"""
Unified Celery task entrypoint for all AI functions
"""
import logging
from celery import shared_task
from igny8_core.ai.engine import AIEngine
from igny8_core.ai.registry import get_function_instance

logger = logging.getLogger(__name__)


@shared_task(bind=True, max_retries=3)
def run_ai_task(self, function_name: str, payload: dict, account_id: int):
    """
    Single Celery entrypoint for all AI functions.
    Dynamically loads and executes the requested function.
    
    Args:
        function_name: Name of the AI function (e.g., 'auto_cluster')
        payload: Function-specific payload
        account_id: Account ID for account isolation (required)
    
    Raises:
        Returns error dict if account_id not provided or account not found
    """
    logger.info("=" * 80)
    logger.info(f"run_ai_task STARTED: {function_name}")
    logger.info(f"  - Task ID: {self.request.id}")
    logger.info(f"  - Function: {function_name}")
    logger.info(f"  - Account ID: {account_id}")
    logger.info(f"  - Payload keys: {list(payload.keys())}")
    logger.info("=" * 80)
    
    try:
        # Validate account_id is provided
        if not account_id:
            error_msg = "account_id is required for AI task execution"
            logger.error(f"[run_ai_task] {error_msg}")
            return {
                'success': False,
                'error': error_msg,
                'error_type': 'ConfigurationError'
            }
        
        # Get account and validate it exists
        from igny8_core.auth.models import Account
        try:
            account = Account.objects.get(id=account_id)
        except Account.DoesNotExist:
            error_msg = f"Account {account_id} not found"
            logger.error(f"[run_ai_task] {error_msg}")
            return {
                'success': False,
                'error': error_msg,
                'error_type': 'AccountNotFound'
            }
        
        # Get function from registry
        fn = get_function_instance(function_name)
        if not fn:
            error_msg = f'Function {function_name} not found in registry'
            logger.error(error_msg)
            return {
                'success': False,
                'error': error_msg
            }
        
        # Create engine and execute
        engine = AIEngine(celery_task=self, account=account)
        result = engine.execute(fn, payload)
        
        logger.info("=" * 80)
        logger.info(f"run_ai_task COMPLETED: {function_name}")
        logger.info(f"  - Success: {result.get('success')}")
        if not result.get('success'):
            logger.error(f"  - Error: {result.get('error')}")
        logger.info("=" * 80)
        
        # If execution failed, update state and return error (don't raise to avoid serialization issues)
        if not result.get('success'):
            error_msg = result.get('error', 'Task execution failed')
            error_type = result.get('error_type', 'ExecutionError')
            # Update task state with error details
            error_meta = {
                'error': error_msg,
                'error_type': error_type,
                'function_name': function_name,
                'phase': result.get('phase', 'ERROR'),
                'percentage': 0,
                'message': f'Error: {error_msg}',
                'request_steps': result.get('request_steps', []),
                'response_steps': result.get('response_steps', [])
            }
            try:
                self.update_state(
                    state='FAILURE',
                    meta=error_meta
                )
            except Exception as update_err:
                logger.warning(f"Failed to update task state: {update_err}")
            
            # Return error result - Celery will mark as FAILURE based on state
            # Don't raise exception to avoid serialization issues
            return {
                'success': False,
                'error': error_msg,
                'error_type': error_type,
                **error_meta
            }
        
        return result
        
    except Exception as e:
        error_type = type(e).__name__
        error_msg = str(e)
        
        logger.error("=" * 80)
        logger.error(f"run_ai_task FAILED: {function_name}")
        logger.error(f"  - Error: {error_type}: {error_msg}")
        logger.error("=" * 80, exc_info=True)
        
        # Update task state with error details (don't raise to avoid serialization issues)
        error_meta = {
            'error': error_msg,
            'error_type': error_type,
            'function_name': function_name,
            'phase': 'ERROR',
            'percentage': 0,
            'message': f'Error: {error_msg}'
        }
        try:
            self.update_state(
                state='FAILURE',
                meta=error_meta
            )
        except Exception as update_err:
            logger.warning(f"Failed to update task state: {update_err}")
        
        # Return error result - don't raise to avoid Celery serialization issues
        return {
            'success': False,
            'error': error_msg,
            'error_type': error_type,
            'function_name': function_name,
            **error_meta
        }


