531 lines
20 KiB
Python
531 lines
20 KiB
Python
"""
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LinkedIn Image Editor Service
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This service handles image editing capabilities for LinkedIn content using Gemini's
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conversational editing features. It provides professional image refinement and
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optimization specifically for LinkedIn use cases.
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"""
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import os
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import base64
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from typing import Dict, Any, Optional, List
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from datetime import datetime
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from PIL import Image, ImageEnhance, ImageFilter
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from io import BytesIO
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from loguru import logger
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# Import existing infrastructure
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from ...onboarding.api_key_manager import APIKeyManager
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class LinkedInImageEditor:
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"""
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Handles LinkedIn image editing and refinement using Gemini's capabilities.
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This service provides both AI-powered editing through Gemini and traditional
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image processing for LinkedIn-specific optimizations.
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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 Editor.
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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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# LinkedIn-specific editing parameters
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self.enhancement_factors = {
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'brightness': 1.1, # Slightly brighter for mobile viewing
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'contrast': 1.05, # Subtle contrast enhancement
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'sharpness': 1.2, # Enhanced sharpness for clarity
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'saturation': 1.05 # Slight saturation boost
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}
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logger.info("LinkedIn Image Editor initialized")
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async def edit_image_conversationally(
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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 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: Natural language 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 conversational image editing: {edit_prompt[:100]}...")
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# Enhance edit prompt for LinkedIn optimization
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enhanced_prompt = self._enhance_edit_prompt_for_linkedin(
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edit_prompt, content_context
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)
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# TODO: Implement Gemini conversational editing when available
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# For now, we'll use traditional image processing based on prompt analysis
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edited_image = await self._apply_traditional_editing(
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base_image, edit_prompt, content_context
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)
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if not edited_image.get('success'):
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return edited_image
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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': edited_image['image_data'],
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'metadata': {
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'edit_prompt': edit_prompt,
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'enhanced_prompt': enhanced_prompt,
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'editing_method': 'traditional_processing',
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'editing_time': generation_time,
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'content_context': content_context,
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'model_used': self.model
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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 conversational image editing: {str(e)}")
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return {
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'success': False,
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'error': f"Conversational 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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async def apply_style_transfer(
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self,
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base_image: bytes,
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style_reference: bytes,
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content_context: Dict[str, Any]
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) -> Dict[str, Any]:
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"""
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Apply style transfer from reference image to base image.
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Args:
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base_image: Base image data in bytes
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style_reference: Reference image for style transfer
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content_context: LinkedIn content context
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Returns:
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Dict containing style-transferred image result
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"""
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try:
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start_time = datetime.now()
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logger.info("Starting style transfer for LinkedIn image")
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# TODO: Implement Gemini style transfer when available
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# For now, return placeholder implementation
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return {
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'success': False,
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'error': 'Style transfer 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 style transfer: {str(e)}")
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return {
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'success': False,
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'error': f"Style transfer 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 enhance_image_quality(
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self,
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image_data: bytes,
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enhancement_type: str = "linkedin_optimized",
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content_context: Optional[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""
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Enhance image quality using traditional image processing.
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Args:
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image_data: Image data in bytes
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enhancement_type: Type of enhancement to apply
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content_context: LinkedIn content context for optimization
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Returns:
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Dict containing enhanced image result
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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 image quality enhancement: {enhancement_type}")
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# Open image for processing
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image = Image.open(BytesIO(image_data))
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original_size = image.size
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# Apply LinkedIn-specific enhancements
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if enhancement_type == "linkedin_optimized":
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enhanced_image = self._apply_linkedin_enhancements(image, content_context)
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elif enhancement_type == "professional":
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enhanced_image = self._apply_professional_enhancements(image)
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elif enhancement_type == "creative":
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enhanced_image = self._apply_creative_enhancements(image)
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else:
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enhanced_image = self._apply_linkedin_enhancements(image, content_context)
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# Convert back to bytes
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output_buffer = BytesIO()
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enhanced_image.save(output_buffer, format=image.format or "PNG", optimize=True)
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enhanced_data = output_buffer.getvalue()
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enhancement_time = (datetime.now() - start_time).total_seconds()
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return {
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'success': True,
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'image_data': enhanced_data,
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'metadata': {
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'enhancement_type': enhancement_type,
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'original_size': original_size,
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'enhanced_size': enhanced_image.size,
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'enhancement_time': enhancement_time,
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'content_context': content_context
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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 image quality enhancement: {str(e)}")
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return {
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'success': False,
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'error': f"Quality enhancement 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_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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content_type = content_context.get('content_type', 'post')
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linkedin_edit_enhancements = [
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f"Maintain professional business aesthetic for {industry} industry",
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f"Ensure mobile-optimized composition for LinkedIn {content_type}",
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"Keep professional color scheme and typography",
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"Maintain brand consistency and visual hierarchy",
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"Optimize for LinkedIn feed viewing and engagement"
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]
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enhanced_prompt = f"{edit_prompt}\n\n"
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enhanced_prompt += "\n".join(linkedin_edit_enhancements)
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return enhanced_prompt
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async def _apply_traditional_editing(
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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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Apply traditional image processing based on edit prompt analysis.
