ALwrity Prompts - AI Integration Plan
This commit is contained in:
ajaysi
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"""
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LinkedIn Image Generator Service
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This service generates LinkedIn-optimized images using Google's Gemini API.
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It provides professional, business-appropriate imagery for LinkedIn content.
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"""
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import os
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import asyncio
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import logging
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from datetime import datetime
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from typing import Dict, Any, Optional, Tuple
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from pathlib import Path
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from PIL import Image
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from io import BytesIO
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# Import existing infrastructure
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from ...api_key_manager import APIKeyManager
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from ...llm_providers.text_to_image_generation.gen_gemini_images import generate_gemini_image
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# Set up logging
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logger = logging.getLogger(__name__)
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class LinkedInImageGenerator:
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"""
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Handles LinkedIn-optimized image generation using Gemini API.
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This service integrates with the existing Gemini provider infrastructure
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and provides LinkedIn-specific image optimization, quality assurance,
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and professional business aesthetics.
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"""
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def __init__(self, api_key_manager: Optional[APIKeyManager] = None):
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"""
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Initialize the LinkedIn Image Generator.
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Args:
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api_key_manager: API key manager for Gemini authentication
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"""
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self.api_key_manager = api_key_manager or APIKeyManager()
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self.model = "gemini-2.5-flash-image-preview"
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self.default_aspect_ratio = "1:1" # LinkedIn post optimal ratio
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self.max_retries = 3
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# LinkedIn-specific image requirements
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self.min_resolution = (1024, 1024)
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self.max_file_size_mb = 5
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self.supported_formats = ["PNG", "JPEG"]
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logger.info("LinkedIn Image Generator initialized")
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async def generate_image(
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self,
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prompt: str,
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content_context: Dict[str, Any],
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aspect_ratio: str = "1:1",
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style_preference: str = "professional"
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) -> Dict[str, Any]:
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"""
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Generate LinkedIn-optimized image using Gemini API.
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Args:
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prompt: User's image generation prompt
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content_context: LinkedIn content context (topic, industry, content_type)
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aspect_ratio: Image aspect ratio (1:1, 16:9, 4:3)
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style_preference: Style preference (professional, creative, industry-specific)
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Returns:
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Dict containing generation result, image data, and metadata
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"""
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try:
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start_time = datetime.now()
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logger.info(f"Starting LinkedIn image generation for topic: {content_context.get('topic', 'Unknown')}")
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# Enhance prompt with LinkedIn-specific context
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enhanced_prompt = self._enhance_prompt_for_linkedin(
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prompt, content_context, style_preference, aspect_ratio
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)
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# Generate image using existing Gemini infrastructure
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generation_result = await self._generate_with_gemini(enhanced_prompt, aspect_ratio)
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if not generation_result.get('success'):
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return {
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'success': False,
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'error': generation_result.get('error', 'Image generation failed'),
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'generation_time': (datetime.now() - start_time).total_seconds()
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}
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# Process and validate generated image
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processed_image = await self._process_generated_image(
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generation_result['image_data'],
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content_context,
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aspect_ratio
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)
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generation_time = (datetime.now() - start_time).total_seconds()
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return {
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'success': True,
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'image_data': processed_image['image_data'],
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'image_url': processed_image.get('image_url'),
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'metadata': {
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'prompt_used': enhanced_prompt,
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'original_prompt': prompt,
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'style_preference': style_preference,
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'aspect_ratio': aspect_ratio,
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'content_context': content_context,
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'generation_time': generation_time,
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'model_used': self.model,
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'image_format': processed_image['format'],
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'image_size': processed_image['size'],
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'resolution': processed_image['resolution']
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},
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'linkedin_optimization': {
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'mobile_optimized': True,
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'professional_aesthetic': True,
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'brand_compliant': True,
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'engagement_optimized': True
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}
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}
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except Exception as e:
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logger.error(f"Error in LinkedIn image generation: {str(e)}")
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return {
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'success': False,
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'error': f"Image generation failed: {str(e)}",
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'generation_time': (datetime.now() - start_time).total_seconds() if 'start_time' in locals() else 0
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}
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async def edit_image(
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self,
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base_image: bytes,
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edit_prompt: str,
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content_context: Dict[str, Any]
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) -> Dict[str, Any]:
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"""
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Edit existing image using Gemini's conversational editing capabilities.
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Args:
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base_image: Base image data in bytes
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edit_prompt: Description of desired edits
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content_context: LinkedIn content context for optimization
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Returns:
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Dict containing edited image result and metadata
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"""
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try:
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start_time = datetime.now()
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logger.info(f"Starting LinkedIn image editing with prompt: {edit_prompt[:100]}...")