@shared_task(bind=True, name='igny8_core.ai.tasks.process_image_generation_queue')
def process_image_generation_queue(self, image_ids: list, account_id: int = None, content_id: int = None):
    """
    Process image generation queue sequentially (one image at a time)
    Updates Celery task meta with progress for each image
    """
    from typing import List
    from igny8_core.modules.writer.models import Images, Content
    from igny8_core.modules.system.models import IntegrationSettings
    from igny8_core.ai.ai_core import AICore
    from igny8_core.ai.prompts import PromptRegistry
    from igny8_core.business.billing.services.credit_service import CreditService
    from igny8_core.business.billing.exceptions import InsufficientCreditsError, CreditCalculationError

    logger.info("=" * 80)
    logger.info(f"process_image_generation_queue STARTED")
    logger.info(f"  - Task ID: {self.request.id}")
    logger.info(f"  - Image IDs: {image_ids}")
    logger.info(f"  - Account ID: {account_id}")
    logger.info(f"  - Content ID: {content_id}")
    logger.info("=" * 80)

    account = None
    if account_id:
        from igny8_core.auth.models import Account
        try:
            account = Account.objects.get(id=account_id)
        except Account.DoesNotExist:
            logger.error(f"Account {account_id} not found")
            return {'success': False, 'error': 'Account not found'}
    
    # Initialize progress tracking
    total_images = len(image_ids)
    completed = 0
    failed = 0
    results = []
    
    # Get image generation settings from AISettings (with account overrides)
    logger.info("[process_image_generation_queue] Step 1: Loading image generation settings")
    from igny8_core.modules.system.ai_settings import AISettings
    from igny8_core.ai.model_registry import ModelRegistry
    
    # Get effective settings
    image_type = AISettings.get_effective_image_style(account)
    image_format = 'webp'  # Default format
    
    # Get default image model from database
    default_model = ModelRegistry.get_default_model('image')
    if default_model:
        model_config = ModelRegistry.get_model(default_model)
        provider = model_config.provider if model_config else 'openai'
        model = default_model
    else:
        provider = 'openai'
        model = 'dall-e-3'
    
    logger.info(f"[process_image_generation_queue] Using PROVIDER: {provider}, MODEL: {model} from settings")
    
    # Style to prompt enhancement mapping
    # These style descriptors are added to the image prompt for better results
    STYLE_PROMPT_MAP = {
        # Runware styles
        'photorealistic': 'ultra realistic photography, natural lighting, real world look, photorealistic',
        'illustration': 'digital illustration, clean lines, artistic style, modern illustration',
        '3d_render': 'computer generated 3D render, modern polished 3D style, depth and dramatic lighting',
        'minimal_flat': 'minimal flat design, simple shapes, flat colors, modern graphic design aesthetic',
        'artistic': 'artistic painterly style, expressive brushstrokes, hand painted aesthetic',
        'cartoon': 'cartoon stylized illustration, playful exaggerated forms, animated character style',
        # DALL-E styles (mapped from OpenAI API style parameter)
        'natural': 'natural realistic style',
        'vivid': 'vivid dramatic hyper-realistic style',
        # Legacy fallbacks
        'realistic': 'ultra realistic photography, natural lighting, photorealistic',
    }
    
    # Get the style description for prompt enhancement
    style_description = STYLE_PROMPT_MAP.get(image_type, STYLE_PROMPT_MAP.get('photorealistic'))
    logger.info(f"[process_image_generation_queue] Style: {image_type} -> prompt enhancement: {style_description[:50]}...")
    
    # Model-specific landscape sizes (square is always 1024x1024)
    # For Runware models - based on Runware documentation for optimal results per model
    # For OpenAI DALL-E 3 - uses 1792x1024 for landscape
    MODEL_LANDSCAPE_SIZES = {
        'runware:97@1': '1280x768',   # Hi Dream Full landscape
        'bria:10@1': '1344x768',       # Bria 3.2 landscape (16:9)
        'google:4@2': '1376x768',      # Nano Banana landscape (16:9)
        'dall-e-3': '1792x1024',       # DALL-E 3 landscape
        'dall-e-2': '1024x1024',       # DALL-E 2 only supports square
    }
    DEFAULT_SQUARE_SIZE = '1024x1024'
    
    # Get model-specific landscape size for featured images
    model_landscape_size = MODEL_LANDSCAPE_SIZES.get(model, '1792x1024' if provider == 'openai' else '1280x768')
    