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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
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Returns:
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Dict containing edited image result
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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(base_image))
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# Analyze edit prompt and apply appropriate processing
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edit_prompt_lower = edit_prompt.lower()
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if any(word in edit_prompt_lower for word in ['brighter', 'light', 'lighting']):
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image = self._adjust_brightness(image, 1.2)
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logger.info("Applied brightness adjustment")
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if any(word in edit_prompt_lower for word in ['sharper', 'sharp', 'clear']):
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image = self._apply_sharpening(image)
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logger.info("Applied sharpening")
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if any(word in edit_prompt_lower for word in ['warmer', 'warm', 'color']):
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image = self._adjust_color_temperature(image, 'warm')
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logger.info("Applied warm color adjustment")
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if any(word in edit_prompt_lower for word in ['professional', 'business']):
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image = self._apply_professional_enhancements(image)
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logger.info("Applied professional enhancements")
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# Convert back to bytes
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output_buffer = BytesIO()
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image.save(output_buffer, format=image.format or "PNG", optimize=True)
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edited_data = output_buffer.getvalue()
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return {
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'success': True,
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'image_data': edited_data
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}
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except Exception as e:
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logger.error(f"Error in traditional editing: {str(e)}")
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return {
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'success': False,
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'error': f"Traditional editing failed: {str(e)}"
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}
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def _apply_linkedin_enhancements(
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self,
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image: Image.Image,
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content_context: Optional[Dict[str, Any]] = None
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) -> Image.Image:
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"""
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Apply LinkedIn-specific image enhancements.
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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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Enhanced image
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"""
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try:
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# Apply standard LinkedIn optimizations
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image = self._adjust_brightness(image, self.enhancement_factors['brightness'])
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image = self._adjust_contrast(image, self.enhancement_factors['contrast'])
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image = self._apply_sharpening(image)
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image = self._adjust_saturation(image, self.enhancement_factors['saturation'])
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# Ensure professional appearance
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image = self._ensure_professional_appearance(image, content_context)
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return image
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except Exception as e:
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logger.error(f"Error applying LinkedIn enhancements: {str(e)}")
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return image
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def _apply_professional_enhancements(self, image: Image.Image) -> Image.Image:
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"""
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Apply professional business aesthetic enhancements.
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Args:
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image: PIL Image object
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Returns:
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Enhanced image
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"""
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try:
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# Subtle enhancements for professional appearance
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image = self._adjust_brightness(image, 1.05)
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image = self._adjust_contrast(image, 1.03)
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image = self._apply_sharpening(image)
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return image
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except Exception as e:
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logger.error(f"Error applying professional enhancements: {str(e)}")
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return image
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def _apply_creative_enhancements(self, image: Image.Image) -> Image.Image:
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"""
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Apply creative and engaging enhancements.
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Args:
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image: PIL Image object
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Returns:
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Enhanced image
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"""
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try:
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# More pronounced enhancements for creative appeal
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image = self._adjust_brightness(image, 1.1)
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image = self._adjust_contrast(image, 1.08)
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image = self._adjust_saturation(image, 1.1)
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image = self._apply_sharpening(image)
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return image
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except Exception as e:
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logger.error(f"Error applying creative enhancements: {str(e)}")
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return image
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def _adjust_brightness(self, image: Image.Image, factor: float) -> Image.Image:
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"""Adjust image brightness."""
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try:
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enhancer = ImageEnhance.Brightness(image)
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return enhancer.enhance(factor)
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except Exception as e:
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logger.error(f"Error adjusting brightness: {str(e)}")
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return image
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def _adjust_contrast(self, image: Image.Image, factor: float) -> Image.Image:
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"""Adjust image contrast."""
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try:
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enhancer = ImageEnhance.Contrast(image)
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return enhancer.enhance(factor)
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except Exception as e:
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logger.error(f"Error adjusting contrast: {str(e)}")
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return image
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def _adjust_saturation(self, image: Image.Image, factor: float) -> Image.Image:
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"""Adjust image saturation."""
