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removeing unneceary code

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This commit is contained in:
Desktop
2025-11-12 04:20:43 +05:00
parent d1d2d768e5
commit 4bd158ce01
10 changed files with 130 additions and 1004 deletions
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3
backend/igny8_core/ai/base.py
View File

@@ -84,8 +84,7 @@ class BaseAIFunction(ABC):
original_data: Any,
account=None,
progress_tracker=None,
step_tracker=None,
console_tracker=None
step_tracker=None
) -> Dict:
"""
Save parsed results to database.

266
backend/igny8_core/ai/engine.py
View File

@@ -4,7 +4,7 @@ AI Engine - Central orchestrator for all AI functions
import logging
from typing import Dict, Any, Optional
from igny8_core.ai.base import BaseAIFunction
from igny8_core.ai.tracker import StepTracker, ProgressTracker, CostTracker, ConsoleStepTracker
from igny8_core.ai.tracker import StepTracker, ProgressTracker, CostTracker
from igny8_core.ai.ai_core import AICore
from igny8_core.ai.settings import get_model_config
@@ -22,7 +22,6 @@ class AIEngine:
self.account = account
self.tracker = ProgressTracker(celery_task)
self.step_tracker = StepTracker('ai_engine') # For Celery progress callbacks
self.console_tracker = None # Will be initialized per function
self.cost_tracker = CostTracker()
def _get_input_description(self, function_name: str, payload: dict, count: int) -> str:
@@ -81,12 +80,6 @@ class AIEngine:
total_images = 1 + max_images
return f"Mapping Content for {total_images} Image Prompts"
return f"Mapping Content for Image Prompts"
elif function_name == 'generate_images_from_prompts':
# Extract image count from data
if isinstance(data, dict) and 'images' in data:
total_images = len(data.get('images', []))
return f"Preparing to generate {total_images} image{'s' if total_images != 1 else ''}"
return f"Preparing image generation queue"
return f"Preparing {count} item{'s' if count != 1 else ''}"
def _get_ai_call_message(self, function_name: str, count: int) -> str:
@@ -99,8 +92,6 @@ class AIEngine:
return f"Writing article{'s' if count != 1 else ''} with AI"
elif function_name == 'generate_images':
return f"Creating image{'s' if count != 1 else ''} with AI"
elif function_name == 'generate_images_from_prompts':
return f"Generating images with AI"
return f"Processing with AI"
def _get_parse_message(self, function_name: str) -> str:
@@ -131,8 +122,6 @@ class AIEngine:
if in_article_count > 0:
return f"Writing {in_article_count} In‑article Image Prompts"
return "Writing In‑article Image Prompts"
elif function_name == 'generate_images_from_prompts':
return f"{count} image{'s' if count != 1 else ''} generated"
return f"{count} item{'s' if count != 1 else ''} processed"
def _get_save_message(self, function_name: str, count: int) -> str:
@@ -148,8 +137,6 @@ class AIEngine:
elif function_name == 'generate_image_prompts':
# Count is total prompts created
return f"Assigning {count} Prompts to Dedicated Slots"
elif function_name == 'generate_images_from_prompts':
return f"Saving {count} image{'s' if count != 1 else ''}"
return f"Saving {count} item{'s' if count != 1 else ''}"
def execute(self, fn: BaseAIFunction, payload: dict) -> dict:
@@ -167,10 +154,6 @@ class AIEngine:
function_name = fn.get_name()
self.step_tracker.function_name = function_name
# Initialize console tracker for logging (Stage 3 requirement)
self.console_tracker = ConsoleStepTracker(function_name)
self.console_tracker.init(f"Starting {function_name} execution")
try:
# Phase 1: INIT - Validation & Setup (0-10%)
# Extract input data for user-friendly messages
@@ -178,16 +161,12 @@ class AIEngine:
input_count = len(ids) if ids else 0
input_description = self._get_input_description(function_name, payload, input_count)
self.console_tracker.prep(f"Validating {input_description}")
validated = fn.validate(payload, self.account)
if not validated['valid']:
self.console_tracker.error('ValidationError', validated['error'])
