igny8/backend/igny8_core/ai/engine.py

"""
AI Engine - Central orchestrator for all AI functions
"""
import logging
from typing import Dict, Any, Optional
from igny8_core.ai.base import BaseAIFunction
from igny8_core.ai.tracker import StepTracker, ProgressTracker, CostTracker
from igny8_core.ai.ai_core import AICore
from igny8_core.ai.settings import get_model_config

logger = logging.getLogger(__name__)


class AIEngine:
    """
    Central orchestrator for all AI functions.
    Manages lifecycle, progress, logging, retries, cost tracking.
    """
    
    def __init__(self, celery_task=None, account=None):
        self.task = celery_task
        self.account = account
        self.tracker = ProgressTracker(celery_task)
        self.step_tracker = StepTracker('ai_engine')  # For Celery progress callbacks
        self.cost_tracker = CostTracker()
    
    def _get_input_description(self, function_name: str, payload: dict, count: int) -> str:
        """Get user-friendly input description"""
        if function_name == 'auto_cluster':
            return f"{count} keyword{'s' if count != 1 else ''}"
        elif function_name == 'generate_ideas':
            return f"{count} cluster{'s' if count != 1 else ''}"
        elif function_name == 'generate_content':
            return f"{count} article{'s' if count != 1 else ''}"
        elif function_name == 'generate_images':
            return f"{count} image{'s' if count != 1 else ''}"
        elif function_name == 'generate_image_prompts':
            return f"{count} image prompt{'s' if count != 1 else ''}"
        elif function_name == 'optimize_content':
            return f"{count} article{'s' if count != 1 else ''}"
        elif function_name == 'generate_site_structure':
            return "site blueprint"
        return f"{count} item{'s' if count != 1 else ''}"
    
    def _build_validation_message(self, function_name: str, payload: dict, count: int, input_description: str) -> str:
        """Build validation message with item names for better UX"""
        if function_name == 'auto_cluster' and count > 0:
            try:
                from igny8_core.modules.planner.models import Keywords
                ids = payload.get('ids', [])
                keywords = Keywords.objects.filter(id__in=ids, account=self.account).values_list('keyword', flat=True)[:3]
                keyword_list = list(keywords)
                
                if len(keyword_list) > 0:
                    remaining = count - len(keyword_list)
                    if remaining > 0:
                        keywords_text = ', '.join(keyword_list)
                        return f"Validating {count} keywords for clustering"
                    else:
                        keywords_text = ', '.join(keyword_list)
                        return f"Validating {keywords_text}"
            except Exception as e:
                logger.warning(f"Failed to load keyword names for validation message: {e}")
        elif function_name == 'generate_ideas':
            return f"Analyzing {count} clusters for content opportunities"
        elif function_name == 'generate_content':
            return f"Preparing {count} article{'s' if count != 1 else ''} for generation"
        elif function_name == 'generate_image_prompts':
            return f"Analyzing content for image opportunities"
        elif function_name == 'generate_images':
            return f"Queuing {count} image{'s' if count != 1 else ''} for generation"
        elif function_name == 'optimize_content':
            return f"Analyzing {count} article{'s' if count != 1 else ''} for optimization"
        