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try:
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enhancer = ImageEnhance.Color(image)
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return enhancer.enhance(factor)
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except Exception as e:
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logger.error(f"Error adjusting saturation: {str(e)}")
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return image
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def _apply_sharpening(self, image: Image.Image) -> Image.Image:
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"""Apply image sharpening."""
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try:
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# Apply unsharp mask for professional sharpening
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return image.filter(ImageFilter.UnsharpMask(radius=1, percent=150, threshold=3))
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except Exception as e:
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logger.error(f"Error applying sharpening: {str(e)}")
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return image
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def _adjust_color_temperature(self, image: Image.Image, temperature: str) -> Image.Image:
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"""Adjust image color temperature."""
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try:
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if temperature == 'warm':
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# Apply warm color adjustment
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enhancer = ImageEnhance.Color(image)
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image = enhancer.enhance(1.1)
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# Slight red tint for warmth
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# This is a simplified approach - more sophisticated color grading could be implemented
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return image
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else:
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return image
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except Exception as e:
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logger.error(f"Error adjusting color temperature: {str(e)}")
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return image
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def _ensure_professional_appearance(
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self,
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image: Image.Image,
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content_context: Optional[Dict[str, Any]] = None
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) -> Image.Image:
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"""
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Ensure image meets professional LinkedIn standards.
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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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Professionally optimized image
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"""
|
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try:
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# Ensure minimum quality standards
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if image.mode in ('RGBA', 'LA', 'P'):
|
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# Convert to RGB for better compatibility
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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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|
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# Ensure minimum resolution for LinkedIn
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min_resolution = (1024, 1024)
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if image.size[0] < min_resolution[0] or image.size[1] < min_resolution[1]:
|
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# Resize to minimum resolution while maintaining aspect ratio
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ratio = max(min_resolution[0] / image.size[0], min_resolution[1] / image.size[1])
|
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new_size = (int(image.size[0] * ratio), int(image.size[1] * ratio))
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image = image.resize(new_size, Image.Resampling.LANCZOS)
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logger.info(f"Resized image to {new_size} for LinkedIn professional standards")
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return image
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|
|
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except Exception as e:
|
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logger.error(f"Error ensuring professional appearance: {str(e)}")
|
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return image
|
|
|
|
async def get_editing_suggestions(
|
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self,
|
|
image_data: bytes,
|
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content_context: Dict[str, Any]
|
|
) -> List[Dict[str, Any]]:
|
|
"""
|
|
Get AI-powered editing suggestions for LinkedIn image.
|
|
|
|
Args:
|
|
image_data: Image data in bytes
|
|
content_context: LinkedIn content context
|
|
|
|
Returns:
|
|
List of editing suggestions
|
|
"""
|
|
try:
|
|
# Analyze image and provide contextual suggestions
|
|
suggestions = []
|
|
|
|
# Professional enhancement suggestions
|
|
suggestions.append({
|
|
'id': 'professional_enhancement',
|
|
'title': 'Professional Enhancement',
|
|
'description': 'Apply subtle professional enhancements for business appeal',
|
|
'prompt': 'Enhance this image with professional business aesthetics',
|
|
'priority': 'high'
|
|
})
|
|
|
|
# Mobile optimization suggestions
|
|
suggestions.append({
|
|
'id': 'mobile_optimization',
|
|
'title': 'Mobile Optimization',
|
|
'description': 'Optimize for LinkedIn mobile feed viewing',
|
|
'prompt': 'Optimize this image for mobile LinkedIn viewing',
|
|
'priority': 'medium'
|
|
})
|
|
|
|
# Industry-specific suggestions
|
|
industry = content_context.get('industry', 'business')
|
|
suggestions.append({
|
|
'id': 'industry_optimization',
|
|
'title': f'{industry.title()} Industry Optimization',
|
|
'description': f'Apply {industry} industry-specific visual enhancements',
|
|
'prompt': f'Enhance this image with {industry} industry aesthetics',
|
|
'priority': 'medium'
|
|
})
|
|
|
|
# Engagement optimization suggestions
|
|
suggestions.append({
|
|
'id': 'engagement_optimization',
|
|
'title': 'Engagement Optimization',
|
|
'description': 'Make this image more engaging for LinkedIn audience',
|
|
'prompt': 'Make this image more engaging and shareable for LinkedIn',
|
|
'priority': 'low'
|
|
})
|
|
|
|
return suggestions
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error getting editing suggestions: {str(e)}")
|
|
return []
|