per_credit)
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# Apply rounding (always round up to avoid undercharging)
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credits = math.ceil(credits_float)
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# Apply minimum cost from config (if set)
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credits = max(credits, config.credits_cost)
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logger.info(
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f"Token-based calculation: {total_tokens} tokens / {tokens_per_credit} = {credits} credits "
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f"(model: {model_config.model_name}, operation: {operation_type})"
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)
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return credits
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else:
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# Fall back to legacy calculation for non-token operations
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logger.warning(
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f"Operation {operation_type} uses unit {config.unit}, falling back to legacy calculation"
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)
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return config.credits_cost
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except Exception as e:
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logger.error(f"Failed to calculate credits from tokens: {e}")
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raise CreditCalculationError(f"Credit calculation failed: {e}")
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@staticmethod
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def get_model_for_operation(account, operation_type, task_model_override=None):
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"""
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Determine which AI model to use for an operation.
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Priority: Task Override > Account Default > Operation Default > System Default
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Args:
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account: Account instance
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operation_type: Type of operation
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task_model_override: Optional AIModelConfig instance from task
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Returns:
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AIModelConfig: The model to use
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"""
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import logging
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logger = logging.getLogger(__name__)
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# 1. Task-level override (highest priority)
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if task_model_override:
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logger.info(f"Using task-level model override: {task_model_override.model_name}")
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return task_model_override
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# 2. Account default model (from IntegrationSettings)
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try:
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from igny8_core.modules.system.models import IntegrationSettings
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from igny8_core.business.billing.models import CreditCostConfig
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integration = IntegrationSettings.objects.filter(account=account).first()
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if integration:
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# Determine if this is text or image operation
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config = CreditCostConfig.objects.filter(
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operation_type=operation_type,
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is_active=True
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).first()
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if config and config.default_model:
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model_type = config.default_model.model_type
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if model_type == 'text' and integration.default_text_model:
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logger.info(f"Using account default text model: {integration.default_text_model.model_name}")
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return integration.default_text_model
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elif model_type == 'image' and integration.default_image_model:
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logger.info(f"Using account default image model: {integration.default_image_model.model_name}")
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return integration.default_image_model
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except Exception as e:
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logger.warning(f"Failed to get account default model: {e}")
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# 3. Operation default model
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try:
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from igny8_core.business.billing.models import CreditCostConfig
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config = CreditCostConfig.objects.filter(
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operation_type=operation_type,
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is_active=True
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).first()
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if config and config.default_model:
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logger.info(f"Using operation default model: {config.default_model.model_name}")
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return config.default_model
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except Exception as e:
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logger.warning(f"Failed to get operation default model: {e}")
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# 4. System-wide default (fallback)
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try:
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default_model = AIModelConfig.objects.filter(
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is_default=True,
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is_active=True
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).first()
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if default_model:
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logger.info(f"Using system default model: {default_model.model_name}")
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return default_model
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except Exception as e:
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logger.warning(f"Failed to get system default model: {e}")
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# 5. Hard-coded fallback
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logger.warning("All model selection failed, using hard-coded fallback: gpt-4o-mini")
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return AIModelConfig.objects.filter(model_name='gpt-4o-mini').first()
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