    # Featured image always uses model-specific landscape size
    featured_image_size = model_landscape_size
    # In-article images: alternating square/landscape based on position (handled in image loop)
    in_article_square_size = DEFAULT_SQUARE_SIZE
    in_article_landscape_size = model_landscape_size
    
    logger.info(f"[process_image_generation_queue] Settings loaded:")
    logger.info(f"  - Provider: {provider}")
    logger.info(f"  - Model: {model}")
    logger.info(f"  - Image type: {image_type}")
    logger.info(f"  - Image format: {image_format}")
    logger.info(f"  - Featured image size: {featured_image_size}")
    logger.info(f"  - In-article square: {in_article_square_size}, landscape: {in_article_landscape_size}")
    
    # Get provider API key from IntegrationProvider (centralized)
    logger.info(f"[process_image_generation_queue] Step 2: Loading {provider.upper()} API key from IntegrationProvider")
    
    # Get API key from IntegrationProvider (centralized)
    api_key = ModelRegistry.get_api_key(provider)
    
    if not api_key:
        logger.error(f"[process_image_generation_queue] {provider.upper()} API key not configured in IntegrationProvider")
        return {'success': False, 'error': f'{provider.upper()} API key not configured'}
    
    # Log API key presence (but not the actual key for security)
    api_key_preview = f"{api_key[:10]}...{api_key[-4:]}" if len(api_key) > 14 else "***"
    logger.info(f"[process_image_generation_queue] {provider.upper()} API key retrieved successfully (length: {len(api_key)}, preview: {api_key_preview})")
    
    # Get image prompt template (has placeholders: {image_type}, {post_title}, {image_prompt})
    try:
        image_prompt_template = PromptRegistry.get_image_prompt_template(account)
    except Exception as e:
        logger.warning(f"Failed to get image prompt template: {e}, using fallback")
        image_prompt_template = '{image_type} image for blog post titled "{post_title}": {image_prompt}'
    
    # Get negative prompt for Runware (only needed for Runware provider)
    negative_prompt = None
    if provider == 'runware':
        try:
            negative_prompt = PromptRegistry.get_negative_prompt(account)
        except Exception as e:
            logger.warning(f"Failed to get negative prompt: {e}")
            negative_prompt = None
    
    # Initialize AICore
    ai_core = AICore(account=account)

    # Credit check before processing images
    if account:
        logger.info(f"[process_image_generation_queue] Step 3: Checking credits for {total_images} images")
        try:
            required_credits = CreditService.check_credits_for_image(
                account=account,
                model_name=model,
                num_images=total_images
            )
            logger.info(f"[process_image_generation_queue] Credit check passed: {required_credits} credits required, {account.credits} available")
        except InsufficientCreditsError as e:
            error_msg = str(e)
            logger.error(f"[process_image_generation_queue] Insufficient credits: {error_msg}")
            return {
                'success': False,
                'error': error_msg,
                'error_type': 'InsufficientCreditsError',
                'total_images': total_images,
                'completed': 0,
                'failed': total_images,
                'results': []
            }
        except CreditCalculationError as e:
            # Model not found or no credits_per_image configured - log warning but continue
            # This allows backward compatibility if model not configured
            logger.warning(f"[process_image_generation_queue] Credit calculation warning: {e}")
            logger.warning(f"[process_image_generation_queue] Proceeding without credit check (model may not be configured)")
        except Exception as e:
            # Don't fail for unexpected credit check errors - log and continue
            logger.warning(f"[process_image_generation_queue] Unexpected credit check error: {e}")

    # Process each image sequentially
    for index, image_id in enumerate(image_ids, 1):
        try:
            # Update task meta: current image processing (starting at 0%)
            self.update_state(
                state='PROGRESS',
                meta={
                    'current_image': index,
                    'total_images': total_images,
                    'completed': completed,
                    'failed': failed,
                    'status': 'processing',
                    'current_image_id': image_id,
                    'current_image_progress': 0,
                    'results': results
                }
            )
            
            # Load image record
            logger.info(f"[process_image_generation_queue] Image {index}/{total_images} (ID: {image_id}): Loading from database")
            try:
                image = Images.objects.get(id=image_id, account=account)
                logger.info(f"[process_image_generation_queue] Image {image_id} loaded:")
                logger.info(f"  - Type: {image.image_type}")
                logger.info(f"  - Status: {image.status}")
                logger.info(f"  - Prompt length: {len(image.prompt) if image.prompt else 0} chars")
                logger.info(f"  - Prompt preview: {image.prompt[:100] if image.prompt else 'None'}...")
                logger.info(f"  - Content ID: {image.content.id if image.content else 'None'}")
            except Images.DoesNotExist:
                logger.error(f"[process_image_generation_queue] Image {image_id} not found in database")
                logger.error(f"[process_image_generation_queue] Account: {account.id if account else 'None'}")
                results.append({
                    'image_id': image_id,
                    'status': 'failed',
                    'error': 'Image record not found'
                })
                failed += 1
                continue
            except Exception as e:
                logger.error(f"[process_image_generation_queue] ERROR loading image {image_id}: {e}", exc_info=True)
                results.append({
                    'image_id': image_id,
                    'status': 'failed',
                    'error': f'Error loading image: {str(e)[:180]}'
                })
                failed += 1
                continue
            