return self._handle_error(validated['error'], fn)
# Build validation message with keyword names for auto_cluster
validation_message = self._build_validation_message(function_name, payload, input_count, input_description)
self.console_tracker.prep("Validation complete")
self.step_tracker.add_request_step("INIT", "success", validation_message)
self.tracker.update("INIT", 10, validation_message, meta=self.step_tracker.get_meta())
@@ -207,149 +186,127 @@ class AIEngine:
data_count = input_count
prep_message = self._get_prep_message(function_name, data_count, data)
self.console_tracker.prep(prep_message)
# For image generation, build_prompt returns placeholder
# Actual processing happens in save_output
if function_name == 'generate_images_from_prompts':
prompt = "Image generation queue prepared"
else:
prompt = fn.build_prompt(data, self.account)
self.console_tracker.prep(f"Prompt built: {len(prompt)} characters")
prompt = fn.build_prompt(data, self.account)
self.step_tracker.add_request_step("PREP", "success", prep_message)
self.tracker.update("PREP", 25, prep_message, meta=self.step_tracker.get_meta())
# Phase 3: AI_CALL - Provider API Call (25-70%)
# For image generation, AI calls happen in save_output, so skip this phase
if function_name == 'generate_images_from_prompts':
# Skip AI_CALL phase - processing happens in save_output
raw_response = {'content': 'Image generation queue ready'}
parsed = {'processed': True}
else:
ai_core = AICore(account=self.account)
function_name = fn.get_name()
# Generate function_id for tracking (ai-{function_name}-01)
# Normalize underscores to hyphens to match frontend tracking IDs
function_id_base = function_name.replace('_', '-')
function_id = f"ai-{function_id_base}-01-desktop"
# Get model config from settings (Stage 4 requirement)
# Pass account to read model from IntegrationSettings
model_config = get_model_config(function_name, account=self.account)
model = model_config.get('model')
# Read model straight from IntegrationSettings for visibility
model_from_integration = None
if self.account:
try:
from igny8_core.modules.system.models import IntegrationSettings
openai_settings = IntegrationSettings.objects.filter(
integration_type='openai',
account=self.account,
is_active=True
).first()
if openai_settings and openai_settings.config:
model_from_integration = openai_settings.config.get('model')
except Exception as integration_error:
logger.warning(
"[AIEngine] Unable to read model from IntegrationSettings: %s",
integration_error,
exc_info=True,
)
# Debug logging: Show model configuration (console only, not in step tracker)
logger.info(f"[AIEngine] Model Configuration for {function_name}:")
logger.info(f" - Model from get_model_config: {model}")
logger.info(f" - Full model_config: {model_config}")
self.console_tracker.ai_call(f"Model from settings: {model_from_integration or 'Not set'}")
self.console_tracker.ai_call(f"Model selected for request: {model or 'default'}")
self.console_tracker.ai_call(f"Calling {model or 'default'} model with {len(prompt)} char prompt")
self.console_tracker.ai_call(f"Function ID: {function_id}")
# Track AI call start with user-friendly message
ai_call_message = self._get_ai_call_message(function_name, data_count)
self.step_tracker.add_response_step("AI_CALL", "success", ai_call_message)
self.tracker.update("AI_CALL", 50, ai_call_message, meta=self.step_tracker.get_meta())
ai_core = AICore(account=self.account)
function_name = fn.get_name()
# Generate function_id for tracking (ai-{function_name}-01)
# Normalize underscores to hyphens to match frontend tracking IDs
function_id_base = function_name.replace('_', '-')
function_id = f"ai-{function_id_base}-01-desktop"
# Get model config from settings (Stage 4 requirement)
# Pass account to read model from IntegrationSettings
model_config = get_model_config(function_name, account=self.account)
model = model_config.get('model')
# Read model straight from IntegrationSettings for visibility
model_from_integration = None
if self.account:
try:
