"""
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 = None):
    """
    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
    """
    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:
        # Get account
        account = None
        if account_id:
            from igny8_core.auth.models import Account
            try:
                account = Account.objects.get(id=account_id)
            except Account.DoesNotExist:
                logger.warning(f"Account {account_id} not found")
        
        # 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
    
    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 IntegrationSettings
    try:
        image_settings = IntegrationSettings.objects.get(
            account=account,
            integration_type='image_generation',
            is_active=True
        )
        config = image_settings.config or {}
        provider = config.get('provider', 'openai')
        model = config.get('model', 'dall-e-3')
        image_type = config.get('image_type', 'realistic')
        image_format = config.get('image_format', 'webp')
        desktop_enabled = config.get('desktop_enabled', True)
        mobile_enabled = config.get('mobile_enabled', True)
    except IntegrationSettings.DoesNotExist:
        logger.error("Image generation settings not found")
        return {'success': False, 'error': 'Image generation settings not found'}
    
    # Get provider API key (using same approach as test image generation)
    # Note: API key is stored as 'apiKey' (camelCase) in IntegrationSettings.config
    try:
        provider_settings = IntegrationSettings.objects.get(
            account=account,
            integration_type=provider,  # 'openai' or 'runware'
            is_active=True
        )
        api_key = provider_settings.config.get('apiKey') if provider_settings.config else None
        if not api_key:
            logger.error(f"[process_image_generation_queue] {provider.upper()} API key not found in config")
            return {'success': False, 'error': f'{provider.upper()} API key not configured'}
        logger.info(f"[process_image_generation_queue] {provider.upper()} API key retrieved successfully")
    except IntegrationSettings.DoesNotExist:
        logger.error(f"[process_image_generation_queue] {provider.upper()} integration settings not found")
        return {'success': False, 'error': f'{provider.upper()} integration not found or not active'}
    except Exception as e:
        logger.error(f"[process_image_generation_queue] Error getting {provider} API key: {e}", exc_info=True)
        return {'success': False, 'error': f'Error retrieving {provider} API key: {str(e)}'}
    
    # 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 = 'Create a high-quality {image_type} image for a blog post titled "{post_title}". Image prompt: {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)
    
    # Process each image sequentially
    for index, image_id in enumerate(image_ids, 1):
        try:
            # Update task meta: current image processing
            self.update_state(
                state='PROGRESS',
                meta={
                    'current_image': index,
                    'total_images': total_images,
                    'completed': completed,
                    'failed': failed,
                    'status': 'processing',
                    'current_image_id': image_id,
                    'results': results
                }
            )
            
            # Load image record
            try:
                image = Images.objects.get(id=image_id, account=account)
            except Images.DoesNotExist:
                logger.error(f"Image {image_id} not found")
                results.append({
                    'image_id': image_id,
                    'status': 'failed',
                    'error': 'Image record not found'
                })
                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
            # Template has placeholders: {image_type}, {post_title}, {image_prompt}
            try:
                formatted_prompt = image_prompt_template.format(
                    image_type=image_type,
                    post_title=content.title or content.meta_title or f"Content #{content.id}",
                    image_prompt=image.prompt  # Read directly from database field
                )
            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
            
            # 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)}")
            logger.info(f"[process_image_generation_queue] Image type: {image_type}")
            
            result = ai_core.generate_image(
                prompt=formatted_prompt,
                provider=provider,
                model=model,
                size='1024x1024',
                api_key=api_key,
                negative_prompt=negative_prompt,
                function_name='generate_images_from_prompts'
            )
            
            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'):
                logger.error(f"Image generation failed for {image_id}: {result.get('error')}")
                # Update image record: failed
                image.status = 'failed'
                image.save(update_fields=['status'])
                
                results.append({
                    'image_id': image_id,
                    'status': 'failed',
                    'error': result.get('error')
                })
                failed += 1
            else:
                logger.info(f"Image generation successful for {image_id}")
                # Update image record: success
                image.image_url = result.get('url')
                image.status = 'generated'
                image.save(update_fields=['image_url', 'status'])
                
                results.append({
                    'image_id': image_id,
                    'status': 'completed',
                    'image_url': result.get('url'),
                    '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
    
    # 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)
    
    return {
        'success': True,
        'total_images': total_images,
        'completed': completed,
        'failed': failed,
        'results': results
    }