        # Fallback to simple count message
        return f"Validating {input_description}"
    
    def _get_prep_message(self, function_name: str, count: int, data: Any) -> str:
        """Get user-friendly prep message"""
        if function_name == 'auto_cluster':
            return f"Analyzing keyword relationships for {count} keyword{'s' if count != 1 else ''}"
        elif function_name == 'generate_ideas':
            # Count keywords in clusters if available
            keyword_count = 0
            if isinstance(data, dict) and 'cluster_data' in data:
                for cluster in data['cluster_data']:
                    keyword_count += len(cluster.get('keywords', []))
            if keyword_count > 0:
                return f"Mapping {keyword_count} keywords to topic briefs"
            return f"Mapping keywords to topic briefs for {count} cluster{'s' if count != 1 else ''}"
        elif function_name == 'generate_content':
            return f"Building content brief{'s' if count != 1 else ''} with target keywords"
        elif function_name == 'generate_images':
            return f"Preparing AI image generation ({count} image{'s' if count != 1 else ''})"
        elif function_name == 'generate_image_prompts':
            # Extract max_images from data if available
            if isinstance(data, list) and len(data) > 0:
                max_images = data[0].get('max_images')
                total_images = 1 + max_images  # 1 featured + max_images in-article
                return f"Identifying 1 featured + {max_images} in-article image slots"
            elif isinstance(data, dict) and 'max_images' in data:
                max_images = data.get('max_images')
                total_images = 1 + max_images
                return f"Identifying 1 featured + {max_images} in-article image slots"
            return f"Identifying featured and in-article image slots"
        elif function_name == 'optimize_content':
            return f"Analyzing SEO factors for {count} article{'s' if count != 1 else ''}"
        elif function_name == 'generate_site_structure':
            blueprint_name = ''
            if isinstance(data, dict):
                blueprint = data.get('blueprint')
                if blueprint and getattr(blueprint, 'name', None):
                    blueprint_name = f'"{blueprint.name}"'
            return f"Preparing site blueprint {blueprint_name}".strip()
        return f"Preparing {count} item{'s' if count != 1 else ''}"
    
    def _get_ai_call_message(self, function_name: str, count: int) -> str:
        """Get user-friendly AI call message"""
        if function_name == 'auto_cluster':
            return f"Grouping {count} keywords by search intent"
        elif function_name == 'generate_ideas':
            return f"Generating content ideas for {count} cluster{'s' if count != 1 else ''}"
        elif function_name == 'generate_content':
            return f"Writing {count} article{'s' if count != 1 else ''} with AI"
        elif function_name == 'generate_images':
            return f"Generating image{'s' if count != 1 else ''} with AI"
        elif function_name == 'generate_image_prompts':
            return f"Creating optimized prompts for {count} image{'s' if count != 1 else ''}"
        elif function_name == 'optimize_content':
            return f"Optimizing {count} article{'s' if count != 1 else ''} for SEO"
        elif function_name == 'generate_site_structure':
            return "Designing complete site architecture"
        return f"Processing with AI"
    
    def _get_parse_message(self, function_name: str) -> str:
        """Get user-friendly parse message"""
        if function_name == 'auto_cluster':
            return "Organizing semantic clusters"
        elif function_name == 'generate_ideas':
            return "Structuring article outlines"
        elif function_name == 'generate_content':
            return "Formatting HTML content and metadata"
        elif function_name == 'generate_images':
            return "Processing generated images"
        elif function_name == 'generate_image_prompts':
            return "Refining contextual image descriptions"
        elif function_name == 'optimize_content':
            return "Compiling optimization scores"
        elif function_name == 'generate_site_structure':
            return "Compiling site map"
        return "Processing results"
    
    def _get_parse_message_with_count(self, function_name: str, count: int) -> str:
        """Get user-friendly parse message with count"""
        if function_name == 'auto_cluster':
            return f"Organizing {count} semantic cluster{'s' if count != 1 else ''}"
        elif function_name == 'generate_ideas':
            return f"Structuring {count} article outline{'s' if count != 1 else ''}"
        elif function_name == 'generate_content':
            return f"Formatting {count} article{'s' if count != 1 else ''}"
        elif function_name == 'generate_images':
            return f"Processing {count} generated image{'s' if count != 1 else ''}"
        elif function_name == 'generate_image_prompts':
            # Count is total prompts, in-article is count - 1 (subtract featured)
            in_article_count = max(0, count - 1)
            if in_article_count > 0:
                return f"Refining {in_article_count} in-article image description{'s' if in_article_count != 1 else ''}"
            return "Refining image descriptions"
        elif function_name == 'optimize_content':
            return f"Compiling scores for {count} article{'s' if count != 1 else ''}"
        elif function_name == 'generate_site_structure':
            return f"{count} page blueprint{'s' if count != 1 else ''} mapped"
        return f"{count} item{'s' if count != 1 else ''} processed"
    