# Use centralized run_ai_request() with console logging (Stage 2 & 3 requirement)
# Pass console_tracker for unified logging
raw_response = ai_core.run_ai_request(
prompt=prompt,
model=model,
max_tokens=model_config.get('max_tokens'),
temperature=model_config.get('temperature'),
response_format=model_config.get('response_format'),
function_name=function_name,
function_id=function_id, # Pass function_id for tracking
tracker=self.console_tracker # Pass console tracker for logging
from igny8_core.modules.system.models import IntegrationSettings
openai_settings = IntegrationSettings.objects.filter(
integration_type='openai',
account=self.account,
is_active=True
).first()
if openai_settings and openai_settings.config:
model_from_integration = openai_settings.config.get('model')
except Exception as integration_error:
logger.warning(
"[AIEngine] Unable to read model from IntegrationSettings: %s",
integration_error,
exc_info=True,
)
except Exception as e:
error_msg = f"AI call failed: {str(e)}"
logger.error(f"Exception during AI call: {error_msg}", exc_info=True)
return self._handle_error(error_msg, fn)
if raw_response.get('error'):
error_msg = raw_response.get('error', 'Unknown AI error')
logger.error(f"AI call returned error: {error_msg}")
return self._handle_error(error_msg, fn)
if not raw_response.get('content'):
error_msg = "AI call returned no content"
logger.error(error_msg)
return self._handle_error(error_msg, fn)
# Track cost
self.cost_tracker.record(
# Debug logging: Show model configuration (console only, not in step tracker)
logger.info(f"[AIEngine] Model Configuration for {function_name}:")
logger.info(f" - Model from get_model_config: {model}")
logger.info(f" - Full model_config: {model_config}")
# Track AI call start with user-friendly message
ai_call_message = self._get_ai_call_message(function_name, data_count)
self.step_tracker.add_response_step("AI_CALL", "success", ai_call_message)
self.tracker.update("AI_CALL", 50, ai_call_message, meta=self.step_tracker.get_meta())
try:
# Use centralized run_ai_request()
raw_response = ai_core.run_ai_request(
prompt=prompt,
model=model,
max_tokens=model_config.get('max_tokens'),
temperature=model_config.get('temperature'),
response_format=model_config.get('response_format'),
function_name=function_name,
cost=raw_response.get('cost', 0),
tokens=raw_response.get('total_tokens', 0),
model=raw_response.get('model')
function_id=function_id # Pass function_id for tracking
)
except Exception as e:
error_msg = f"AI call failed: {str(e)}"
logger.error(f"Exception during AI call: {error_msg}", exc_info=True)
return self._handle_error(error_msg, fn)
if raw_response.get('error'):
error_msg = raw_response.get('error', 'Unknown AI error')
logger.error(f"AI call returned error: {error_msg}")
return self._handle_error(error_msg, fn)
if not raw_response.get('content'):
error_msg = "AI call returned no content"
logger.error(error_msg)
return self._handle_error(error_msg, fn)
# Track cost
self.cost_tracker.record(
function_name=function_name,
cost=raw_response.get('cost', 0),
tokens=raw_response.get('total_tokens', 0),
model=raw_response.get('model')
)
# Update AI_CALL step with results
self.step_tracker.response_steps[-1] = {
**self.step_tracker.response_steps[-1],
'message': f"Received {raw_response.get('total_tokens', 0)} tokens, Cost: ${raw_response.get('cost', 0):.6f}",
'duration': raw_response.get('duration')
}
self.tracker.update("AI_CALL", 70, f"AI response received ({raw_response.get('total_tokens', 0)} tokens)", meta=self.step_tracker.get_meta())
# Phase 4: PARSE - Response Parsing (70-85%)
try:
parse_message = self._get_parse_message(function_name)
response_content = raw_response.get('content', '')
parsed = fn.parse_response(response_content, self.step_tracker)
# Update AI_CALL step with results
self.step_tracker.response_steps[-1] = {
**self.step_tracker.response_steps[-1],
'message': f"Received {raw_response.get('total_tokens', 0)} tokens, Cost: ${raw_response.get('cost', 0):.6f}",
