"""Image analysis module for dog emotion detection"""

from typing import Dict, Any, Union, Optional
from pathlib import Path
import base64

from .base import BaseModule
from ..utils.validators import validate_image
from ..utils.exceptions import ProcessingError
from ..models import EnsembleDogEmotionModel


class ImageAnalyzer(BaseModule):
    """
    Analyze dog emotions from images

    This module processes dog images and returns:
    - Text description of detected emotions
    - Audio narration of the analysis
    """

    def __init__(self, config, model_dir: Optional[Union[str, Path]] = None):
        """
        Initialize ImageAnalyzer

        Args:
            config: Configuration object
            model_dir: Optional path to directory containing ensemble models
                      If None, uses bundled models
        """
        super().__init__(config)
        self.emotion_model = EnsembleDogEmotionModel(model_dir)
        self.logger.info(f"ImageAnalyzer initialized with ensemble emotion model ({self.emotion_model.ensemble_size} models)")

    def analyze(
        self,
        image_path: Union[str, Path, bytes],
        **kwargs
    ) -> Dict[str, Any]:
        """
        Analyze dog emotion from image

        Args:
            image_path: Path to image file, URL, or image bytes
            **kwargs: Additional parameters
                - language: Output language (default: 'en')
                - detail_level: 'simple' or 'detailed' (default: 'simple')
                - return_confidence: Whether to return confidence scores (default: False)

        Returns:
            Dict with keys:
                - text: Description of detected emotion
                - audio: Audio narration as bytes
                - emotion: Detected emotion label (optional)
                - confidence: Confidence score (optional)

        Example:
            >>> analyzer = ImageAnalyzer(config)
            >>> result = analyzer.analyze("dog.jpg")
            >>> print(result['text'])
            'The dog appears happy and excited'
        """
        self.logger.info(f"Analyzing image: {image_path}")

        # Validate input
        validate_image(image_path)

        # Process image
        return self.process(image_path, **kwargs)

    def process(
        self,
        input_data: Union[str, Path, bytes],
        **kwargs
    ) -> Dict[str, Any]:
        """
        Process image and detect emotions

        Args:
            input_data: Image data
            **kwargs: Additional parameters

        Returns:
            Analysis results
        """
        try:
            # Load image
            if isinstance(input_data, bytes):
                image_bytes = input_data
            else:
                image_bytes = self._load_file(str(input_data))

            # Extract parameters
            language = kwargs.get('language', 'en')
            detail_level = kwargs.get('detail_level', 'simple')
            return_confidence = kwargs.get('return_confidence', False)

            # TODO: Implement actual emotion detection model
            # This is a placeholder that should be replaced with actual ML model
            emotion_result = self._detect_emotion(image_bytes)

            # Generate text description
            text = self._generate_description(
                emotion_result,
                language=language,
                detail_level=detail_level
            )

            # Generate audio narration
            audio = self._generate_audio(text)

            # Build result
            result = {
                'text': text,
                'audio': audio,
            }

            if return_confidence:
                result['emotion'] = emotion_result['emotion']
                result['confidence'] = emotion_result['confidence']
                result['all_predictions'] = emotion_result['all_predictions']
                # Pass through ensemble-specific info
                if 'ensemble_size' in emotion_result:
                    result['ensemble_size'] = emotion_result['ensemble_size']
                if 'voting_strategy' in emotion_result:
                    result['voting_strategy'] = emotion_result['voting_strategy']
                if 'model_weights' in emotion_result:
                    result['model_weights'] = emotion_result['model_weights']
                if 'model_predictions' in emotion_result:
                    result['model_predictions'] = emotion_result['model_predictions']

            self.logger.info(f"Analysis complete: {emotion_result['emotion']}")
            return result

        except Exception as e:
            self.logger.error(f"Error processing image: {str(e)}")
            raise ProcessingError(f"Failed to process image: {str(e)}")

    def _detect_emotion(self, image_bytes: bytes) -> Dict[str, Any]:
        """
        Detect emotion from image using ML model

        Args:
            image_bytes: Image data

        Returns:
            Dict with emotion and confidence
        """
        try:
            # Use the trained model to predict emotion
            result = self.emotion_model.predict(image_bytes)
            return result
        except Exception as e:
            self.logger.error(f"Error in emotion detection: {str(e)}")
            raise ProcessingError(f"Emotion detection failed: {str(e)}")

    def _generate_description(
        self,
        emotion_result: Dict[str, Any],
        language: str = 'en',
        detail_level: str = 'simple'
    ) -> str:
        """
        Generate text description of emotion

        Args:
            emotion_result: Emotion detection results
            language: Output language
            detail_level: Level of detail

        Returns:
            Text description
        """
        emotion = emotion_result['emotion']
        confidence = emotion_result['confidence']

        # Simple templates (can be expanded with proper NLG)
        templates = {
            'en': {
                'simple': f"The dog appears {emotion}.",
                'detailed': f"The dog appears {emotion} with {confidence*100:.1f}% confidence. "
                           f"This is indicated by their body language and facial expressions."
            },
            'zh': {
                'simple': f"这只狗看起来{emotion}。",
                'detailed': f"这只狗看起来{emotion}，置信度为{confidence*100:.1f}%。"
            }
        }

        lang_templates = templates.get(language, templates['en'])
        return lang_templates.get(detail_level, lang_templates['simple'])