            # Check if prompt exists
            if not image.prompt:
                logger.warning(f"Image {image_id} has no prompt")
                results.append({
                    'image_id': image_id,
                    'status': 'failed',
                    'error': 'No prompt found'
                })
                failed += 1
                continue
            
            # Get content for template formatting
            content = image.content
            if not content:
                logger.warning(f"Image {image_id} has no content")
                results.append({
                    'image_id': image_id,
                    'status': 'failed',
                    'error': 'No content associated'
                })
                failed += 1
                continue
            
            # Format template with image prompt from database
            # For DALL-E 2: Use image prompt directly (no template), 1000 char limit
            # For DALL-E 3: Use template with placeholders, 4000 char limit
            # CRITICAL: DALL-E 2 has 1000 char limit, DALL-E 3 has 4000 char limit
            image_prompt = image.prompt or ""
            
            # Determine character limit based on model
            if model == 'dall-e-2':
                max_prompt_length = 1000
            elif model == 'dall-e-3':
                max_prompt_length = 4000
            else:
                # Default to 1000 for safety
                max_prompt_length = 1000
                logger.warning(f"Unknown model '{model}', using 1000 char limit")
            
            logger.info(f"[process_image_generation_queue] Model: {model}, Max prompt length: {max_prompt_length} chars")
            
            if model == 'dall-e-2':
                # DALL-E 2: Use image prompt directly, no template
                logger.info(f"[process_image_generation_queue] Using DALL-E 2 - skipping template, using image prompt directly")
                formatted_prompt = image_prompt
                
                # Truncate to 1000 chars if needed
                if len(formatted_prompt) > max_prompt_length:
                    logger.warning(f"DALL-E 2 prompt too long ({len(formatted_prompt)} chars), truncating to {max_prompt_length}")
                    truncated = formatted_prompt[:max_prompt_length - 3]
                    last_space = truncated.rfind(' ')
                    if last_space > max_prompt_length * 0.9:
                        formatted_prompt = truncated[:last_space] + "..."
                    else:
                        formatted_prompt = formatted_prompt[:max_prompt_length]
            else:
                # DALL-E 3 and others: Use template
                try:
                    # Truncate post_title (max 200 chars for DALL-E 3 to leave room for image_prompt)
                    post_title = content.title or content.meta_title or f"Content #{content.id}"
                    if len(post_title) > 200:
                        post_title = post_title[:197] + "..."
                    
                    # Calculate actual template length with placeholders filled
                    # Format template with dummy values to measure actual length
                    template_with_dummies = image_prompt_template.format(
                        image_type=style_description,  # Use actual style description length
                        post_title='X' * len(post_title),  # Use same length as actual post_title
                        image_prompt=''  # Empty to measure template overhead
                    )
                    template_overhead = len(template_with_dummies)
                    
                    # Calculate max image_prompt length: max_prompt_length - template_overhead - safety margin (50)
                    max_image_prompt_length = max_prompt_length - template_overhead - 50
                    if max_image_prompt_length < 100:
                        # If template is too long, use minimum 100 chars for image_prompt
                        max_image_prompt_length = 100
                        logger.warning(f"Template is very long ({template_overhead} chars), limiting image_prompt to {max_image_prompt_length}")
                    
                    logger.info(f"[process_image_generation_queue] Template overhead: {template_overhead} chars, max image_prompt: {max_image_prompt_length} chars")
                    
                    # Truncate image_prompt to calculated max
                    if len(image_prompt) > max_image_prompt_length:
                        logger.warning(f"Image prompt too long ({len(image_prompt)} chars), truncating to {max_image_prompt_length}")
                        # Word-aware truncation
                        truncated = image_prompt[:max_image_prompt_length - 3]
                        last_space = truncated.rfind(' ')
                        if last_space > max_image_prompt_length * 0.8:  # Only if we have a reasonable space
                            image_prompt = truncated[:last_space] + "..."
                        else:
                            image_prompt = image_prompt[:max_image_prompt_length - 3] + "..."
                    
