Add Image Generation from Prompts: Implement new functionality to generate images from prompts, including backend processing, API integration, and frontend handling with progress modal. Update settings and registry for new AI function.
This commit is contained in:
IGNY8 VPS (Salman)
@@ -81,6 +81,12 @@ class AIEngine:
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total_images = 1 + max_images
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return f"Mapping Content for {total_images} Image Prompts"
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return f"Mapping Content for Image Prompts"
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elif function_name == 'generate_images_from_prompts':
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# Extract image count from data
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if isinstance(data, dict) and 'images' in data:
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total_images = len(data.get('images', []))
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return f"Preparing to generate {total_images} image{'s' if total_images != 1 else ''}"
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return f"Preparing image generation queue"
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return f"Preparing {count} item{'s' if count != 1 else ''}"
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def _get_ai_call_message(self, function_name: str, count: int) -> str:
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@@ -93,6 +99,8 @@ class AIEngine:
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return f"Writing article{'s' if count != 1 else ''} with AI"
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elif function_name == 'generate_images':
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return f"Creating image{'s' if count != 1 else ''} with AI"
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elif function_name == 'generate_images_from_prompts':
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return f"Generating images with AI"
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return f"Processing with AI"
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def _get_parse_message(self, function_name: str) -> str:
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@@ -123,6 +131,8 @@ class AIEngine:
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if in_article_count > 0:
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return f"Writing {in_article_count} In‑article Image Prompts"
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return "Writing In‑article Image Prompts"
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elif function_name == 'generate_images_from_prompts':
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return f"{count} image{'s' if count != 1 else ''} generated"
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return f"{count} item{'s' if count != 1 else ''} processed"
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def _get_save_message(self, function_name: str, count: int) -> str:
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@@ -138,6 +148,8 @@ class AIEngine:
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elif function_name == 'generate_image_prompts':
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# Count is total prompts created
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return f"Assigning {count} Prompts to Dedicated Slots"
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elif function_name == 'generate_images_from_prompts':
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return f"Saving {count} image{'s' if count != 1 else ''}"
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return f"Saving {count} item{'s' if count != 1 else ''}"
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def execute(self, fn: BaseAIFunction, payload: dict) -> dict:
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@@ -196,131 +208,144 @@ class AIEngine:
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prep_message = self._get_prep_message(function_name, data_count, data)
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self.console_tracker.prep(prep_message)
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prompt = fn.build_prompt(data, self.account)
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self.console_tracker.prep(f"Prompt built: {len(prompt)} characters")
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# For image generation, build_prompt returns placeholder
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# Actual processing happens in save_output
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if function_name == 'generate_images_from_prompts':
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prompt = "Image generation queue prepared"
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else:
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prompt = fn.build_prompt(data, self.account)
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self.console_tracker.prep(f"Prompt built: {len(prompt)} characters")
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self.step_tracker.add_request_step("PREP", "success", prep_message)
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self.tracker.update("PREP", 25, prep_message, meta=self.step_tracker.get_meta())
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# Phase 3: AI_CALL - Provider API Call (25-70%)
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ai_core = AICore(account=self.account)
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function_name = fn.get_name()
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# Generate function_id for tracking (ai-{function_name}-01)
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# Normalize underscores to hyphens to match frontend tracking IDs
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function_id_base = function_name.replace('_', '-')
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function_id = f"ai-{function_id_base}-01-desktop"
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# Get model config from settings (Stage 4 requirement)
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# Pass account to read model from IntegrationSettings
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model_config = get_model_config(function_name, account=self.account)
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model = model_config.get('model')
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# Read model straight from IntegrationSettings for visibility
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model_from_integration = None
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if self.account:
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# For image generation, AI calls happen in save_output, so skip this phase
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if function_name == 'generate_images_from_prompts':
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# Skip AI_CALL phase - processing happens in save_output
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raw_response = {'content': 'Image generation queue ready'}
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parsed = {'processed': True}
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else:
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ai_core = AICore(account=self.account)
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function_name = fn.get_name()
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# Generate function_id for tracking (ai-{function_name}-01)
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# Normalize underscores to hyphens to match frontend tracking IDs
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function_id_base = function_name.replace('_', '-')
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function_id = f"ai-{function_id_base}-01-desktop"
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# Get model config from settings (Stage 4 requirement)
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# Pass account to read model from IntegrationSettings
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model_config = get_model_config(function_name, account=self.account)
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model = model_config.get('model')
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# Read model straight from IntegrationSettings for visibility
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model_from_integration = None
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if self.account:
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try:
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from igny8_core.modules.system.models import IntegrationSettings
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openai_settings = IntegrationSettings.objects.filter(
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integration_type='openai',
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account=self.account,
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is_active=True
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).first()
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if openai_settings and openai_settings.config:
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model_from_integration = openai_settings.config.get('model')
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except Exception as integration_error:
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logger.warning(
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"[AIEngine] Unable to read model from IntegrationSettings: %s",
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integration_error,
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exc_info=True,
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)
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# Debug logging: Show model configuration (console only, not in step tracker)
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logger.info(f"[AIEngine] Model Configuration for {function_name}:")
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logger.info(f" - Model from get_model_config: {model}")
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logger.info(f" - Full model_config: {model_config}")
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self.console_tracker.ai_call(f"Model from settings: {model_from_integration or 'Not set'}")
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self.console_tracker.ai_call(f"Model selected for request: {model or 'default'}")
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self.console_tracker.ai_call(f"Calling {model or 'default'} model with {len(prompt)} char prompt")
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self.console_tracker.ai_call(f"Function ID: {function_id}")
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# Track AI call start with user-friendly message
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ai_call_message = self._get_ai_call_message(function_name, data_count)
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self.step_tracker.add_response_step("AI_CALL", "success", ai_call_message)
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self.tracker.update("AI_CALL", 50, ai_call_message, meta=self.step_tracker.get_meta())
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try:
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from igny8_core.modules.system.models import IntegrationSettings
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openai_settings = IntegrationSettings.objects.filter(
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integration_type='openai',
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account=self.account,
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is_active=True
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).first()
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if openai_settings and openai_settings.config:
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model_from_integration = openai_settings.config.get('model')
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except Exception as integration_error:
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logger.warning(
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"[AIEngine] Unable to read model from IntegrationSettings: %s",
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integration_error,
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exc_info=True,
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# Use centralized run_ai_request() with console logging (Stage 2 & 3 requirement)
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# Pass console_tracker for unified logging
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raw_response = ai_core.run_ai_request(
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prompt=prompt,
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model=model,
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max_tokens=model_config.get('max_tokens'),
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temperature=model_config.get('temperature'),
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response_format=model_config.get('response_format'),
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function_name=function_name,
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function_id=function_id, # Pass function_id for tracking
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tracker=self.console_tracker # Pass console tracker for logging
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)
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# Debug logging: Show model configuration (console only, not in step tracker)
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logger.info(f"[AIEngine] Model Configuration for {function_name}:")
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logger.info(f" - Model from get_model_config: {model}")
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logger.info(f" - Full model_config: {model_config}")
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self.console_tracker.ai_call(f"Model from settings: {model_from_integration or 'Not set'}")
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self.console_tracker.ai_call(f"Model selected for request: {model or 'default'}")
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self.console_tracker.ai_call(f"Calling {model or 'default'} model with {len(prompt)} char prompt")
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self.console_tracker.ai_call(f"Function ID: {function_id}")
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# Track AI call start with user-friendly message
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ai_call_message = self._get_ai_call_message(function_name, data_count)
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self.step_tracker.add_response_step("AI_CALL", "success", ai_call_message)
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self.tracker.update("AI_CALL", 50, ai_call_message, meta=self.step_tracker.get_meta())
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try:
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# Use centralized run_ai_request() with console logging (Stage 2 & 3 requirement)
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# Pass console_tracker for unified logging
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raw_response = ai_core.run_ai_request(
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prompt=prompt,
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model=model,
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max_tokens=model_config.get('max_tokens'),
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temperature=model_config.get('temperature'),
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response_format=model_config.get('response_format'),
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except Exception as e:
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error_msg = f"AI call failed: {str(e)}"
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logger.error(f"Exception during AI call: {error_msg}", exc_info=True)
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return self._handle_error(error_msg, fn)
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if raw_response.get('error'):
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error_msg = raw_response.get('error', 'Unknown AI error')
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logger.error(f"AI call returned error: {error_msg}")
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return self._handle_error(error_msg, fn)
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if not raw_response.get('content'):
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error_msg = "AI call returned no content"
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logger.error(error_msg)
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return self._handle_error(error_msg, fn)
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# Track cost
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self.cost_tracker.record(
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function_name=function_name,
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function_id=function_id, # Pass function_id for tracking
