"""Legacy compatibility functions for STT functionality."""

import logging
from pathlib import Path
from typing import Union

from .provider_factory import STTProviderFactory
from ...domain.models.audio_content import AudioContent
from ...domain.exceptions import SpeechRecognitionException

logger = logging.getLogger(__name__)


def transcribe_audio(audio_path: Union[str, Path], model_name: str = "parakeet") -> str:
    """
    Convert audio file to text using specified STT model (legacy interface).

    This function maintains backward compatibility with the original utils/stt.py interface.

    Args:
        audio_path: Path to input audio file
        model_name: Name of the STT model/provider to use (whisper or parakeet)

    Returns:
        str: Transcribed English text

    Raises:
        SpeechRecognitionException: If transcription fails
    """
    logger.info(f"Starting transcription for: {audio_path} using {model_name} model")

    try:
        # Convert path to Path object
        audio_path = Path(audio_path)

        if not audio_path.exists():
            raise SpeechRecognitionException(f"Audio file not found: {audio_path}")

        # Read audio file and create AudioContent
        with open(audio_path, 'rb') as f:
            audio_data = f.read()

        # Determine audio format from file extension
        audio_format = audio_path.suffix.lower().lstrip('.')
        if audio_format not in ['wav', 'mp3', 'flac', 'ogg']:
            audio_format = 'wav'  # Default fallback

        # Create AudioContent (we'll use reasonable placeholder values)
        # The provider will handle the actual audio analysis during preprocessing
        try:
            audio_content = AudioContent(
                data=audio_data,
                format=audio_format,
                sample_rate=16000,  # Standard rate for STT
                duration=max(1.0, len(audio_data) / (16000 * 2)),  # Rough estimate
                filename=audio_path.name
            )
        except ValueError:
            # If validation fails, try with minimal valid values
            audio_content = AudioContent(
                data=audio_data,
                format=audio_format,
                sample_rate=16000,
                duration=1.0,  # Minimum valid duration
                filename=audio_path.name
            )

        # Get the appropriate provider
        try:
            provider = STTProviderFactory.create_provider(model_name)
        except SpeechRecognitionException:
            # Fallback to any available provider
            logger.warning(f"Requested provider {model_name} not available, using fallback")
            provider = STTProviderFactory.create_provider_with_fallback(model_name)

        # Get the default model for the provider
        model = provider.get_default_model()

        # Transcribe audio
        text_content = provider.transcribe(audio_content, model)
        result = text_content.text

        logger.info(f"Transcription completed: {result}")
        return result

    except Exception as e:
        logger.error(f"Transcription failed: {str(e)}", exc_info=True)
        raise SpeechRecognitionException(f"Transcription failed: {str(e)}") from e


def create_audio_content_from_file(audio_path: Union[str, Path]) -> AudioContent:
    """
    Create AudioContent from an audio file with proper metadata detection.

    Args:
        audio_path: Path to the audio file

    Returns:
        AudioContent: The audio content object

    Raises:
        SpeechRecognitionException: If file cannot be processed
    """
    try:
        from pydub import AudioSegment

        audio_path = Path(audio_path)

        # Load audio file to get metadata
        audio_segment = AudioSegment.from_file(audio_path)

        # Read raw audio data
        with open(audio_path, 'rb') as f:
            audio_data = f.read()

        # Determine format
        audio_format = audio_path.suffix.lower().lstrip('.')
        if audio_format not in ['wav', 'mp3', 'flac', 'ogg']:
            audio_format = 'wav'

        # Create AudioContent with actual metadata
        return AudioContent(
            data=audio_data,
            format=audio_format,
            sample_rate=audio_segment.frame_rate,
            duration=len(audio_segment) / 1000.0,  # Convert ms to seconds
            filename=audio_path.name
        )

    except ImportError:
        # Fallback without pydub
        logger.warning("pydub not available, using placeholder metadata")

        with open(audio_path, 'rb') as f:
            audio_data = f.read()

        audio_format = Path(audio_path).suffix.lower().lstrip('.')
        if audio_format not in ['wav', 'mp3', 'flac', 'ogg']:
            audio_format = 'wav'

        return AudioContent(
            data=audio_data,
            format=audio_format,
            sample_rate=16000,  # Default
            duration=1.0,  # Placeholder
            filename=Path(audio_path).name
        )

    except Exception as e:
        raise SpeechRecognitionException(f"Failed to create AudioContent from file: {str(e)}") from e