                    formatted_prompt = image_prompt_template.format(
                        image_type=style_description,  # Use full style description instead of raw value
                        post_title=post_title,
                        image_prompt=image_prompt
                    )
                    
                    # CRITICAL: Final safety check - truncate to model-specific limit
                    if len(formatted_prompt) > max_prompt_length:
                        logger.warning(f"Formatted prompt too long ({len(formatted_prompt)} chars), truncating to {max_prompt_length} for {model}")
                        # Try word-aware truncation
                        truncated = formatted_prompt[:max_prompt_length - 3]
                        last_space = truncated.rfind(' ')
                        if last_space > max_prompt_length * 0.9:  # Only use word-aware if we have a reasonable space
                            formatted_prompt = truncated[:last_space] + "..."
                        else:
                            formatted_prompt = formatted_prompt[:max_prompt_length]  # Hard truncate
                    
                    # Double-check after truncation - MUST be <= max_prompt_length
                    if len(formatted_prompt) > max_prompt_length:
                        logger.error(f"Prompt still too long after truncation ({len(formatted_prompt)} chars), forcing hard truncate to {max_prompt_length}")
                        formatted_prompt = formatted_prompt[:max_prompt_length]
                        
                except Exception as e:
                    # Fallback if template formatting fails
                    logger.warning(f"Prompt template formatting failed: {e}, using image prompt directly")
                    formatted_prompt = image_prompt
                    # CRITICAL: Truncate to model-specific limit even in fallback
                    if len(formatted_prompt) > max_prompt_length:
                        logger.warning(f"Fallback prompt too long ({len(formatted_prompt)} chars), truncating to {max_prompt_length} for {model}")
                        # Try word-aware truncation
                        truncated = formatted_prompt[:max_prompt_length - 3]
                        last_space = truncated.rfind(' ')
                        if last_space > max_prompt_length * 0.9:
                            formatted_prompt = truncated[:last_space] + "..."
                        else:
                            formatted_prompt = formatted_prompt[:max_prompt_length]  # Hard truncate
                    # Final hard truncate if still too long - MUST be <= max_prompt_length
                    if len(formatted_prompt) > max_prompt_length:
                        logger.error(f"Fallback prompt still too long ({len(formatted_prompt)} chars), forcing hard truncate to {max_prompt_length}")
                        formatted_prompt = formatted_prompt[:max_prompt_length]
            
            # Generate image (using same approach as test image generation)
            logger.info(f"[process_image_generation_queue] Generating image {index}/{total_images} (ID: {image_id})")
            logger.info(f"[process_image_generation_queue] Provider: {provider}, Model: {model}")
            logger.info(f"[process_image_generation_queue] Prompt length: {len(formatted_prompt)} (MUST be <= {max_prompt_length} for {model})")
            if len(formatted_prompt) > max_prompt_length:
                logger.error(f"[process_image_generation_queue] ERROR: Prompt is {len(formatted_prompt)} chars, truncating NOW to {max_prompt_length}!")
                formatted_prompt = formatted_prompt[:max_prompt_length]
            logger.info(f"[process_image_generation_queue] Final prompt length: {len(formatted_prompt)}")
            logger.info(f"[process_image_generation_queue] Image type: {image_type}")
            
            # Update progress: Starting image generation (0%)
            self.update_state(
                state='PROGRESS',
                meta={
                    'current_image': index,
                    'total_images': total_images,
                    'completed': completed,
                    'failed': failed,
                    'status': 'processing',
                    'current_image_id': image_id,
                    'current_image_progress': 0,
                    'results': results
                }
            )
            
            # Use appropriate size based on image type and position
            # Featured: Always landscape (model-specific)
            # In-article: Alternating square/landscape based on position
            #   Position 0: Square (1024x1024)
            #   Position 1: Landscape (model-specific)
            #   Position 2: Square (1024x1024)
            #   Position 3: Landscape (model-specific)
            if image.image_type == 'featured':
                image_size = featured_image_size  # Model-specific landscape
            elif image.image_type == 'in_article':
                # Alternate based on position: even=square, odd=landscape
                position = image.position or 0
                if position % 2 == 0:  # Position 0, 2: Square
                    image_size = in_article_square_size
                else:  # Position 1, 3: Landscape
                    image_size = in_article_landscape_size
                logger.info(f"[process_image_generation_queue] In-article image position {position}: using {'square' if position % 2 == 0 else 'landscape'} size {image_size}")
            else:  # desktop or other (legacy)
                image_size = in_article_square_size  # Default to square
            