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tracker=self.console_tracker # Pass console tracker for logging
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cost=raw_response.get('cost', 0),
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tokens=raw_response.get('total_tokens', 0),
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model=raw_response.get('model')
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)
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except Exception as e:
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error_msg = f"AI call failed: {str(e)}"
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logger.error(f"Exception during AI call: {error_msg}", exc_info=True)
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return self._handle_error(error_msg, fn)
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if raw_response.get('error'):
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error_msg = raw_response.get('error', 'Unknown AI error')
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logger.error(f"AI call returned error: {error_msg}")
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return self._handle_error(error_msg, fn)
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if not raw_response.get('content'):
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error_msg = "AI call returned no content"
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logger.error(error_msg)
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return self._handle_error(error_msg, fn)
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# Track cost
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self.cost_tracker.record(
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function_name=function_name,
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cost=raw_response.get('cost', 0),
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tokens=raw_response.get('total_tokens', 0),
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model=raw_response.get('model')
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)
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# Update AI_CALL step with results
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self.step_tracker.response_steps[-1] = {
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**self.step_tracker.response_steps[-1],
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'message': f"Received {raw_response.get('total_tokens', 0)} tokens, Cost: ${raw_response.get('cost', 0):.6f}",
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'duration': raw_response.get('duration')
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}
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self.tracker.update("AI_CALL", 70, f"AI response received ({raw_response.get('total_tokens', 0)} tokens)", meta=self.step_tracker.get_meta())
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# Phase 4: PARSE - Response Parsing (70-85%)
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try:
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parse_message = self._get_parse_message(function_name)
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self.console_tracker.parse(parse_message)
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response_content = raw_response.get('content', '')
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parsed = fn.parse_response(response_content, self.step_tracker)
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if isinstance(parsed, (list, tuple)):
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parsed_count = len(parsed)
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elif isinstance(parsed, dict):
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# Check if it's a content dict (has 'content' field) or a result dict (has 'count')
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if 'content' in parsed:
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parsed_count = 1 # Single content item
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# Update AI_CALL step with results
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self.step_tracker.response_steps[-1] = {
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**self.step_tracker.response_steps[-1],
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'message': f"Received {raw_response.get('total_tokens', 0)} tokens, Cost: ${raw_response.get('cost', 0):.6f}",
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'duration': raw_response.get('duration')
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}
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self.tracker.update("AI_CALL", 70, f"AI response received ({raw_response.get('total_tokens', 0)} tokens)", meta=self.step_tracker.get_meta())
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# Phase 4: PARSE - Response Parsing (70-85%)
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try:
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parse_message = self._get_parse_message(function_name)
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self.console_tracker.parse(parse_message)
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response_content = raw_response.get('content', '')
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parsed = fn.parse_response(response_content, self.step_tracker)
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if isinstance(parsed, (list, tuple)):
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parsed_count = len(parsed)
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elif isinstance(parsed, dict):
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# Check if it's a content dict (has 'content' field) or a result dict (has 'count')
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if 'content' in parsed:
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parsed_count = 1 # Single content item
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else:
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parsed_count = parsed.get('count', 1)
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else:
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parsed_count = parsed.get('count', 1)
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else:
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parsed_count = 1
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# Update parse message with count for better UX
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parse_message = self._get_parse_message_with_count(function_name, parsed_count)
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self.console_tracker.parse(f"Successfully parsed {parsed_count} items from response")
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self.step_tracker.add_response_step("PARSE", "success", parse_message)
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self.tracker.update("PARSE", 85, parse_message, meta=self.step_tracker.get_meta())
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except Exception as parse_error:
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error_msg = f"Failed to parse AI response: {str(parse_error)}"
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logger.error(f"AIEngine: {error_msg}", exc_info=True)
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logger.error(f"AIEngine: Response content was: {response_content[:500] if response_content else 'None'}...")
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return self._handle_error(error_msg, fn)
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parsed_count = 1
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# Update parse message with count for better UX
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parse_message = self._get_parse_message_with_count(function_name, parsed_count)
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self.console_tracker.parse(f"Successfully parsed {parsed_count} items from response")
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self.step_tracker.add_response_step("PARSE", "success", parse_message)
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self.tracker.update("PARSE", 85, parse_message, meta=self.step_tracker.get_meta())
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except Exception as parse_error:
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error_msg = f"Failed to parse AI response: {str(parse_error)}"
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logger.error(f"AIEngine: {error_msg}", exc_info=True)
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logger.error(f"AIEngine: Response content was: {response_content[:500] if response_content else 'None'}...")
