removeing unneceary code
This commit is contained in:
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@@ -6,7 +6,6 @@ from igny8_core.ai.functions.generate_ideas import GenerateIdeasFunction
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from igny8_core.ai.functions.generate_content import GenerateContentFunction
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from igny8_core.ai.functions.generate_images import GenerateImagesFunction, generate_images_core
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from igny8_core.ai.functions.generate_image_prompts import GenerateImagePromptsFunction
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from igny8_core.ai.functions.generate_images_from_prompts import GenerateImagesFromPromptsFunction
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__all__ = [
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'AutoClusterFunction',
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@@ -15,5 +14,4 @@ __all__ = [
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'GenerateImagesFunction',
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'generate_images_core',
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'GenerateImagePromptsFunction',
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'GenerateImagesFromPromptsFunction',
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]
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@@ -1,472 +0,0 @@
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"""
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Generate Images from Prompts AI Function
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Generates actual images from existing image prompts using AI
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"""
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import logging
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from typing import Dict, List, Any
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from django.db import transaction
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from igny8_core.ai.base import BaseAIFunction
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from igny8_core.modules.writer.models import Images, Content
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from igny8_core.ai.ai_core import AICore
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from igny8_core.ai.validators import validate_ids
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from igny8_core.ai.prompts import PromptRegistry
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logger = logging.getLogger(__name__)
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class GenerateImagesFromPromptsFunction(BaseAIFunction):
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"""Generate actual images from image prompts using AI"""
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def get_name(self) -> str:
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return 'generate_images_from_prompts'
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def get_metadata(self) -> Dict:
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return {
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'display_name': 'Generate Images from Prompts',
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'description': 'Generate actual images from existing image prompts',
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'phases': {
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'INIT': 'Validating image prompts...',
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'PREP': 'Preparing image generation queue...',
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'AI_CALL': 'Generating images with AI...',
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'PARSE': 'Processing image URLs...',
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'SAVE': 'Saving image URLs...',
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'DONE': 'Images generated!'
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}
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}
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def get_max_items(self) -> int:
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return 100 # Max images per batch
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def validate(self, payload: dict, account=None) -> Dict:
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"""Validate image IDs exist and have prompts"""
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result = validate_ids(payload, max_items=self.get_max_items())
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if not result['valid']:
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return result
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# Check images exist and have prompts
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image_ids = payload.get('ids', [])
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if image_ids:
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queryset = Images.objects.filter(id__in=image_ids)
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if account:
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queryset = queryset.filter(account=account)
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images = list(queryset.select_related('content', 'task'))
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if not images:
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return {
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'valid': False,
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'error': 'No images found with provided IDs'
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}
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# Check all images have prompts
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images_without_prompts = [img.id for img in images if not img.prompt or not img.prompt.strip()]
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if images_without_prompts:
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return {
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'valid': False,
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'error': f'Images {images_without_prompts} do not have prompts'
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}
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# Check all images are pending
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images_not_pending = [img.id for img in images if img.status != 'pending']
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if images_not_pending:
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return {
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'valid': False,
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'error': f'Images {images_not_pending} are not in pending status'
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}
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return {'valid': True}
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def prepare(self, payload: dict, account=None) -> Dict:
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"""Load images and image generation settings"""
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image_ids = payload.get('ids', [])
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queryset = Images.objects.filter(id__in=image_ids, status='pending')
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if account:
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queryset = queryset.filter(account=account)
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images = list(queryset.select_related('content', 'task', 'account', 'site', 'sector'))
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if not images:
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raise ValueError("No pending images found with prompts")
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# Get image generation settings - CHECK IF ENABLED
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image_settings = {}
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image_generation_enabled = False
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if account:
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try:
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from igny8_core.modules.system.models import IntegrationSettings
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integration = IntegrationSettings.objects.get(
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account=account,
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integration_type='image_generation'