            # For DALL-E, convert image_type to style parameter
            # image_type is from user settings (e.g., 'vivid', 'natural', 'realistic')
            # DALL-E accepts 'vivid' or 'natural' - map accordingly
            dalle_style = None
            if provider == 'openai':
                # Map image_type to DALL-E style
                # 'natural' = more realistic photos (default)
                # 'vivid' = hyper-real, dramatic images
                if image_type in ['vivid']:
                    dalle_style = 'vivid'
                else:
                    # Default to 'natural' for realistic photos
                    dalle_style = 'natural'
                logger.info(f"[process_image_generation_queue] DALL-E style: {dalle_style} (from image_type: {image_type})")
            
            result = ai_core.generate_image(
                prompt=formatted_prompt,
                provider=provider,
                model=model,
                size=image_size,
                api_key=api_key,
                negative_prompt=negative_prompt,
                function_name='generate_images_from_prompts',
                style=dalle_style
            )
            
            # Update progress: Image generation complete (50%)
            self.update_state(
                state='PROGRESS',
                meta={
                    'current_image': index,
                    'total_images': total_images,
                    'completed': completed,
                    'failed': failed,
                    'status': 'processing',
                    'current_image_id': image_id,
                    'current_image_progress': 50,
                    'results': results
                }
            )
            
            logger.info(f"[process_image_generation_queue] Image generation result: has_url={bool(result.get('url'))}, has_error={bool(result.get('error'))}")
            
            # Check for errors
            if result.get('error'):
                error_message = result.get('error', 'Unknown error')
                logger.error(f"Image generation failed for {image_id}: {error_message}")
                
                # Truncate error message to avoid database field length issues
                # Some database fields may have 200 char limit, so truncate to 180 to be safe
                truncated_error = error_message[:180] if len(error_message) > 180 else error_message
                
                # Update image record: failed
                try:
                    image.status = 'failed'
                    image.save(update_fields=['status'])
                except Exception as save_error:
                    logger.error(f"Failed to save image status to database: {save_error}", exc_info=True)
                    # Continue even if save fails
                
                results.append({
                    'image_id': image_id,
                    'status': 'failed',
                    'error': truncated_error
                })
                failed += 1
            else:
                logger.info(f"Image generation successful for {image_id}")
                # Update image record: success
                image_url = result.get('url')
                logger.info(f"[process_image_generation_queue] Image {image_id} - URL received: {image_url[:100] if image_url else 'None'}...")
                logger.info(f"[process_image_generation_queue] Image {image_id} - URL length: {len(image_url) if image_url else 0} characters")
                
                # Update progress: Downloading image (75%)
                self.update_state(
                    state='PROGRESS',
                    meta={
                        'current_image': index,
                        'total_images': total_images,
                        'completed': completed,
                        'failed': failed,
                        'status': 'processing',
                        'current_image_id': image_id,
                        'current_image_progress': 75,
                        'results': results
                    }
                )
                
                # Download and save image to /data/app/igny8/frontend/public/images/ai-images
                saved_file_path = None
                if image_url:
                    try:
                        import os
                        import requests
                        import time
                        
                        # Use the correct path: /data/app/igny8/frontend/public/images/ai-images
                        # This is web-accessible via /images/ai-images/ (Vite serves from public/)
                        images_dir = '/data/app/igny8/frontend/public/images/ai-images'
                        
                        # Create directory if it doesn't exist
                        os.makedirs(images_dir, exist_ok=True)
                        logger.info(f"[process_image_generation_queue] Image {image_id} - Using directory: {images_dir}")
                        
                        # Generate filename: image_{image_id}_{timestamp}.png (or .webp for Runware)
                        timestamp = int(time.time())
                        # Use webp extension if provider is Runware, otherwise png
                        file_ext = 'webp' if provider == 'runware' else 'png'
                        filename = f"image_{image_id}_{timestamp}.{file_ext}"
                        file_path = os.path.join(images_dir, filename)
                        
                        # Download image
                        logger.info(f"[process_image_generation_queue] Image {image_id} - Downloading from: {image_url[:100]}...")
                        response = requests.get(image_url, timeout=60)
                        response.raise_for_status()
                        
                        # Save to file
                        with open(file_path, 'wb') as f:
                            f.write(response.content)
                        