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return self._handle_error(error_msg, fn)
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# Phase 5: SAVE - Database Operations (85-98%)
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# Pass step_tracker to save_output so it can add validation steps
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@@ -6,6 +6,7 @@ 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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@@ -14,4 +15,5 @@ __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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311
backend/igny8_core/ai/functions/generate_images_from_prompts.py
Normal file
311
backend/igny8_core/ai/functions/generate_images_from_prompts.py
Normal file
@@ -0,0 +1,311 @@
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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")
|
||||
|
||||
# Get image generation settings
|
||||
image_settings = {}
|
||||
if account:
|
||||
try:
|
||||
from igny8_core.modules.system.models import IntegrationSettings
|
||||
integration = IntegrationSettings.objects.get(
|
||||
account=account,
|
||||
integration_type='image_generation',
|
||||
is_active=True
|
||||
)
|
||||
image_settings = integration.config or {}
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to load image generation settings: {e}")
|
||||
|
||||
# Extract settings with defaults
|
||||
provider = image_settings.get('provider') or image_settings.get('service', 'openai')
|
||||
if provider == 'runware':
|
||||
model = image_settings.get('model') or image_settings.get('runwareModel', 'runware:97@1')
|
||||
else:
|
||||
model = image_settings.get('model', 'dall-e-3')
|
||||
|
||||
# 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,
|
||||
'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
|
||||
) -> Dict:
|
||||
"""
|
||||
Process all images sequentially and generate them.
|
||||
This method handles the loop and makes AI calls directly.
|
||||
"""
|
||||
images = original_data.get('images', [])
|
||||
if not images:
|
||||
raise ValueError("No images to process")
|
||||
|
||||
provider = original_data.get('provider', 'openai')
|
||||
model = original_data.get('model', 'dall-e-3')
|
||||
image_type = original_data.get('image_type', 'realistic')
|
||||
image_prompt_template = original_data.get('image_prompt_template', '')
|
||||
negative_prompt = original_data.get('negative_prompt', '')
|
||||
|
||||
ai_core = AICore(account=account or original_data.get('account'))
|
||||
|
||||
total_images = len(images)
|
||||
images_generated = 0
|
||||
images_failed = 0
|
||||
errors = []
|
||||
|
||||
# Process each image sequentially
|
||||
for index, image in enumerate(images, 1):
|
||||
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"
|
||||
|
||||
# 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
|
||||
)
|
||||
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}"
|
||||
else:
|
||||
# Fallback template
|
||||
formatted_prompt = f"Create a high-quality {image_type} image: {image.prompt}"
|
||||
|
||||
# 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)
|
||||
progress_pct = 10 + int((index - 1) / total_images * 15) # 10-25% for PREP
|
||||
progress_tracker.update("PREP", progress_pct, prep_msg, meta=step_tracker.get_meta())
|
||||
|
||||
# Generate image
|
||||
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)
|
||||
progress_pct = 25 + int((index - 1) / total_images * 45) # 25-70% for AI_CALL
|
||||
progress_tracker.update("AI_CALL", progress_pct, ai_msg, meta=step_tracker.get_meta())
|
||||
|
||||
result = ai_core.generate_image(
|
||||
prompt=formatted_prompt,
|
||||
provider=provider,
|
||||
model=model,
|
||||
size='1024x1024',
|
||||
negative_prompt=negative_prompt if provider == 'runware' else None,
|
||||
function_name='generate_images_from_prompts'
|
||||
)
|
||||
|
||||
if result.get('error'):
|
||||
# Mark as failed
|
||||
with transaction.atomic():
|
||||
image.status = 'failed'
|
||||
image.save(update_fields=['status', 'updated_at'])
|
||||
|
||||