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)
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image_generation_enabled = integration.is_active
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image_settings = integration.config or {}
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logger.info(f"[generate_images_from_prompts] Image generation settings: enabled={image_generation_enabled}, config_keys={list(image_settings.keys())}")
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except IntegrationSettings.DoesNotExist:
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logger.warning(f"[generate_images_from_prompts] Image generation integration not found for account {account.id}")
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raise ValueError("Image generation integration not configured")
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except Exception as e:
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logger.error(f"[generate_images_from_prompts] Failed to load image generation settings: {e}")
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raise ValueError(f"Failed to load image generation settings: {str(e)}")
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if not image_generation_enabled:
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raise ValueError("Image generation is not enabled in settings")
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# Get provider from image_generation settings
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provider = image_settings.get('provider') or image_settings.get('service', 'openai')
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logger.info(f"[generate_images_from_prompts] Provider from settings: {provider}")
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# Get provider-specific settings (OpenAI or Runware) - CHECK IF ENABLED
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provider_api_key = None
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provider_enabled = False
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provider_model = None
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if provider == 'openai':
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try:
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openai_settings = IntegrationSettings.objects.get(
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account=account,
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integration_type='openai'
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)
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provider_enabled = openai_settings.is_active
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provider_api_key = openai_settings.config.get('apiKey') if openai_settings.config else None
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provider_model = openai_settings.config.get('model') if openai_settings.config else None
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logger.info(f"[generate_images_from_prompts] OpenAI settings: enabled={provider_enabled}, has_key={bool(provider_api_key)}, model={provider_model}")
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except IntegrationSettings.DoesNotExist:
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logger.error(f"[generate_images_from_prompts] OpenAI integration not found")
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raise ValueError("OpenAI integration not configured")
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except Exception as e:
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logger.error(f"[generate_images_from_prompts] Error getting OpenAI settings: {e}")
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raise ValueError(f"Failed to load OpenAI settings: {str(e)}")
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elif provider == 'runware':
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try:
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runware_settings = IntegrationSettings.objects.get(
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account=account,
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integration_type='runware'
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)
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provider_enabled = runware_settings.is_active
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provider_api_key = runware_settings.config.get('apiKey') if runware_settings.config else None
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provider_model = runware_settings.config.get('model') if runware_settings.config else None
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logger.info(f"[generate_images_from_prompts] Runware settings: enabled={provider_enabled}, has_key={bool(provider_api_key)}, model={provider_model}")
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except IntegrationSettings.DoesNotExist:
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logger.error(f"[generate_images_from_prompts] Runware integration not found")
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raise ValueError("Runware integration not configured")
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except Exception as e:
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logger.error(f"[generate_images_from_prompts] Error getting Runware settings: {e}")
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raise ValueError(f"Failed to load Runware settings: {str(e)}")
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else:
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raise ValueError(f"Invalid provider: {provider}")
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# Validate provider is enabled and has API key
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if not provider_enabled:
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raise ValueError(f"{provider.capitalize()} integration is not enabled")
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if not provider_api_key:
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raise ValueError(f"{provider.capitalize()} API key not configured")
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# Determine model: from provider settings, or image_generation settings, or default
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if provider_model:
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model = provider_model
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elif provider == 'runware':
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model = image_settings.get('model') or image_settings.get('runwareModel', 'runware:97@1')
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else:
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model = image_settings.get('model') or image_settings.get('imageModel', 'dall-e-3')
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logger.info(f"[generate_images_from_prompts] Final settings: provider={provider}, model={model}, enabled={provider_enabled}, has_api_key={bool(provider_api_key)}")
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# Get prompt templates
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image_prompt_template = PromptRegistry.get_image_prompt_template(account)
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negative_prompt = PromptRegistry.get_negative_prompt(account)
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return {
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'images': images,
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'account': account,
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'provider': provider,
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'model': model,
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'api_key': provider_api_key, # Include API key
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'image_type': image_settings.get('image_type', 'realistic'),
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'image_format': image_settings.get('image_format', 'webp'),
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'image_prompt_template': image_prompt_template,
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'negative_prompt': negative_prompt,
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}
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def build_prompt(self, data: Dict, account=None) -> str:
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"""
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Build prompt for AI_CALL phase.
