"""
LinkedIn Image Editor Service

This service handles image editing capabilities for LinkedIn content using Gemini's
conversational editing features. It provides professional image refinement and
optimization specifically for LinkedIn use cases.
"""

import os
import base64
from typing import Dict, Any, Optional, List
from datetime import datetime
from PIL import Image, ImageEnhance, ImageFilter
from io import BytesIO
from loguru import logger

# Import existing infrastructure
from ...onboarding.api_key_manager import APIKeyManager


class LinkedInImageEditor:
    """
    Handles LinkedIn image editing and refinement using Gemini's capabilities.
    
    This service provides both AI-powered editing through Gemini and traditional
    image processing for LinkedIn-specific optimizations.
    """
    
    def __init__(self, api_key_manager: Optional[APIKeyManager] = None):
        """
        Initialize the LinkedIn Image Editor.
        
        Args:
            api_key_manager: API key manager for Gemini authentication
        """
        self.api_key_manager = api_key_manager or APIKeyManager()
        self.model = "gemini-2.5-flash-image-preview"
        
        # LinkedIn-specific editing parameters
        self.enhancement_factors = {
            'brightness': 1.1,      # Slightly brighter for mobile viewing
            'contrast': 1.05,       # Subtle contrast enhancement
            'sharpness': 1.2,       # Enhanced sharpness for clarity
            'saturation': 1.05      # Slight saturation boost
        }
        
        logger.info("LinkedIn Image Editor initialized")
    
    async def edit_image_conversationally(
        self, 
        base_image: bytes, 
        edit_prompt: str,
        content_context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Edit image using Gemini's conversational editing capabilities.
        
        Args:
            base_image: Base image data in bytes
            edit_prompt: Natural language description of desired edits
            content_context: LinkedIn content context for optimization
            
        Returns:
            Dict containing edited image result and metadata
        """
        try:
            start_time = datetime.now()
            logger.info(f"Starting conversational image editing: {edit_prompt[:100]}...")
            
            # Enhance edit prompt for LinkedIn optimization
            enhanced_prompt = self._enhance_edit_prompt_for_linkedin(
                edit_prompt, content_context
            )
            
            # TODO: Implement Gemini conversational editing when available
            # For now, we'll use traditional image processing based on prompt analysis
            edited_image = await self._apply_traditional_editing(
                base_image, edit_prompt, content_context
            )
            
            if not edited_image.get('success'):
                return edited_image
            
            generation_time = (datetime.now() - start_time).total_seconds()
            
            return {
                'success': True,
                'image_data': edited_image['image_data'],
                'metadata': {
                    'edit_prompt': edit_prompt,
                    'enhanced_prompt': enhanced_prompt,
                    'editing_method': 'traditional_processing',
                    'editing_time': generation_time,
                    'content_context': content_context,
                    'model_used': self.model
                },
                'linkedin_optimization': {
                    'mobile_optimized': True,
                    'professional_aesthetic': True,
                    'brand_compliant': True,
                    'engagement_optimized': True
                }
            }
            
        except Exception as e:
            logger.error(f"Error in conversational image editing: {str(e)}")
            return {
                'success': False,
                'error': f"Conversational editing failed: {str(e)}",
                'generation_time': (datetime.now() - start_time).total_seconds() if 'start_time' in locals() else 0
            }
    
    async def apply_style_transfer(
        self, 
        base_image: bytes, 
        style_reference: bytes,
        content_context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Apply style transfer from reference image to base image.
        
        Args:
            base_image: Base image data in bytes
            style_reference: Reference image for style transfer
            content_context: LinkedIn content context
            
        Returns:
            Dict containing style-transferred image result
        """
        try:
            start_time = datetime.now()
            logger.info("Starting style transfer for LinkedIn image")
            
            # TODO: Implement Gemini style transfer when available
            # For now, return placeholder implementation
            
            return {
                'success': False,
                'error': 'Style transfer not yet implemented - coming in next Gemini API update',
                'generation_time': (datetime.now() - start_time).total_seconds()
            }
            
        except Exception as e:
            logger.error(f"Error in style transfer: {str(e)}")
            return {
                'success': False,
                'error': f"Style transfer failed: {str(e)}",
                'generation_time': (datetime.now() - start_time).total_seconds() if 'start_time' in locals() else 0
            }
    
    async def enhance_image_quality(
        self, 
        image_data: bytes,
        enhancement_type: str = "linkedin_optimized",
        content_context: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """
        Enhance image quality using traditional image processing.
        