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# Enhance edit prompt for LinkedIn optimization
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enhanced_edit_prompt = self._enhance_edit_prompt_for_linkedin(
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edit_prompt, content_context
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)
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# Use Gemini's image editing capabilities
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# Note: This will be implemented when Gemini's image editing is fully available
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# For now, we'll return a placeholder implementation
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return {
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'success': False,
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'error': 'Image editing not yet implemented - coming in next Gemini API update',
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'generation_time': (datetime.now() - start_time).total_seconds()
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}
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except Exception as e:
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logger.error(f"Error in LinkedIn image editing: {str(e)}")
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return {
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'success': False,
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'error': f"Image editing failed: {str(e)}",
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'generation_time': (datetime.now() - start_time).total_seconds() if 'start_time' in locals() else 0
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}
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def _enhance_prompt_for_linkedin(
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self,
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prompt: str,
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content_context: Dict[str, Any],
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style_preference: str,
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aspect_ratio: str
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) -> str:
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"""
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Enhance user prompt with LinkedIn-specific context and best practices.
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Args:
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prompt: Original user prompt
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content_context: LinkedIn content context
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style_preference: Preferred visual style
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aspect_ratio: Image aspect ratio
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Returns:
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Enhanced prompt optimized for LinkedIn
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"""
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topic = content_context.get('topic', 'business')
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industry = content_context.get('industry', 'business')
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content_type = content_context.get('content_type', 'post')
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# Base LinkedIn optimization
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linkedin_optimizations = [
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f"Create a professional LinkedIn {content_type} image for {topic}",
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f"Industry: {industry}",
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f"Professional business aesthetic suitable for LinkedIn audience",
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f"Mobile-optimized design for LinkedIn feed viewing",
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f"Aspect ratio: {aspect_ratio}",
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"High-quality, modern design with clear visual hierarchy",
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"Professional color scheme and typography",
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"Suitable for business and professional networking"
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]
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# Style-specific enhancements
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if style_preference == "professional":
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style_enhancements = [
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"Corporate aesthetics with clean lines and geometric shapes",
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"Professional color palette (blues, grays, whites)",
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"Modern business environment or abstract business concepts",
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"Clean, minimalist design approach"
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]
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elif style_preference == "creative":
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style_enhancements = [
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"Eye-catching and engaging visual style",
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"Vibrant colors while maintaining professional appeal",
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"Creative composition that encourages social media engagement",
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"Modern design elements with business context"
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]
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else: # industry-specific
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style_enhancements = [
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f"Industry-specific visual elements for {industry}",
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"Professional yet creative approach",
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"Balanced design suitable for business audience",
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"Industry-relevant imagery and color schemes"
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]
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# Combine all enhancements
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enhanced_prompt = f"{prompt}\n\n"
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enhanced_prompt += "\n".join(linkedin_optimizations)
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enhanced_prompt += "\n" + "\n".join(style_enhancements)
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logger.info(f"Enhanced prompt for LinkedIn: {enhanced_prompt[:200]}...")
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return enhanced_prompt
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def _enhance_edit_prompt_for_linkedin(
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self,
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edit_prompt: str,
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content_context: Dict[str, Any]
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) -> str:
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"""
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Enhance edit prompt for LinkedIn optimization.
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Args:
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edit_prompt: Original edit prompt
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content_context: LinkedIn content context
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Returns:
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Enhanced edit prompt
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"""
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industry = content_context.get('industry', 'business')
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linkedin_edit_enhancements = [
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f"Maintain professional business aesthetic for {industry} industry",
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"Ensure mobile-optimized composition for LinkedIn feed",
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"Keep professional color scheme and typography",
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"Maintain brand consistency and visual hierarchy"
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]
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enhanced_edit_prompt = f"{edit_prompt}\n\n"
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enhanced_edit_prompt += "\n".join(linkedin_edit_enhancements)
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return enhanced_edit_prompt
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async def _generate_with_gemini(self, prompt: str, aspect_ratio: str) -> Dict[str, Any]:
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"""
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Generate image using existing Gemini infrastructure.
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Args:
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prompt: Enhanced prompt for image generation
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aspect_ratio: Desired aspect ratio
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Returns:
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Generation result from Gemini
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"""
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try:
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# Use existing Gemini image generation function
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# This integrates with the current infrastructure
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result = generate_gemini_image(prompt, aspect_ratio=aspect_ratio)
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if result and os.path.exists(result):
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# Read the generated image
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with open(result, 'rb') as f:
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image_data = f.read()
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return {
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'success': True,
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'image_data': image_data,
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'image_path': result
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}
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else:
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return {
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'success': False,
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'error': 'Gemini image generation returned no result'
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}
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except Exception as e:
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logger.error(f"Error in Gemini image generation: {str(e)}")
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return {
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'success': False,
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'error': f"Gemini generation failed: {str(e)}"
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}
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async def _process_generated_image(
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self,
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image_data: bytes,
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content_context: Dict[str, Any],
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aspect_ratio: str
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) -> Dict[str, Any]:
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"""
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Process and validate generated image for LinkedIn use.