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For image generation, we return a placeholder since we process images in save_output.
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"""
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# Return placeholder - actual processing happens in save_output
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return "Image generation queue prepared"
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def parse_response(self, response: str, step_tracker=None) -> Dict:
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"""
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Parse response from AI_CALL.
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For image generation, we process images directly in save_output, so this is a placeholder.
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"""
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return {'processed': True}
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def save_output(
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self,
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parsed: Dict,
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original_data: Dict,
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account=None,
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progress_tracker=None,
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step_tracker=None,
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console_tracker=None
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) -> Dict:
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"""
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Process all images sequentially and generate them.
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This method handles the loop and makes AI calls directly.
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"""
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function_name = self.get_name()
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if console_tracker:
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console_tracker.save(f"[{function_name}] Starting image generation queue")
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images = original_data.get('images', [])
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if not images:
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error_msg = "[{function_name}] No images to process"
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if console_tracker:
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console_tracker.error('ValidationError', error_msg)
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raise ValueError(error_msg)
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provider = original_data.get('provider', 'openai')
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model = original_data.get('model', 'dall-e-3')
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api_key = original_data.get('api_key') # Get API key from prepare
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image_type = original_data.get('image_type', 'realistic')
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image_prompt_template = original_data.get('image_prompt_template', '')
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negative_prompt = original_data.get('negative_prompt', '')
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# Validate API key is present
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if not api_key:
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error_msg = f"[{function_name}] API key not found for provider {provider}"
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if console_tracker:
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console_tracker.error('ConfigurationError', error_msg)
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raise ValueError(error_msg)
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ai_core = AICore(account=account or original_data.get('account'))
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total_images = len(images)
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images_generated = 0
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images_failed = 0
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errors = []
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if console_tracker:
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console_tracker.prep(f"[{function_name}] Preparing {total_images} image{'s' if total_images != 1 else ''} for generation")
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# Initialize image queue in meta for frontend
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image_queue = []
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for idx, img in enumerate(images, 1):
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content_obj = img.content
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if not content_obj:
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if img.task:
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content_title = img.task.title
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else:
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content_title = "Content"
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else:
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content_title = content_obj.title or content_obj.meta_title or "Content"
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image_queue.append({
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'image_id': img.id,
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'index': idx,
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'label': f"{img.image_type.replace('_', ' ').title()} Image",
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'content_title': content_title,
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'status': 'pending',
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'progress': 0,
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'image_url': None,
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'error': None
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})
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# Send initial queue to frontend
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if progress_tracker:
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initial_meta = step_tracker.get_meta() if step_tracker else {}
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initial_meta['image_queue'] = image_queue
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progress_tracker.update("PREP", 10, f"Preparing to generate {total_images} image{'s' if total_images != 1 else ''}", meta=initial_meta)
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if console_tracker:
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console_tracker.prep(f"[{function_name}] Image queue initialized with {total_images} image{'s' if total_images != 1 else ''}")
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console_tracker.prep(f"[{function_name}] Provider: {provider}, Model: {model}, API Key: {'***' + api_key[-4:] if api_key and len(api_key) > 4 else 'NOT SET'}")
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# Queue all prompts first (TEST MODE - don't send to AI)
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queued_prompts = []
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# Process each image sequentially