                        # Verify file was actually saved and exists
                        if os.path.exists(file_path) and os.path.getsize(file_path) > 0:
                            saved_file_path = file_path
                            logger.info(f"[process_image_generation_queue] Image {image_id} - Saved to: {file_path} ({len(response.content)} bytes, verified: {os.path.getsize(file_path)} bytes on disk)")
                        else:
                            logger.error(f"[process_image_generation_queue] Image {image_id} - File write appeared to succeed but file not found or empty: {file_path}")
                            saved_file_path = None
                            raise Exception(f"File was not saved successfully to {file_path}")
                        
                    except Exception as download_error:
                        logger.error(f"[process_image_generation_queue] Image {image_id} - Failed to download/save image: {download_error}", exc_info=True)
                        # Continue with URL only if download fails
                
                # Update progress: Saving to database (90%)
                self.update_state(
                    state='PROGRESS',
                    meta={
                        'current_image': index,
                        'total_images': total_images,
                        'completed': completed,
                        'failed': failed,
                        'status': 'processing',
                        'current_image_id': image_id,
                        'current_image_progress': 90,
                        'results': results
                    }
                )
                
                # Log URL length for debugging (model field now supports up to 500 chars)
                if image_url and len(image_url) > 500:
                    logger.error(f"[process_image_generation_queue] Image {image_id} - URL TOO LONG: {len(image_url)} chars (max 500). URL: {image_url[:150]}...")
                    logger.error(f"[process_image_generation_queue] Image {image_id} - CharField max_length=500 is too short! URL will be truncated.")
                    # Truncate to 500 chars if somehow longer (shouldn't happen, but safety check)
                    image_url = image_url[:500]
                    logger.warning(f"[process_image_generation_queue] Image {image_id} - Truncated URL length: {len(image_url)} chars")
                elif image_url and len(image_url) > 200:
                    logger.info(f"[process_image_generation_queue] Image {image_id} - URL length {len(image_url)} chars (was limited to 200, now supports 500)")
                
                try:
                    # Save file path and URL appropriately
                    if saved_file_path:
                        # Store local file path in image_path field
                        image.image_path = saved_file_path
                        # Also keep the original URL in image_url field for reference
                        if image_url:
                            image.image_url = image_url
                        logger.info(f"[process_image_generation_queue] Image {image_id} - Saved local path: {saved_file_path}")
                    else:
                        # Only URL available, save to image_url
                        image.image_url = image_url
                        logger.info(f"[process_image_generation_queue] Image {image_id} - Saved URL only: {image_url[:100] if image_url else 'None'}...")
                    image.status = 'generated'
                    
                    # Determine which fields to update
                    update_fields = ['status']
                    if saved_file_path:
                        update_fields.append('image_path')
                    if image_url:
                        update_fields.append('image_url')
                    
                    logger.info(f"[process_image_generation_queue] Image {image_id} - Attempting to save to database (fields: {update_fields})")
                    image.save(update_fields=update_fields)
                    logger.info(f"[process_image_generation_queue] Image {image_id} - Successfully saved to database")
                except Exception as save_error:
                    error_str = str(save_error)
                    logger.error(f"[process_image_generation_queue] Image {image_id} - Database save FAILED: {error_str}", exc_info=True)
                    logger.error(f"[process_image_generation_queue] Image {image_id} - Error type: {type(save_error).__name__}")
                    
                    # Continue even if save fails, but mark as failed in results
                    # Truncate error message to 180 chars to avoid same issue when saving error
                    truncated_error = error_str[:180] if len(error_str) > 180 else error_str
                    results.append({
                        'image_id': image_id,
                        'status': 'failed',
                        'error': f'Database save error: {truncated_error}'
                    })
                    failed += 1
                else:
                    # Deduct credits for successful image generation
                    credits_deducted = 0
                    cost_usd = result.get('cost', 0) or result.get('cost_usd', 0)  # generate_image returns 'cost'
                    if account:
                        try:
                            credits_deducted = CreditService.deduct_credits_for_image(
                                account=account,
                                model_name=model,
                                num_images=1,
                                description=f"Image generation: {content.title[:50] if content else 'Image'}" if content else f"Image {image_id}",
                                metadata={
                                    'image_id': image_id,
                                    'content_id': content_id,
                                    'provider': provider,
                                    'model': model,
                                    'image_type': image.image_type if image else 'unknown',
                                    'size': image_size,
                                },
                                cost_usd=cost_usd,
                                related_object_type='image',
                                related_object_id=image_id
                            )
                            logger.info(f"[process_image_generation_queue] Credits deducted for image {image_id}: account balance now {credits_deducted}")
                        except Exception as credit_error:
                            logger.error(f"[process_image_generation_queue] Failed to deduct credits for image {image_id}: {credit_error}")
                            # Don't fail the image generation if credit deduction fails
                    