'duration': raw_response.get('duration')
}
self.tracker.update("AI_CALL", 70, f"AI response received ({raw_response.get('total_tokens', 0)} tokens)", meta=self.step_tracker.get_meta())
# Phase 4: PARSE - Response Parsing (70-85%)
try:
parse_message = self._get_parse_message(function_name)
self.console_tracker.parse(parse_message)
response_content = raw_response.get('content', '')
parsed = fn.parse_response(response_content, self.step_tracker)
if isinstance(parsed, (list, tuple)):
parsed_count = len(parsed)
elif isinstance(parsed, dict):
# Check if it's a content dict (has 'content' field) or a result dict (has 'count')
if 'content' in parsed:
parsed_count = 1 # Single content item
else:
parsed_count = parsed.get('count', 1)
if isinstance(parsed, (list, tuple)):
parsed_count = len(parsed)
elif isinstance(parsed, dict):
# Check if it's a content dict (has 'content' field) or a result dict (has 'count')
if 'content' in parsed:
parsed_count = 1 # Single content item
else:
parsed_count = 1
# Update parse message with count for better UX
parse_message = self._get_parse_message_with_count(function_name, parsed_count)
self.console_tracker.parse(f"Successfully parsed {parsed_count} items from response")
self.step_tracker.add_response_step("PARSE", "success", parse_message)
self.tracker.update("PARSE", 85, parse_message, meta=self.step_tracker.get_meta())
except Exception as parse_error:
error_msg = f"Failed to parse AI response: {str(parse_error)}"
logger.error(f"AIEngine: {error_msg}", exc_info=True)
logger.error(f"AIEngine: Response content was: {response_content[:500] if response_content else 'None'}...")
return self._handle_error(error_msg, fn)
parsed_count = parsed.get('count', 1)
else:
parsed_count = 1
# Update parse message with count for better UX
parse_message = self._get_parse_message_with_count(function_name, parsed_count)
self.step_tracker.add_response_step("PARSE", "success", parse_message)
self.tracker.update("PARSE", 85, parse_message, meta=self.step_tracker.get_meta())
except Exception as parse_error:
error_msg = f"Failed to parse AI response: {str(parse_error)}"
logger.error(f"AIEngine: {error_msg}", exc_info=True)
logger.error(f"AIEngine: Response content was: {response_content[:500] if response_content else 'None'}...")
return self._handle_error(error_msg, fn)
# Phase 5: SAVE - Database Operations (85-98%)
# Pass step_tracker and console_tracker to save_output so it can add validation steps and log
save_result = fn.save_output(parsed, data, self.account, self.tracker, step_tracker=self.step_tracker, console_tracker=self.console_tracker)
save_result = fn.save_output(parsed, data, self.account, self.tracker, step_tracker=self.step_tracker)
clusters_created = save_result.get('clusters_created', 0)
keywords_updated = save_result.get('keywords_updated', 0)
count = save_result.get('count', 0)
@@ -362,7 +319,6 @@ class AIEngine:
else:
save_msg = self._get_save_message(function_name, data_count)
self.console_tracker.save(save_msg)
self.step_tracker.add_request_step("SAVE", "success", save_msg)
self.tracker.update("SAVE", 98, save_msg, meta=self.step_tracker.get_meta())
@@ -403,7 +359,6 @@ class AIEngine:
# Phase 6: DONE - Finalization (98-100%)
success_msg = f"Task completed: {final_save_msg}" if 'final_save_msg' in locals() else "Task completed successfully"
self.console_tracker.done(success_msg)
self.step_tracker.add_request_step("DONE", "success", "Task completed successfully")
self.tracker.update("DONE", 100, "Task complete!", meta=self.step_tracker.get_meta())
@@ -427,11 +382,6 @@ class AIEngine:
"""Centralized error handling"""
function_name = fn.get_name() if fn else 'unknown'
# Log to console tracker if available (Stage 3 requirement)
if self.console_tracker:
error_type = type(error).__name__ if isinstance(error, Exception) else 'Error'
self.console_tracker.error(error_type, str(error), exception=error if isinstance(error, Exception) else None)
self.step_tracker.add_request_step("Error", "error", error, error=error)
error_meta = {