    def _get_save_message(self, function_name: str, count: int) -> str:
        """Get user-friendly save message"""
        if function_name == 'auto_cluster':
            return f"Saving {count} cluster{'s' if count != 1 else ''} with keywords"
        elif function_name == 'generate_ideas':
            return f"Saving {count} idea{'s' if count != 1 else ''} with outlines"
        elif function_name == 'generate_content':
            return f"Saving {count} article{'s' if count != 1 else ''}"
        elif function_name == 'generate_images':
            return f"Uploading {count} image{'s' if count != 1 else ''} to media library"
        elif function_name == 'generate_image_prompts':
            in_article = max(0, count - 1)
            return f"Assigning {count} prompts (1 featured + {in_article} in-article)"
        elif function_name == 'optimize_content':
            return f"Saving optimization scores for {count} article{'s' if count != 1 else ''}"
        elif function_name == 'generate_site_structure':
            return f"Publishing {count} page blueprint{'s' if count != 1 else ''}"
        return f"Saving {count} item{'s' if count != 1 else ''}"
    
    def _get_done_message(self, function_name: str, result: dict) -> str:
        """Get user-friendly completion message with counts"""
        count = result.get('count', 0)
        
        if function_name == 'auto_cluster':
            keyword_count = result.get('keywords_clustered', 0)
            return f"✓ Organized {keyword_count} keywords into {count} semantic cluster{'s' if count != 1 else ''}"
        elif function_name == 'generate_ideas':
            return f"✓ Created {count} content idea{'s' if count != 1 else ''} with detailed outlines"
        elif function_name == 'generate_content':
            total_words = result.get('total_words', 0)
            if total_words > 0:
                return f"✓ Generated {count} article{'s' if count != 1 else ''} ({total_words:,} words)"
            return f"✓ Generated {count} article{'s' if count != 1 else ''}"
        elif function_name == 'generate_images':
            return f"✓ Generated and saved {count} AI image{'s' if count != 1 else ''}"
        elif function_name == 'generate_image_prompts':
            in_article = max(0, count - 1)
            return f"✓ Created {count} image prompt{'s' if count != 1 else ''} (1 featured + {in_article} in-article)"
        elif function_name == 'optimize_content':
            avg_score = result.get('average_score', 0)
            if avg_score > 0:
                return f"✓ Optimized {count} article{'s' if count != 1 else ''} (avg score: {avg_score}%)"
            return f"✓ Optimized {count} article{'s' if count != 1 else ''}"
        elif function_name == 'generate_site_structure':
            return f"✓ Created {count} page blueprint{'s' if count != 1 else ''}"
        return f"✓ {count} item{'s' if count != 1 else ''} completed"
    
    def execute(self, fn: BaseAIFunction, payload: dict) -> dict:
        """
        Unified execution pipeline for all AI functions.
        
        Phases with improved percentage mapping:
        - INIT (0-10%): Validation & preparation
        - PREP (10-25%): Data loading & prompt building
        - AI_CALL (25-70%): API call to provider (longest phase)
        - PARSE (70-85%): Response parsing
        - SAVE (85-98%): Database operations
        - DONE (98-100%): Finalization
        """
        function_name = fn.get_name()
        self.step_tracker.function_name = function_name
        
        try:
            # Phase 1: INIT - Validation & Setup (0-10%)
            # Extract input data for user-friendly messages
            ids = payload.get('ids', [])
            input_count = len(ids) if ids else 0
            input_description = self._get_input_description(function_name, payload, input_count)
            
            validated = fn.validate(payload, self.account)
            if not validated['valid']:
                return self._handle_error(validated['error'], fn)
            