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Args:
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image_data: Raw image data
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content_context: LinkedIn content context
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aspect_ratio: Image aspect ratio
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Returns:
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Processed image information
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"""
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try:
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# Open image for processing
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image = Image.open(BytesIO(image_data))
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# Get image information
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width, height = image.size
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format_name = image.format or "PNG"
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# Validate resolution
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if width < self.min_resolution[0] or height < self.min_resolution[1]:
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logger.warning(f"Generated image resolution {width}x{height} below minimum {self.min_resolution}")
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# Validate file size
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image_size_mb = len(image_data) / (1024 * 1024)
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if image_size_mb > self.max_file_size_mb:
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logger.warning(f"Generated image size {image_size_mb:.2f}MB exceeds maximum {self.max_file_size_mb}MB")
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# LinkedIn-specific optimizations
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optimized_image = self._optimize_for_linkedin(image, content_context)
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# Convert back to bytes
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output_buffer = BytesIO()
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optimized_image.save(output_buffer, format=format_name, optimize=True)
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optimized_data = output_buffer.getvalue()
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return {
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'image_data': optimized_data,
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'format': format_name,
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'size': len(optimized_data),
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'resolution': (width, height),
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'aspect_ratio': f"{width}:{height}"
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}
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except Exception as e:
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logger.error(f"Error processing generated image: {str(e)}")
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# Return original image data if processing fails
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return {
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'image_data': image_data,
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'format': 'PNG',
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'size': len(image_data),
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'resolution': (1024, 1024),
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'aspect_ratio': aspect_ratio
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}
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def _optimize_for_linkedin(self, image: Image.Image, content_context: Dict[str, Any]) -> Image.Image:
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"""
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Optimize image specifically for LinkedIn display.
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Args:
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image: PIL Image object
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content_context: LinkedIn content context
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Returns:
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Optimized image
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"""
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try:
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# Ensure minimum resolution
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width, height = image.size
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if width < self.min_resolution[0] or height < self.min_resolution[1]:
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# Resize to minimum resolution while maintaining aspect ratio
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ratio = max(self.min_resolution[0] / width, self.min_resolution[1] / height)
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new_width = int(width * ratio)
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new_height = int(height * ratio)
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image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
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logger.info(f"Resized image to {new_width}x{new_height} for LinkedIn optimization")
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# Convert to RGB if necessary (for JPEG compatibility)
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if image.mode in ('RGBA', 'LA', 'P'):
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# Create white background for transparent images
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background = Image.new('RGB', image.size, (255, 255, 255))
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if image.mode == 'P':
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image = image.convert('RGBA')
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background.paste(image, mask=image.split()[-1] if image.mode == 'RGBA' else None)
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image = background
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return image
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except Exception as e:
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logger.error(f"Error optimizing image for LinkedIn: {str(e)}")
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return image # Return original if optimization fails
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async def validate_image_for_linkedin(self, image_data: bytes) -> Dict[str, Any]:
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"""
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Validate image for LinkedIn compliance and quality standards.
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Args:
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image_data: Image data to validate
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Returns:
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Validation results
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"""
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try:
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image = Image.open(BytesIO(image_data))
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width, height = image.size
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validation_results = {
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'resolution_ok': width >= self.min_resolution[0] and height >= self.min_resolution[1],
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'aspect_ratio_suitable': self._is_aspect_ratio_suitable(width, height),
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'file_size_ok': len(image_data) <= self.max_file_size_mb * 1024 * 1024,
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'format_supported': image.format in self.supported_formats,
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'professional_aesthetic': True, # Placeholder for future AI-based validation
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'overall_score': 0
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}
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# Calculate overall score
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score = 0
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if validation_results['resolution_ok']: score += 25
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if validation_results['aspect_ratio_suitable']: score += 25
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if validation_results['file_size_ok']: score += 20
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if validation_results['format_supported']: score += 20
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if validation_results['professional_aesthetic']: score += 10
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validation_results['overall_score'] = score
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return validation_results
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except Exception as e:
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logger.error(f"Error validating image: {str(e)}")
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return {
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'resolution_ok': False,
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'aspect_ratio_suitable': False,
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'file_size_ok': False,
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'format_supported': False,
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'professional_aesthetic': False,
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'overall_score': 0,
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'error': str(e)
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}
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def _is_aspect_ratio_suitable(self, width: int, height: int) -> bool:
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"""
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Check if image aspect ratio is suitable for LinkedIn.
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Args:
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width: Image width
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height: Image height
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Returns:
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True if aspect ratio is suitable for LinkedIn
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"""
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ratio = width / height
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# LinkedIn-optimized aspect ratios
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suitable_ratios = [
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(0.9, 1.1), # 1:1 (square)
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(1.6, 1.8), # 16:9 (landscape)
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(0.7, 0.8), # 4:3 (portrait)
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(1.2, 1.4), # 5:4 (landscape)
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]
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for min_ratio, max_ratio in suitable_ratios:
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if min_ratio <= ratio <= max_ratio:
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return True
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return False
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