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.
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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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