"""Video analysis module for dog emotion detection"""

from typing import Dict, Any, Union, List
from pathlib import Path

from .base import BaseModule
from ..utils.validators import validate_video
from ..utils.exceptions import ProcessingError


class VideoAnalyzer(BaseModule):
    """
    Analyze dog emotions from videos

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

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

        Args:
            video_path: Path to video file, URL, or video bytes
            **kwargs: Additional parameters
                - language: Output language (default: 'en')
                - sample_rate: Frames per second to sample (default: 1)
                - aggregate_method: 'average', 'dominant', 'timeline' (default: 'dominant')
                - return_timeline: Return emotion timeline (default: False)

        Returns:
            Dict with keys:
                - text: Description of detected emotions
                - audio: Audio narration as bytes
                - dominant_emotion: Most common emotion (optional)
                - timeline: List of emotions over time (optional)

        Example:
            >>> analyzer = VideoAnalyzer(config)
            >>> result = analyzer.analyze("dog_video.mp4")
            >>> print(result['text'])
            'Throughout the video, the dog appears mostly happy and playful'
        """
        self.logger.info(f"Analyzing video: {video_path}")

        # Validate input
        validate_video(video_path)

        # Process video
        return self.process(video_path, **kwargs)

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

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

        Returns:
            Analysis results
        """
        try:
            # Load video
            if isinstance(input_data, bytes):
                video_bytes = input_data
                video_path = self._save_temp_video(video_bytes)
            else:
                video_path = str(input_data)

            # Extract parameters
            language = kwargs.get('language', 'en')
            sample_rate = kwargs.get('sample_rate', 1)
            aggregate_method = kwargs.get('aggregate_method', 'dominant')
            return_timeline = kwargs.get('return_timeline', False)

            # TODO: Implement actual video emotion detection
            # Extract frames and analyze
            frames = self._extract_frames(video_path, sample_rate)
            emotion_timeline = self._analyze_frames(frames)

            # Aggregate emotions
            aggregated = self._aggregate_emotions(emotion_timeline, aggregate_method)

            # Generate text description
            text = self._generate_video_description(
                aggregated,
                emotion_timeline if return_timeline else None,
                language=language
            )

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

            # Build result
            result = {
                'text': text,
                'audio': audio,
                'dominant_emotion': aggregated['dominant_emotion'],
            }

            if return_timeline:
                result['timeline'] = emotion_timeline

            self.logger.info(f"Video analysis complete: {aggregated['dominant_emotion']}")
            return result

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

    def _save_temp_video(self, video_bytes: bytes) -> str:
        """Save video bytes to temporary file"""
        import tempfile
        with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as f:
            f.write(video_bytes)
            return f.name

    def _extract_frames(self, video_path: str, sample_rate: float) -> List[bytes]:
        """
        Extract frames from video

        Args:
            video_path: Path to video
            sample_rate: Frames per second to extract

        Returns:
            List of frame images as bytes
        """
        # TODO: Implement actual frame extraction using cv2 or similar
        # Placeholder implementation
        self.logger.info(f"Extracting frames at {sample_rate} fps")

        # In real implementation, would use:
        # import cv2
        # cap = cv2.VideoCapture(video_path)
        # Extract frames at specified rate
        # Convert to bytes

        # Placeholder: return dummy frames
        return [b"FRAME_1", b"FRAME_2", b"FRAME_3"]

    def _analyze_frames(self, frames: List[bytes]) -> List[Dict[str, Any]]:
        """
        Analyze emotions in each frame

        Args:
            frames: List of frame images

        Returns:
            List of emotion results for each frame
        """
        timeline = []
        for i, frame in enumerate(frames):
            # TODO: Use actual emotion detection model
            # Placeholder logic
            emotions_cycle = ['happy', 'playful', 'happy', 'relaxed']
            emotion = emotions_cycle[i % len(emotions_cycle)]

            timeline.append({
                'frame': i,
                'timestamp': i * 1.0,  # assuming 1 fps
                'emotion': emotion,
                'confidence': 0.85
            })

        return timeline

    def _aggregate_emotions(
        self,
        timeline: List[Dict[str, Any]],
        method: str
    ) -> Dict[str, Any]:
        """
        Aggregate emotions across video

        Args:
            timeline: List of frame-by-frame emotions
            method: Aggregation method

        Returns:
            Aggregated emotion data
        """
        if not timeline:
            return {'dominant_emotion': 'unknown', 'distribution': {}}

        # Count emotions
        emotion_counts = {}
        for item in timeline:
            emotion = item['emotion']
            emotion_counts[emotion] = emotion_counts.get(emotion, 0) + 1

        # Find dominant emotion
        dominant = max(emotion_counts.items(), key=lambda x: x[1])[0]

        # Calculate distribution
        total = len(timeline)
        distribution = {
            emotion: count / total
            for emotion, count in emotion_counts.items()
        }

        return {
            'dominant_emotion': dominant,
            'distribution': distribution,
            'total_frames': total
        }

    def _generate_video_description(
        self,
        aggregated: Dict[str, Any],
        timeline: List[Dict[str, Any]] = None,
        language: str = 'en'
    ) -> str:
        """
        Generate text description of video analysis

        Args:
            aggregated: Aggregated emotion data
            timeline: Frame-by-frame timeline (optional)
            language: Output language

        Returns:
            Text description
        """
        dominant = aggregated['dominant_emotion']
        distribution = aggregated['distribution']

        # Generate description
        if language == 'zh':
            text = f"在整个视频中，狗主要表现出{dominant}的情绪。"
        else:
            text = f"Throughout the video, the dog appears mostly {dominant}. "

            # Add distribution info
            other_emotions = [
                f"{emotion} ({dist*100:.0f}%)"
                for emotion, dist in distribution.items()
                if emotion != dominant and dist > 0.1
            ]
            if other_emotions:
                text += f"Other emotions detected: {', '.join(other_emotions)}."

        return text