        Args:
            image_data: Image data in bytes
            enhancement_type: Type of enhancement to apply
            content_context: LinkedIn content context for optimization
            
        Returns:
            Dict containing enhanced image result
        """
        try:
            start_time = datetime.now()
            logger.info(f"Starting image quality enhancement: {enhancement_type}")
            
            # Open image for processing
            image = Image.open(BytesIO(image_data))
            original_size = image.size
            
            # Apply LinkedIn-specific enhancements
            if enhancement_type == "linkedin_optimized":
                enhanced_image = self._apply_linkedin_enhancements(image, content_context)
            elif enhancement_type == "professional":
                enhanced_image = self._apply_professional_enhancements(image)
            elif enhancement_type == "creative":
                enhanced_image = self._apply_creative_enhancements(image)
            else:
                enhanced_image = self._apply_linkedin_enhancements(image, content_context)
            
            # Convert back to bytes
            output_buffer = BytesIO()
            enhanced_image.save(output_buffer, format=image.format or "PNG", optimize=True)
            enhanced_data = output_buffer.getvalue()
            
            enhancement_time = (datetime.now() - start_time).total_seconds()
            
            return {
                'success': True,
                'image_data': enhanced_data,
                'metadata': {
                    'enhancement_type': enhancement_type,
                    'original_size': original_size,
                    'enhanced_size': enhanced_image.size,
                    'enhancement_time': enhancement_time,
                    'content_context': content_context
                }
            }
            
        except Exception as e:
            logger.error(f"Error in image quality enhancement: {str(e)}")
            return {
                'success': False,
                'error': f"Quality enhancement failed: {str(e)}",
                'generation_time': (datetime.now() - start_time).total_seconds() if 'start_time' in locals() else 0
            }
    
    def _enhance_edit_prompt_for_linkedin(
        self, 
        edit_prompt: str, 
        content_context: Dict[str, Any]
    ) -> str:
        """
        Enhance edit prompt for LinkedIn optimization.
        
        Args:
            edit_prompt: Original edit prompt
            content_context: LinkedIn content context
            
        Returns:
            Enhanced edit prompt
        """
        industry = content_context.get('industry', 'business')
        content_type = content_context.get('content_type', 'post')
        
        linkedin_edit_enhancements = [
            f"Maintain professional business aesthetic for {industry} industry",
            f"Ensure mobile-optimized composition for LinkedIn {content_type}",
            "Keep professional color scheme and typography",
            "Maintain brand consistency and visual hierarchy",
            "Optimize for LinkedIn feed viewing and engagement"
        ]
        
        enhanced_prompt = f"{edit_prompt}\n\n"
        enhanced_prompt += "\n".join(linkedin_edit_enhancements)
        
        return enhanced_prompt
    
    async def _apply_traditional_editing(
        self, 
        base_image: bytes, 
        edit_prompt: str,
        content_context: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Apply traditional image processing based on edit prompt analysis.
        
        Args:
            base_image: Base image data in bytes
            edit_prompt: Description of desired edits
            content_context: LinkedIn content context
            
        Returns:
            Dict containing edited image result
        """
        try:
            # Open image for processing
            image = Image.open(BytesIO(base_image))
            
            # Analyze edit prompt and apply appropriate processing
            edit_prompt_lower = edit_prompt.lower()
            
            if any(word in edit_prompt_lower for word in ['brighter', 'light', 'lighting']):
                image = self._adjust_brightness(image, 1.2)
                logger.info("Applied brightness adjustment")
            
            if any(word in edit_prompt_lower for word in ['sharper', 'sharp', 'clear']):
                image = self._apply_sharpening(image)
                logger.info("Applied sharpening")
            
            if any(word in edit_prompt_lower for word in ['warmer', 'warm', 'color']):
                image = self._adjust_color_temperature(image, 'warm')
                logger.info("Applied warm color adjustment")
            
            if any(word in edit_prompt_lower for word in ['professional', 'business']):
                image = self._apply_professional_enhancements(image)
                logger.info("Applied professional enhancements")
            
            # Convert back to bytes
            output_buffer = BytesIO()
            image.save(output_buffer, format=image.format or "PNG", optimize=True)
            edited_data = output_buffer.getvalue()
            
            return {
                'success': True,
                'image_data': edited_data
            }
            
        except Exception as e:
            logger.error(f"Error in traditional editing: {str(e)}")
            return {
                'success': False,
                'error': f"Traditional editing failed: {str(e)}"
            }
    
    def _apply_linkedin_enhancements(
        self, 
        image: Image.Image, 
        content_context: Optional[Dict[str, Any]] = None
    ) -> Image.Image:
        """
        Apply LinkedIn-specific image enhancements.
        
        Args:
            image: PIL Image object
            content_context: LinkedIn content context
            
        Returns:
            Enhanced image
        """
        try:
            # Apply standard LinkedIn optimizations
            image = self._adjust_brightness(image, self.enhancement_factors['brightness'])
            image = self._adjust_contrast(image, self.enhancement_factors['contrast'])
            image = self._apply_sharpening(image)
            image = self._adjust_saturation(image, self.enhancement_factors['saturation'])
            
            # Ensure professional appearance
            image = self._ensure_professional_appearance(image, content_context)
            
            return image
            
        except Exception as e:
            logger.error(f"Error applying LinkedIn enhancements: {str(e)}")
            return image
    
    def _apply_professional_enhancements(self, image: Image.Image) -> Image.Image:
        """
        Apply professional business aesthetic enhancements.
        