            # Build validation message with keyword names for auto_cluster
            validation_message = self._build_validation_message(function_name, payload, input_count, input_description)
            self.step_tracker.add_request_step("INIT", "success", validation_message)
            self.tracker.update("INIT", 10, validation_message, meta=self.step_tracker.get_meta())
            
            # Phase 2: PREP - Data Loading & Prompt Building (10-25%)
            data = fn.prepare(payload, self.account)
            if isinstance(data, (list, tuple)):
                data_count = len(data)
            elif isinstance(data, dict):
                # Check for cluster_data (for generate_ideas) or keywords (for auto_cluster)
                if 'cluster_data' in data:
                    data_count = len(data['cluster_data'])
                elif 'keywords' in data:
                    data_count = len(data['keywords'])
                else:
                    data_count = data.get('count', input_count)
            else:
                data_count = input_count
            
            prep_message = self._get_prep_message(function_name, data_count, data)
            
            prompt = fn.build_prompt(data, self.account)
            
            self.step_tracker.add_request_step("PREP", "success", prep_message)
            self.tracker.update("PREP", 25, prep_message, meta=self.step_tracker.get_meta())
            
            # Phase 2.5: CREDIT CHECK - Check credits before AI call (25%)
            if self.account:
                try:
                    from igny8_core.business.billing.services.credit_service import CreditService
                    from igny8_core.business.billing.exceptions import InsufficientCreditsError
                    
                    # Map function name to operation type
                    operation_type = self._get_operation_type(function_name)
                    
                    # Calculate estimated cost
                    estimated_amount = self._get_estimated_amount(function_name, data, payload)
                    
                    # Check credits BEFORE AI call
                    CreditService.check_credits(self.account, operation_type, estimated_amount)
                    
                    logger.info(f"[AIEngine] Credit check passed: {operation_type}, estimated amount: {estimated_amount}")
                except InsufficientCreditsError as e:
                    error_msg = str(e)
                    error_type = 'InsufficientCreditsError'
                    logger.error(f"[AIEngine] {error_msg}")
                    return self._handle_error(error_msg, fn, error_type=error_type)
                except Exception as e:
                    logger.warning(f"[AIEngine] Failed to check credits: {e}", exc_info=True)
                    # Don't fail the operation if credit check fails (for backward compatibility)
            
            # Phase 3: AI_CALL - Provider API Call (25-70%)
            # Validate account exists before proceeding
            if not self.account:
                error_msg = "Account is required for AI function execution"
                logger.error(f"[AIEngine] {error_msg}")
                return self._handle_error(error_msg, fn)
            
            ai_core = AICore(account=self.account)
            function_name = fn.get_name()
            
            # Generate function_id for tracking (ai-{function_name}-01)
            # Normalize underscores to hyphens to match frontend tracking IDs
            function_id_base = function_name.replace('_', '-')
            function_id = f"ai-{function_id_base}-01-desktop"
            
            # Get model config from settings (requires account)
            # This will raise ValueError if IntegrationSettings not configured
            try:
                model_config = get_model_config(function_name, account=self.account)
                model = model_config.get('model')
            except ValueError as e:
                # IntegrationSettings not configured or model missing
                error_msg = str(e)
                error_type = 'ConfigurationError'
                logger.error(f"[AIEngine] {error_msg}")
                return self._handle_error(error_msg, fn, error_type=error_type)
            except Exception as e:
                # Other unexpected errors
                error_msg = f"Failed to get model configuration: {str(e)}"
                error_type = type(e).__name__
                logger.error(f"[AIEngine] {error_msg}", exc_info=True)
                return self._handle_error(error_msg, fn, error_type=error_type)
            
            # Debug logging: Show model configuration (console only, not in step tracker)
            logger.info(f"[AIEngine] Model Configuration for {function_name}:")
            logger.info(f"  - Model from get_model_config: {model}")
            logger.info(f"  - Full model_config: {model_config}")
            
