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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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41
backend/igny8_core/business/optimization/services/optimizer_service.py
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@@ -133,7 +133,10 @@ class OptimizerService:
scores_after = self.analyzer.analyze(optimized_content)
# Calculate credits used
credits_used = self.credit_service.get_credit_cost('optimization', word_count)
estimated_tokens = len(content.html_content or '') // 4
credits_used = self.credit_service.calculate_credits_from_tokens(
'optimization', estimated_tokens, 0
)
# Update optimization task
task.scores_after = scores_after
@@ -148,18 +151,22 @@ class OptimizerService:
content.optimization_scores = scores_after
content.save(update_fields=['html_content', 'optimizer_version', 'optimization_scores'])
# Deduct credits
# Deduct credits (non-AI operation - use fixed token estimate based on content size)
# Estimate: 1 token per 4 characters of HTML content
estimated_tokens = len(content.html_content or '') // 4
self.credit_service.deduct_credits_for_operation(
account=account,
operation_type='optimization',
amount=word_count,
tokens_input=estimated_tokens,
tokens_output=0,
description=f"Content optimization: {content.title or 'Untitled'}",
related_object_type='content',
related_object_id=content.id,
metadata={
'scores_before': scores_before,
'scores_after': scores_after,
'improvement': scores_after.get('overall_score', 0) - scores_before.get('overall_score', 0)
'improvement': scores_after.get('overall_score', 0) - scores_before.get('overall_score', 0),
'word_count': word_count
}
)
@@ -279,7 +286,10 @@ class OptimizerService:
scores_after = self._enhance_product_scores(scores_after, optimized_content)
# Calculate credits used
credits_used = self.credit_service.get_credit_cost('optimization', word_count)
estimated_tokens = len(content.html_content or '') // 4
credits_used = self.credit_service.calculate_credits_from_tokens(
'optimization', estimated_tokens, 0
)
# Update optimization task
task.scores_after = scores_after
@@ -294,11 +304,14 @@ class OptimizerService:
content.optimization_scores = scores_after
content.save(update_fields=['html_content', 'optimizer_version', 'optimization_scores'])
# Deduct credits
# Deduct credits (non-AI operation - use fixed token estimate based on content size)
# Estimate: 1 token per 4 characters of HTML content
estimated_tokens = len(content.html_content or '') // 4
self.credit_service.deduct_credits_for_operation(
account=account,
operation_type='optimization',
amount=word_count,
tokens_input=estimated_tokens,
tokens_output=0,
description=f"Product optimization: {content.title or 'Untitled'}",
related_object_type='content',
related_object_id=content.id,
@@ -306,6 +319,7 @@ class OptimizerService:
'scores_before': scores_before,
'scores_after': scores_after,
'improvement': scores_after.get('overall_score', 0) - scores_before.get('overall_score', 0),
'word_count': word_count,
'entity_type': 'product'
}
)
@@ -372,7 +386,11 @@ class OptimizerService:
scores_after = self._enhance_taxonomy_scores(scores_after, optimized_content)
# Calculate credits used
credits_used = self.credit_service.get_credit_cost('optimization', word_count)
# Calculate estimated credits for task tracking
estimated_tokens = len(content.html_content or '') // 4
credits_used = self.credit_service.calculate_credits_from_tokens(
'optimization', estimated_tokens, 0
)
# Update optimization task
task.scores_after = scores_after
@@ -387,17 +405,20 @@ class OptimizerService:
content.optimization_scores = scores_after
content.save(update_fields=['html_content', 'optimizer_version', 'optimization_scores'])
# Deduct credits
# Deduct credits (non-AI operation - use fixed token estimate based on content size)
# Estimate: 1 token per 4 characters of HTML content
self.credit_service.deduct_credits_for_operation(
account=account,
operation_type='optimization',
amount=word_count,
tokens_input=estimated_tokens,
tokens_output=0,
description=f"Taxonomy optimization: {content.title or 'Untitled'}",
related_object_type='content',
related_object_id=content.id,
metadata={
'scores_before': scores_before,
'scores_after': scores_after,
'word_count': word_count,
'improvement': scores_after.get('overall_score', 0) - scores_before.get('overall_score', 0),
'entity_type': 'taxonomy'
}