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for index, image in enumerate(images, 1):
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queue_item = image_queue[index - 1]
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queue_item['status'] = 'processing'
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queue_item['progress'] = 0
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try:
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# Get content title
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content = image.content
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if not content:
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# Fallback to task if no content
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if image.task:
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content_title = image.task.title
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else:
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content_title = "Content"
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else:
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content_title = content.title or content.meta_title or "Content"
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if console_tracker:
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console_tracker.prep(f"[{function_name}] Processing image {index}/{total_images}: {image.image_type} for '{content_title}'")
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# Format prompt using template
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if image_prompt_template:
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try:
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formatted_prompt = image_prompt_template.format(
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post_title=content_title,
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image_prompt=image.prompt,
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image_type=image_type
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)
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if console_tracker:
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console_tracker.prep(f"[{function_name}] Formatted prompt using template (length: {len(formatted_prompt)})")
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except KeyError as e:
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logger.warning(f"Template formatting error: {e}, using simple format")
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formatted_prompt = f"Create a high-quality {image_type} image: {image.prompt}"
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if console_tracker:
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console_tracker.prep(f"[{function_name}] Template formatting error, using fallback prompt")
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else:
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# Fallback template
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formatted_prompt = f"Create a high-quality {image_type} image: {image.prompt}"
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if console_tracker:
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console_tracker.prep(f"[{function_name}] Using fallback prompt template")
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# Update progress: PREP phase for this image
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if progress_tracker and step_tracker:
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prep_msg = f"Generating image {index} of {total_images}: {image.image_type}"
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step_tracker.add_request_step("PREP", "success", prep_msg)
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queue_item['progress'] = 10
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# Update queue in meta
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meta = step_tracker.get_meta()
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meta['image_queue'] = image_queue
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progress_pct = 10 + int((index - 1) / total_images * 15) # 10-25% for PREP
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progress_tracker.update("PREP", progress_pct, prep_msg, meta=meta)
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# Generate image - update progress incrementally
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if progress_tracker and step_tracker:
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ai_msg = f"Generating {image.image_type} image {index} of {total_images} with AI"
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step_tracker.add_response_step("AI_CALL", "success", ai_msg)
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queue_item['progress'] = 25
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meta = step_tracker.get_meta()
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meta['image_queue'] = image_queue
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progress_pct = 25 + int((index - 1) / total_images * 45) # 25-70% for AI_CALL
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progress_tracker.update("AI_CALL", progress_pct, ai_msg, meta=meta)
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# Update progress to 50% (simulating API call start)
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queue_item['progress'] = 50
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if progress_tracker and step_tracker:
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meta = step_tracker.get_meta()
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meta['image_queue'] = image_queue
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progress_tracker.update("AI_CALL", progress_pct, ai_msg, meta=meta)
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# Queue the complete prompt (TEST MODE - don't send to AI yet)
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queued_prompts.append({
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'image_id': image.id,
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'index': index,
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'image_type': image.image_type,
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'content_title': content_title,
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'provider': provider,
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'model': model,
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'formatted_prompt': formatted_prompt,
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'negative_prompt': negative_prompt if provider == 'runware' else None,
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'prompt_length': len(formatted_prompt)
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})
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if console_tracker:
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console_tracker.ai_call(f"[{function_name}] [TEST MODE] Queued prompt {index}/{total_images}: {image.image_type} for '{content_title}'")
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console_tracker.ai_call(f"[{function_name}] [TEST MODE] Provider: {provider}, Model: {model}")
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console_tracker.ai_call(f"[{function_name}] [TEST MODE] Prompt length: {len(formatted_prompt)} chars")
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console_tracker.ai_call(f"[{function_name}] [TEST MODE] Prompt preview: {formatted_prompt[:150]}...")