2
backend/igny8_core/ai/functions/__init__.py
View File

@@ -6,7 +6,6 @@ from igny8_core.ai.functions.generate_ideas import GenerateIdeasFunction
from igny8_core.ai.functions.generate_content import GenerateContentFunction
from igny8_core.ai.functions.generate_images import GenerateImagesFunction, generate_images_core
from igny8_core.ai.functions.generate_image_prompts import GenerateImagePromptsFunction
from igny8_core.ai.functions.generate_images_from_prompts import GenerateImagesFromPromptsFunction
__all__ = [
'AutoClusterFunction',
@@ -15,5 +14,4 @@ __all__ = [
'GenerateImagesFunction',
'generate_images_core',
'GenerateImagePromptsFunction',
'GenerateImagesFromPromptsFunction',
]

472
backend/igny8_core/ai/functions/generate_images_from_prompts.py
View File

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

6
backend/igny8_core/ai/registry.py
View File

@@ -94,15 +94,9 @@ def _load_generate_image_prompts():
from igny8_core.ai.functions.generate_image_prompts import GenerateImagePromptsFunction
return GenerateImagePromptsFunction
def _load_generate_images_from_prompts():
"""Lazy loader for generate_images_from_prompts function"""
from igny8_core.ai.functions.generate_images_from_prompts import GenerateImagesFromPromptsFunction
return GenerateImagesFromPromptsFunction
register_lazy_function('auto_cluster', _load_auto_cluster)
register_lazy_function('generate_ideas', _load_generate_ideas)
register_lazy_function('generate_content', _load_generate_content)
register_lazy_function('generate_images', _load_generate_images)
register_lazy_function('generate_image_prompts', _load_generate_image_prompts)
register_lazy_function('generate_images_from_prompts', _load_generate_images_from_prompts)

6
backend/igny8_core/ai/settings.py
View File

@@ -40,12 +40,6 @@ MODEL_CONFIG = {
"temperature": 0.7,
"response_format": {"type": "json_object"},
},
"generate_images_from_prompts": {
"model": "dall-e-3", # Default, overridden by IntegrationSettings
"max_tokens": None, # Not used for images
"temperature": None, # Not used for images
"response_format": None, # Not used for images
},
}
# Function name aliases (for backward compatibility)

67
backend/igny8_core/modules/writer/views.py
View File

@@ -514,73 +514,6 @@ class ImagesViewSet(SiteSectorModelViewSet):
'results': grouped_data
}, status=status.HTTP_200_OK)
@action(detail=False, methods=['post'], url_path='generate_images', url_name='generate_images')
def generate_images(self, request):
"""Generate images from prompts for image records"""
from igny8_core.ai.tasks import run_ai_task
account = getattr(request, 'account', None)
ids = request.data.get('ids', [])
if not ids:
return Response({
'error': 'No IDs provided',
'type': 'ValidationError'
}, status=status.HTTP_400_BAD_REQUEST)
account_id = account.id if account else None
# Queue Celery task
try:
if hasattr(run_ai_task, 'delay'):
task = run_ai_task.delay(
function_name='generate_images_from_prompts',
payload={'ids': ids},
account_id=account_id
)
return Response({
'success': True,
'task_id': str(task.id),
'message': 'Image generation started'
}, status=status.HTTP_200_OK)
else:
# Fallback to synchronous execution
result = run_ai_task(
function_name='generate_images_from_prompts',
payload={'ids': ids},
account_id=account_id
)
if result.get('success'):
# Include queued prompts in response for TEST MODE
response_data = {
'success': True,
'images_generated': result.get('images_generated', 0),
'images_failed': result.get('images_failed', 0),
'count': result.get('count', 0),
'total_images': result.get('total_images', 0),
'message': 'Images generated successfully'
}
# Add test mode data if available
if result.get('queued_prompts'):
response_data['queued_prompts'] = result.get('queued_prompts')
response_data['test_mode'] = result.get('test_mode', False)
response_data['provider'] = result.get('provider')
response_data['model'] = result.get('model')
logger.info(f"[generate_images] Response: {response_data}")
return Response(response_data, status=status.HTTP_200_OK)
else:
return Response({
'error': result.get('error', 'Image generation failed'),
'type': 'TaskExecutionError'
}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
except Exception as e:
return Response({
'error': str(e),
'type': 'ExecutionError'
}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
class ContentViewSet(SiteSectorModelViewSet):
"""
ViewSet for managing task content