            # Track AI call start with user-friendly message
            ai_call_message = self._get_ai_call_message(function_name, data_count)
            self.step_tracker.add_response_step("AI_CALL", "success", ai_call_message)
            self.tracker.update("AI_CALL", 50, ai_call_message, meta=self.step_tracker.get_meta())
            
            try:
                # Use centralized run_ai_request()
                raw_response = ai_core.run_ai_request(
                    prompt=prompt,
                    model=model,
                    max_tokens=model_config.get('max_tokens'),
                    temperature=model_config.get('temperature'),
                    response_format=model_config.get('response_format'),
                    function_name=function_name,
                    function_id=function_id  # Pass function_id for tracking
                )
            except Exception as e:
                error_msg = f"AI call failed: {str(e)}"
                logger.error(f"Exception during AI call: {error_msg}", exc_info=True)
                return self._handle_error(error_msg, fn)
            
            if raw_response.get('error'):
                error_msg = raw_response.get('error', 'Unknown AI error')
                logger.error(f"AI call returned error: {error_msg}")
                return self._handle_error(error_msg, fn)
            
            if not raw_response.get('content'):
                error_msg = "AI call returned no content"
                logger.error(error_msg)
                return self._handle_error(error_msg, fn)
            
            # Track cost
            self.cost_tracker.record(
                function_name=function_name,
                cost=raw_response.get('cost', 0),
                tokens=raw_response.get('total_tokens', 0),
                model=raw_response.get('model')
            )
            
            # Update AI_CALL step with results
            self.step_tracker.response_steps[-1] = {
                **self.step_tracker.response_steps[-1],
                'message': f"Received {raw_response.get('total_tokens', 0)} tokens, Cost: ${raw_response.get('cost', 0):.6f}",
                'duration': raw_response.get('duration')
            }
            self.tracker.update("AI_CALL", 70, f"AI response received ({raw_response.get('total_tokens', 0)} tokens)", meta=self.step_tracker.get_meta())
            
            # Phase 4: PARSE - Response Parsing (70-85%)
            try:
                parse_message = self._get_parse_message(function_name)
                response_content = raw_response.get('content', '')
                parsed = fn.parse_response(response_content, self.step_tracker)
                
                if isinstance(parsed, (list, tuple)):
                    parsed_count = len(parsed)
                elif isinstance(parsed, dict):
                    # Check if it's a content dict (has 'content' field) or a result dict (has 'count')
                    if 'content' in parsed:
                        parsed_count = 1  # Single content item
                    else:
                        parsed_count = parsed.get('count', 1)
                else:
                    parsed_count = 1
                
                # Update parse message with count for better UX
                parse_message = self._get_parse_message_with_count(function_name, parsed_count)
                
                self.step_tracker.add_response_step("PARSE", "success", parse_message)
                self.tracker.update("PARSE", 85, parse_message, meta=self.step_tracker.get_meta())
            except Exception as parse_error:
                error_msg = f"Failed to parse AI response: {str(parse_error)}"
                logger.error(f"AIEngine: {error_msg}", exc_info=True)
                logger.error(f"AIEngine: Response content was: {response_content[:500] if response_content else 'None'}...")
                return self._handle_error(error_msg, fn)
            
            # Phase 5: SAVE - Database Operations (85-98%)
            save_result = fn.save_output(parsed, data, self.account, self.tracker, step_tracker=self.step_tracker)
            clusters_created = save_result.get('clusters_created', 0)
            keywords_updated = save_result.get('keywords_updated', 0)
            count = save_result.get('count', 0)
            
            # Use user-friendly save message based on function type
            if clusters_created:
                save_msg = f"Saving {clusters_created} cluster{'s' if clusters_created != 1 else ''}"
            elif count:
                save_msg = self._get_save_message(function_name, count)
            else:
                save_msg = self._get_save_message(function_name, data_count)
            
            self.step_tracker.add_request_step("SAVE", "success", save_msg)
            self.tracker.update("SAVE", 98, save_msg, meta=self.step_tracker.get_meta())
            