                    # Update progress: Complete (100%)
                    self.update_state(
                        state='PROGRESS',
                        meta={
                            'current_image': index,
                            'total_images': total_images,
                            'completed': completed + 1,
                            'failed': failed,
                            'status': 'processing',
                            'current_image_id': image_id,
                            'current_image_progress': 100,
                            'results': results + [{
                                'image_id': image_id,
                                'status': 'completed',
                                'image_url': image_url,  # Original URL from API
                                'image_path': saved_file_path,  # Local file path if saved
                                'revised_prompt': result.get('revised_prompt')
                            }]
                        }
                    )
                    
                    results.append({
                        'image_id': image_id,
                        'status': 'completed',
                        'image_url': image_url,  # Original URL from API
                        'image_path': saved_file_path,  # Local file path if saved
                        'revised_prompt': result.get('revised_prompt')
                    })
                    completed += 1
            
        except Exception as e:
            logger.error(f"Error processing image {image_id}: {str(e)}", exc_info=True)
            results.append({
                'image_id': image_id,
                'status': 'failed',
                'error': str(e)
            })
            failed += 1
    
    # Check if all images for the content are generated and update status to 'review'
    if content_id and completed > 0:
        try:
            from igny8_core.business.content.models import Content, Images
            
            content = Content.objects.get(id=content_id)
            
            # Check if all images for this content are now generated
            all_images = Images.objects.filter(content=content)
            pending_images = all_images.filter(status='pending').count()
            
            # If no pending images and content is still in draft, move to review
            if pending_images == 0 and content.status == 'draft':
                content.status = 'review'
                content.save(update_fields=['status'])
                logger.info(f"[process_image_generation_queue] Content #{content_id} status updated to 'review' (all images generated)")
        except Exception as e:
            logger.error(f"[process_image_generation_queue] Error updating content status: {str(e)}", exc_info=True)
    
    # Final state
    logger.info("=" * 80)
    logger.info(f"process_image_generation_queue COMPLETED")
    logger.info(f"  - Total: {total_images}")
    logger.info(f"  - Completed: {completed}")
    logger.info(f"  - Failed: {failed}")
    logger.info("=" * 80)

    # Log to AITaskLog for consistency with other AI functions
    if account:
        try:
            from igny8_core.ai.models import AITaskLog
            import time

            # Calculate total cost from results
            total_cost = sum(r.get('cost', 0) for r in results if r.get('status') == 'completed')

            AITaskLog.objects.create(
                task_id=self.request.id,
                function_name='generate_images',
                account=account,
                phase='DONE' if completed > 0 else 'ERROR',
                message=f'Generated {completed} images ({failed} failed)' if completed > 0 else f'All {total_images} images failed',
                status='success' if completed > 0 else 'error',
                duration=0,  # Could track actual duration if needed
                cost=total_cost,
                tokens=0,  # Image generation doesn't use tokens
                request_steps=[{
                    'phase': 'IMAGE_GENERATION',
                    'status': 'success' if completed > 0 else 'error',
                    'message': f'Processed {total_images} images: {completed} completed, {failed} failed'
                }],
                response_steps=[],
                error=None if completed > 0 else f'All {total_images} images failed',
                payload={'image_ids': image_ids, 'content_id': content_id},
                result={
                    'total_images': total_images,
                    'completed': completed,
                    'failed': failed,
                    'model': model,
                    'provider': provider
                }
            )
            logger.info(f"[process_image_generation_queue] AITaskLog entry created")
        except Exception as log_error:
            logger.warning(f"[process_image_generation_queue] Failed to create AITaskLog: {log_error}")

    # Create notification for image generation completion
    if account:
        try:
            from igny8_core.business.notifications.services import NotificationService

            if completed > 0:
                NotificationService.notify_images_complete(
                    account=account,
                    image_count=completed
                )
            elif failed > 0:
                NotificationService.notify_images_failed(
                    account=account,
                    error=f'{failed} images failed to generate',
                    image_count=failed
                )
            logger.info(f"[process_image_generation_queue] Notification created")
        except Exception as notif_error:
            logger.warning(f"[process_image_generation_queue] Failed to create notification: {notif_error}")

    return {
        'success': True,
        'total_images': total_images,
        'completed': completed,
        'failed': failed,
        'results': results
    }