src/application/services/audio_processing_service.py

"""Audio Processing Application Service for pipeline orchestration."""

import logging
import os
import tempfile
import time
import uuid
from pathlib import Path
from typing import Optional, Dict, Any
from contextlib import contextmanager

from ..dtos.audio_upload_dto import AudioUploadDto
from ..dtos.processing_request_dto import ProcessingRequestDto
from ..dtos.processing_result_dto import ProcessingResultDto
from ..error_handling.error_mapper import ErrorMapper
from ..error_handling.structured_logger import StructuredLogger, LogContext, get_structured_logger
from ..error_handling.recovery_manager import RecoveryManager, RetryConfig, CircuitBreakerConfig
from ...domain.interfaces.speech_recognition import ISpeechRecognitionService
from ...domain.interfaces.translation import ITranslationService
from ...domain.interfaces.speech_synthesis import ISpeechSynthesisService
from ...domain.models.audio_content import AudioContent
from ...domain.models.text_content import TextContent
from ...domain.models.translation_request import TranslationRequest
from ...domain.models.speech_synthesis_request import SpeechSynthesisRequest
from ...domain.models.voice_settings import VoiceSettings
from ...domain.exceptions import (
    DomainException,
    AudioProcessingException,
    SpeechRecognitionException,
    TranslationFailedException,
    SpeechSynthesisException
)
from ...infrastructure.config.app_config import AppConfig
from ...infrastructure.config.dependency_container import DependencyContainer

logger = get_structured_logger(__name__)


class AudioProcessingApplicationService:
    """Application service for orchestrating the complete audio processing pipeline."""

    def __init__(
        self,
        container: DependencyContainer,
        config: Optional[AppConfig] = None
    ):
        """
        Initialize the audio processing application service.

        Args:
            container: Dependency injection container
            config: Application configuration (optional, will be resolved from container)
        """
        self._container = container
        self._config = config or container.resolve(AppConfig)
        self._temp_files: Dict[str, str] = {}  # Track temporary files for cleanup

        # Initialize error handling components
        self._error_mapper = ErrorMapper()
        self._recovery_manager = RecoveryManager()

        # Setup logging
        self._setup_logging()

        logger.info("AudioProcessingApplicationService initialized")

    def _setup_logging(self) -> None:
        """Setup logging configuration."""
        try:
            log_config = self._config.get_logging_config()

            # Configure logger level
            logger.setLevel(getattr(logging, log_config['level'].upper(), logging.INFO))

            # Add file handler if enabled
            if log_config.get('enable_file_logging', False):
                file_handler = logging.FileHandler(log_config['log_file_path'])
                file_handler.setLevel(logger.level)

                formatter = logging.Formatter(log_config['format'])
                file_handler.setFormatter(formatter)

                logger.addHandler(file_handler)

        except Exception as e:
            logger.warning(f"Failed to setup logging configuration: {e}")

    def process_audio_pipeline(self, request: ProcessingRequestDto) -> ProcessingResultDto:
        """
        Process audio through the complete pipeline: STT -> Translation -> TTS.

        Args:
            request: Processing request containing audio and parameters

        Returns:
            ProcessingResultDto: Result of the complete processing pipeline
        """
        # Generate correlation ID and start operation logging
        correlation_id = logger.log_operation_start(
            "audio_processing_pipeline",
            extra={
                'asr_model': request.asr_model,
                'target_language': request.target_language,
                'voice': request.voice,
                'file_name': request.audio.filename,
                'file_size': request.audio.size
            }
        )

        start_time = time.time()
        context = LogContext(
            correlation_id=correlation_id,
            operation="audio_processing_pipeline",
            component="AudioProcessingApplicationService"
        )

        try:
            # Validate request
            self._validate_request(request)

            # Create temporary working directory
            with self._create_temp_directory(correlation_id) as temp_dir:
                # Step 1: Convert uploaded audio to domain model
                audio_content = self._convert_upload_to_audio_content(request.audio, temp_dir)