            # Store save_msg for use in DONE phase
            final_save_msg = save_msg
            
            # Phase 5.5: DEDUCT CREDITS - Deduct credits after successful save
            if self.account and raw_response:
                try:
                    from igny8_core.business.billing.services.credit_service import CreditService
                    from igny8_core.business.billing.exceptions import InsufficientCreditsError
                    
                    # Map function name to operation type
                    operation_type = self._get_operation_type(function_name)
                    
                    # Get actual token usage from response (AI returns 'input_tokens' and 'output_tokens')
                    tokens_input = raw_response.get('input_tokens', 0)
                    tokens_output = raw_response.get('output_tokens', 0)
                    
                    # Deduct credits based on actual token usage
                    CreditService.deduct_credits_for_operation(
                        account=self.account,
                        operation_type=operation_type,
                        tokens_input=tokens_input,
                        tokens_output=tokens_output,
                        cost_usd=raw_response.get('cost'),
                        model_used=raw_response.get('model', ''),
                        related_object_type=self._get_related_object_type(function_name),
                        related_object_id=save_result.get('id') or save_result.get('cluster_id') or save_result.get('task_id'),
                        metadata={
                            'function_name': function_name,
                            'clusters_created': clusters_created,
                            'keywords_updated': keywords_updated,
                            'count': count,
                            **save_result
                        }
                    )
                    
                    logger.info(
                        f"[AIEngine] Credits deducted: {operation_type}, "
                        f"tokens: {tokens_input + tokens_output} ({tokens_input} in, {tokens_output} out)"
                    )
                except InsufficientCreditsError as e:
                    # This shouldn't happen since we checked before, but log it
                    logger.error(f"[AIEngine] Insufficient credits during deduction: {e}")
                except Exception as e:
                    logger.warning(f"[AIEngine] Failed to deduct credits: {e}", exc_info=True)
                    # Don't fail the operation if credit deduction fails (for backward compatibility)
            
            # Phase 6: DONE - Finalization (98-100%)
            done_msg = self._get_done_message(function_name, save_result)
            self.step_tracker.add_request_step("DONE", "success", done_msg)
            self.tracker.update("DONE", 100, done_msg, meta=self.step_tracker.get_meta())
            
            # Log to database
            self._log_to_database(fn, payload, parsed, save_result)
            
            return {
                'success': True,
                **save_result,
                'request_steps': self.step_tracker.request_steps,
                'response_steps': self.step_tracker.response_steps,
                'cost': self.cost_tracker.get_total(),
                'tokens': self.cost_tracker.get_total_tokens()
            }
            
        except Exception as e:
            error_msg = str(e)
            error_type = type(e).__name__
            logger.error(f"Error in AIEngine.execute for {function_name}: {error_msg}", exc_info=True)
            return self._handle_error(error_msg, fn, exc_info=True, error_type=error_type)
    
    def _handle_error(self, error: str, fn: BaseAIFunction = None, exc_info=False, error_type: str = None):
        """Centralized error handling"""
        function_name = fn.get_name() if fn else 'unknown'
        
        # Determine error type
        if error_type:
            final_error_type = error_type
        elif isinstance(error, Exception):
            final_error_type = type(error).__name__
        else:
            final_error_type = 'Error'
        
        self.step_tracker.add_request_step("Error", "error", error, error=error)
        
        error_meta = {
            'error': error,
            'error_type': final_error_type,
            **self.step_tracker.get_meta()
        }
        self.tracker.error(error, meta=error_meta)
        
        if exc_info:
            logger.error(f"Error in {function_name}: {error}", exc_info=True)
        else:
            logger.error(f"Error in {function_name}: {error}")
        
        self._log_to_database(fn, None, None, None, error=error)
        
        return {
            'success': False,
            'error': error,
            'error_type': final_error_type,
            'request_steps': self.step_tracker.request_steps,
            'response_steps': self.step_tracker.response_steps
        }
    
    def _log_to_database(
        self,
        fn: BaseAIFunction = None,
        payload: dict = None,
        parsed: Any = None,
        save_result: dict = None,
        error: str = None
    ):
        """Log to unified ai_task_logs table"""
        try:
            from igny8_core.ai.models import AITaskLog
            