error_msg = f"Image {index} failed: {result['error']}"
|
||||
errors.append(error_msg)
|
||||
images_failed += 1
|
||||
logger.error(f"Image generation failed for image {image.id}: {result['error']}")
|
||||
|
||||
if progress_tracker and step_tracker:
|
||||
parse_msg = f"Image {index} failed: {result['error']}"
|
||||
step_tracker.add_response_step("PARSE", "error", parse_msg)
|
||||
progress_pct = 70 + int((index - 1) / total_images * 15) # 70-85% for PARSE
|
||||
progress_tracker.update("PARSE", progress_pct, parse_msg, meta=step_tracker.get_meta())
|
||||
|
||||
continue
|
||||
|
||||
image_url = result.get('url')
|
||||
if not image_url:
|
||||
# Mark as failed
|
||||
with transaction.atomic():
|
||||
image.status = 'failed'
|
||||
image.save(update_fields=['status', 'updated_at'])
|
||||
|
||||
error_msg = f"Image {index} failed: No URL returned"
|
||||
errors.append(error_msg)
|
||||
images_failed += 1
|
||||
logger.error(f"No image URL returned for image {image.id}")
|
||||
|
||||
if progress_tracker and step_tracker:
|
||||
parse_msg = f"Image {index} failed: No URL returned"
|
||||
step_tracker.add_response_step("PARSE", "error", parse_msg)
|
||||
progress_pct = 70 + int((index - 1) / total_images * 15)
|
||||
progress_tracker.update("PARSE", progress_pct, parse_msg, meta=step_tracker.get_meta())
|
||||
|
||||
continue
|
||||
|
||||
# Update progress: PARSE phase
|
||||
if progress_tracker and step_tracker:
|
||||
parse_msg = f"Image {index} of {total_images} generated successfully"
|
||||
step_tracker.add_response_step("PARSE", "success", parse_msg)
|
||||
progress_pct = 70 + int((index - 1) / total_images * 15) # 70-85% for PARSE
|
||||
progress_tracker.update("PARSE", progress_pct, parse_msg, meta=step_tracker.get_meta())
|
||||
|
||||
# Update image record
|
||||
with transaction.atomic():
|
||||
image.image_url = image_url
|
||||
image.status = 'generated'
|
||||
image.save(update_fields=['image_url', 'status', 'updated_at'])
|
||||
|
||||
images_generated += 1
|
||||
logger.info(f"Image {image.id} ({image.image_type}) generated successfully: {image_url}")
|
||||
|
||||
# Update progress: SAVE phase
|
||||
if progress_tracker and step_tracker:
|
||||
save_msg = f"Saved image {index} of {total_images}"
|
||||
step_tracker.add_request_step("SAVE", "success", save_msg)
|
||||
progress_pct = 85 + int((index - 1) / total_images * 13) # 85-98% for SAVE
|
||||
progress_tracker.update("SAVE", progress_pct, save_msg, meta=step_tracker.get_meta())
|
||||
|
||||
except Exception as e:
|
||||
# Mark as failed
|
||||
with transaction.atomic():
|
||||
image.status = 'failed'
|
||||
image.save(update_fields=['status', 'updated_at'])
|
||||
|
||||
error_msg = f"Image {index} failed: {str(e)}"
|
||||
errors.append(error_msg)
|
||||
images_failed += 1
|
||||
logger.error(f"Exception generating image {image.id}: {str(e)}", exc_info=True)
|
||||
continue
|
||||
|
||||
# Final progress update
|
||||
if progress_tracker and step_tracker:
|
||||
final_msg = f"Generated {images_generated} of {total_images} images"
|
||||
step_tracker.add_request_step("SAVE", "success", final_msg)
|
||||
progress_tracker.update("SAVE", 98, final_msg, meta=step_tracker.get_meta())
|
||||
|
||||
return {
|
||||
'count': images_generated,
|
||||
'images_generated': images_generated,
|
||||
'images_failed': images_failed,
|
||||
'total_images': total_images,
|
||||
'errors': errors if errors else None
|
||||
}
|
||||
|
||||
@@ -94,9 +94,15 @@ 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)
|
||||
|
||||
|
||||
@@ -40,6 +40,12 @@ 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)
|
||||
|
||||
@@ -513,6 +513,60 @@ class ImagesViewSet(SiteSectorModelViewSet):
|
||||
'count': len(grouped_data),
|
||||
'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'):
|
||||
return Response({
|
||||
'success': True,
|
||||
'images_generated': result.get('count', 0),
|
||||
'images_failed': result.get('images_failed', 0),
|
||||
'message': 'Images generated successfully'
|
||||
}, 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):
|
||||
|
||||
Reference in New Issue
Block a user