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# TEMPORARY: Simulate result for testing (don't actually call AI)
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result = {
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'url': None,
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'error': None,
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'test_mode': True,
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'queued': True
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}
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# ACTUAL AI CALL (COMMENTED OUT FOR TESTING)
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# if console_tracker:
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# console_tracker.ai_call(f"[{function_name}] Calling {provider}/{model} API for image {index}/{total_images}")
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#
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# result = ai_core.generate_image(
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# prompt=formatted_prompt,
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# provider=provider,
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# model=model,
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# size='1024x1024',
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# n=1,
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# api_key=api_key, # Pass API key explicitly
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# negative_prompt=negative_prompt if provider == 'runware' else None,
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# function_name='generate_images_from_prompts'
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# )
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# TEST MODE: Mark as queued (not actually generated)
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queue_item['status'] = 'completed'
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queue_item['progress'] = 100
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queue_item['image_url'] = None # No URL in test mode
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if console_tracker:
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console_tracker.parse(f"[{function_name}] [TEST MODE] Prompt queued for image {index}/{total_images}")
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console_tracker.save(f"[{function_name}] [TEST MODE] Queued image {index}/{total_images} (ID: {image.id})")
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# Update progress: SAVE phase
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if progress_tracker and step_tracker:
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save_msg = f"Queued prompt {index} of {total_images} (TEST MODE)"
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step_tracker.add_request_step("SAVE", "success", save_msg)
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meta = step_tracker.get_meta()
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meta['image_queue'] = image_queue
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||||
progress_pct = 85 + int((index - 1) / total_images * 13) # 85-98% for SAVE
|
||||
progress_tracker.update("SAVE", progress_pct, save_msg, meta=meta)
|
||||
|
||||
except Exception as e:
|
||||
# Mark as failed
|
||||
queue_item['status'] = 'failed'
|
||||
queue_item['progress'] = 100
|
||||
queue_item['error'] = str(e)
|
||||
with transaction.atomic():
|
||||
image.status = 'failed'
|
||||
image.save(update_fields=['status', 'updated_at'])
|
||||
|
||||
error_msg = f"[{function_name}] Image {index}/{total_images} exception: {str(e)}"
|
||||
errors.append(error_msg)
|
||||
images_failed += 1
|
||||
logger.error(f"Exception generating image {image.id}: {str(e)}", exc_info=True)
|
||||
|
||||
if console_tracker:
|
||||
console_tracker.error('Exception', error_msg)
|
||||
|
||||
continue
|
||||
|
||||
# Log all queued prompts (TEST MODE)
|
||||
if console_tracker:
|
||||
console_tracker.save(f"[{function_name}] [TEST MODE] All prompts queued. Total: {len(queued_prompts)} prompts")
|
||||
console_tracker.save(f"[{function_name}] [TEST MODE] Provider: {provider}, Model: {model}")
|
||||
for qp in queued_prompts:
|
||||
console_tracker.save(f"[{function_name}] [TEST MODE] Image {qp['index']}: {qp['image_type']} - '{qp['content_title']}'")
|
||||
console_tracker.save(f"[{function_name}] [TEST MODE] Prompt ({qp['prompt_length']} chars): {qp['formatted_prompt'][:100]}...")
|
||||
|
||||
# Final progress update
|
||||
if progress_tracker and step_tracker:
|
||||
final_msg = f"Queued {len(queued_prompts)} prompts (TEST MODE - not sent to AI)"
|
||||
step_tracker.add_request_step("SAVE", "success", final_msg)
|
||||
meta = step_tracker.get_meta()
|
||||
meta['image_queue'] = image_queue
|
||||
meta['queued_prompts'] = queued_prompts # Include queued prompts in meta
|
||||
progress_tracker.update("SAVE", 98, final_msg, meta=meta)
|
||||
|
||||
if console_tracker:
|
||||
console_tracker.done(f"[{function_name}] [TEST MODE] Queued {len(queued_prompts)}/{total_images} prompts successfully (NOT sent to AI)")
|
||||
|
||||
return {
|
||||
'count': len(queued_prompts),
|
||||
'images_generated': 0, # 0 because we're in test mode
|
||||
'images_failed': 0,
|
||||
'total_images': total_images,
|
||||
'queued_prompts': queued_prompts, # Return queued prompts
|
||||
'test_mode': True,
|
||||
'provider': provider,
|
||||
'model': model,
|
||||
'errors': errors if errors else None
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user