            # Only log if account exists (AITaskLog requires account)
            if not self.account:
                logger.warning("Cannot log AI task - no account available")
                return
            
            AITaskLog.objects.create(
                task_id=self.task.request.id if self.task else None,
                function_name=fn.get_name() if fn else None,
                account=self.account,
                phase=self.tracker.current_phase,
                message=self.tracker.current_message,
                status='error' if error else 'success',
                duration=self.tracker.get_duration(),
                cost=self.cost_tracker.get_total(),
                tokens=self.cost_tracker.get_total_tokens(),
                request_steps=self.step_tracker.request_steps,
                response_steps=self.step_tracker.response_steps,
                error=error,
                payload=payload,
                result=save_result
            )
        except Exception as e:
            # Don't fail the task if logging fails
            logger.warning(f"Failed to log to database: {e}")
    
    def _get_operation_type(self, function_name):
        """Map function name to operation type for credit system"""
        mapping = {
            'auto_cluster': 'clustering',
            'generate_ideas': 'idea_generation',
            'generate_content': 'content_generation',
            'generate_image_prompts': 'image_prompt_extraction',
            'generate_images': 'image_generation',
            'generate_site_structure': 'site_structure_generation',
        }
        return mapping.get(function_name, function_name)
    
    def _get_estimated_amount(self, function_name, data, payload):
        """Get estimated amount for credit calculation (before operation)"""
        if function_name == 'generate_content':
            # Estimate word count - tasks don't have word_count field, use default
            # data is a list of Task objects
            if isinstance(data, list) and len(data) > 0:
                # Multiple tasks - estimate 1000 words per task
                return len(data) * 1000
            return 1000  # Default estimate for single item
        elif function_name == 'generate_images':
            # Count images to generate
            if isinstance(payload, dict):
                image_ids = payload.get('image_ids', [])
                return len(image_ids) if image_ids else 1
            return 1
        elif function_name == 'generate_ideas':
            # Count clusters
            if isinstance(data, dict) and 'cluster_data' in data:
                return len(data['cluster_data'])
            return 1
        # For fixed cost operations (clustering, image_prompt_extraction), return None
        return None
    
    def _get_actual_amount(self, function_name, save_result, parsed, data):
        """Get actual amount for credit calculation (after operation)"""
        if function_name == 'generate_content':
            # Get actual word count from saved content
            if isinstance(save_result, dict):
                word_count = save_result.get('word_count')
                if word_count and word_count > 0:
                    return word_count
            # Fallback: estimate from parsed content
            if isinstance(parsed, dict) and 'content' in parsed:
                content = parsed['content']
                return len(content.split()) if isinstance(content, str) else 1000
            # Fallback: estimate from html_content if available
            if isinstance(parsed, dict) and 'html_content' in parsed:
                html_content = parsed['html_content']
                if isinstance(html_content, str):
                    # Strip HTML tags for word count
                    import re
                    text = re.sub(r'<[^>]+>', '', html_content)
                    return len(text.split())
            return 1000
        elif function_name == 'generate_images':
            # Count successfully generated images
            count = save_result.get('count', 0)
            if count > 0:
                return count
            return 1
        elif function_name == 'generate_ideas':
            # Count ideas generated
            count = save_result.get('count', 0)
            if count > 0:
                return count
            return 1
        # For fixed cost operations, return None
        return None
    
    def _get_related_object_type(self, function_name):
        """Get related object type for credit logging"""
        mapping = {
            'auto_cluster': 'cluster',
            'generate_ideas': 'content_idea',
            'generate_content': 'content',
            'generate_image_prompts': 'image',
            'generate_images': 'image',
            'generate_site_structure': 'site_blueprint',
        }
        return mapping.get(function_name, 'unknown')