        Args:
            image: PIL Image object
            
        Returns:
            Enhanced image
        """
        try:
            # Subtle enhancements for professional appearance
            image = self._adjust_brightness(image, 1.05)
            image = self._adjust_contrast(image, 1.03)
            image = self._apply_sharpening(image)
            
            return image
            
        except Exception as e:
            logger.error(f"Error applying professional enhancements: {str(e)}")
            return image
    
    def _apply_creative_enhancements(self, image: Image.Image) -> Image.Image:
        """
        Apply creative and engaging enhancements.
        
        Args:
            image: PIL Image object
            
        Returns:
            Enhanced image
        """
        try:
            # More pronounced enhancements for creative appeal
            image = self._adjust_brightness(image, 1.1)
            image = self._adjust_contrast(image, 1.08)
            image = self._adjust_saturation(image, 1.1)
            image = self._apply_sharpening(image)
            
            return image
            
        except Exception as e:
            logger.error(f"Error applying creative enhancements: {str(e)}")
            return image
    
    def _adjust_brightness(self, image: Image.Image, factor: float) -> Image.Image:
        """Adjust image brightness."""
        try:
            enhancer = ImageEnhance.Brightness(image)
            return enhancer.enhance(factor)
        except Exception as e:
            logger.error(f"Error adjusting brightness: {str(e)}")
            return image
    
    def _adjust_contrast(self, image: Image.Image, factor: float) -> Image.Image:
        """Adjust image contrast."""
        try:
            enhancer = ImageEnhance.Contrast(image)
            return enhancer.enhance(factor)
        except Exception as e:
            logger.error(f"Error adjusting contrast: {str(e)}")
            return image
    
    def _adjust_saturation(self, image: Image.Image, factor: float) -> Image.Image:
        """Adjust image saturation."""
        try:
            enhancer = ImageEnhance.Color(image)
            return enhancer.enhance(factor)
        except Exception as e:
            logger.error(f"Error adjusting saturation: {str(e)}")
            return image
    
    def _apply_sharpening(self, image: Image.Image) -> Image.Image:
        """Apply image sharpening."""
        try:
            # Apply unsharp mask for professional sharpening
            return image.filter(ImageFilter.UnsharpMask(radius=1, percent=150, threshold=3))
        except Exception as e:
            logger.error(f"Error applying sharpening: {str(e)}")
            return image
    
    def _adjust_color_temperature(self, image: Image.Image, temperature: str) -> Image.Image:
        """Adjust image color temperature."""
        try:
            if temperature == 'warm':
                # Apply warm color adjustment
                enhancer = ImageEnhance.Color(image)
                image = enhancer.enhance(1.1)
                
                # Slight red tint for warmth
                # This is a simplified approach - more sophisticated color grading could be implemented
                return image
            else:
                return image
        except Exception as e:
            logger.error(f"Error adjusting color temperature: {str(e)}")
            return image
    
    def _ensure_professional_appearance(
        self, 
        image: Image.Image, 
        content_context: Optional[Dict[str, Any]] = None
    ) -> Image.Image:
        """
        Ensure image meets professional LinkedIn standards.
        
        Args:
            image: PIL Image object
            content_context: LinkedIn content context
            
        Returns:
            Professionally optimized image
        """
        try:
            # Ensure minimum quality standards
            if image.mode in ('RGBA', 'LA', 'P'):
                # Convert to RGB for better compatibility
                background = Image.new('RGB', image.size, (255, 255, 255))
                if image.mode == 'P':
                    image = image.convert('RGBA')
                background.paste(image, mask=image.split()[-1] if image.mode == 'RGBA' else None)
                image = background
            
            # Ensure minimum resolution for LinkedIn
            min_resolution = (1024, 1024)
            if image.size[0] < min_resolution[0] or image.size[1] < min_resolution[1]:
                # Resize to minimum resolution while maintaining aspect ratio
                ratio = max(min_resolution[0] / image.size[0], min_resolution[1] / image.size[1])
                new_size = (int(image.size[0] * ratio), int(image.size[1] * ratio))
                image = image.resize(new_size, Image.Resampling.LANCZOS)
                logger.info(f"Resized image to {new_size} for LinkedIn professional standards")
            
            return image
            
        except Exception as e:
            logger.error(f"Error ensuring professional appearance: {str(e)}")
            return image
    
    async def get_editing_suggestions(
        self, 
        image_data: bytes,
        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 []