                # Step 2: Speech-to-Text with retry and fallback
                original_text = self._perform_speech_recognition_with_recovery(
                    audio_content,
                    request.asr_model,
                    correlation_id
                )

                # Step 3: Translation (if needed) with retry
                translated_text = original_text
                if request.requires_translation:
                    translated_text = self._perform_translation_with_recovery(
                        original_text,
                        request.source_language,
                        request.target_language,
                        correlation_id
                    )

                # Step 4: Text-to-Speech with fallback providers
                output_audio_path = self._perform_speech_synthesis_with_recovery(
                    translated_text,
                    request.voice,
                    request.speed,
                    request.target_language,
                    temp_dir,
                    correlation_id
                )

                # Calculate processing time
                processing_time = time.time() - start_time

                # Create successful result
                result = ProcessingResultDto.success_result(
                    original_text=original_text.text,
                    translated_text=translated_text.text if translated_text != original_text else None,
                    audio_path=output_audio_path,
                    processing_time=processing_time,
                    metadata={
                        'correlation_id': correlation_id,
                        'asr_model': request.asr_model,
                        'target_language': request.target_language,
                        'voice': request.voice,
                        'speed': request.speed,
                        'translation_required': request.requires_translation
                    }
                )

                # Log successful completion
                logger.log_operation_end(
                    "audio_processing_pipeline",
                    correlation_id,
                    success=True,
                    duration=processing_time,
                    context=context,
                    extra={
                        'original_text_length': len(original_text.text),
                        'translated_text_length': len(translated_text.text) if translated_text != original_text else 0,
                        'output_file': output_audio_path
                    }
                )

                return result

        except DomainException as e:
            processing_time = time.time() - start_time

            # Map exception to user-friendly error
            error_context = {
                'file_name': request.audio.filename,
                'file_size': request.audio.size,
                'operation': 'audio_processing_pipeline',
                'correlation_id': correlation_id
            }

            error_mapping = self._error_mapper.map_exception(e, error_context)

            logger.error(
                f"Domain error in audio processing pipeline: {error_mapping.user_message}",
                context=context,
                exception=e,
                extra={
                    'error_code': error_mapping.error_code,
                    'error_category': error_mapping.category.value,
                    'error_severity': error_mapping.severity.value,
                    'recovery_suggestions': error_mapping.recovery_suggestions
                }
            )

            # Log operation failure
            logger.log_operation_end(
                "audio_processing_pipeline",
                correlation_id,
                success=False,
                duration=processing_time,
                context=context
            )

            return ProcessingResultDto.error_result(
                error_message=error_mapping.user_message,
                error_code=error_mapping.error_code,
                processing_time=processing_time,
                metadata={
                    'correlation_id': correlation_id,
                    'error_category': error_mapping.category.value,
                    'error_severity': error_mapping.severity.value,
                    'recovery_suggestions': error_mapping.recovery_suggestions,
                    'technical_details': error_mapping.technical_details
                }
            )

        except Exception as e:
            processing_time = time.time() - start_time

            # Map unexpected exception
            error_context = {
                'file_name': request.audio.filename,
                'operation': 'audio_processing_pipeline',
                'correlation_id': correlation_id
            }

            error_mapping = self._error_mapper.map_exception(e, error_context)

            logger.critical(
                f"Unexpected error in audio processing pipeline: {error_mapping.user_message}",
                context=context,
                exception=e,
                extra={
                    'error_code': error_mapping.error_code,
                    'error_category': error_mapping.category.value,
                    'error_severity': error_mapping.severity.value
                }
            )

            # Log operation failure
            logger.log_operation_end(
                "audio_processing_pipeline",
                correlation_id,
                success=False,
                duration=processing_time,
                context=context
            )

            return ProcessingResultDto.error_result(
                error_message=error_mapping.user_message,
                error_code=error_mapping.error_code,
                processing_time=processing_time,
                metadata={
                    'correlation_id': correlation_id,
                    'error_category': error_mapping.category.value,
                    'error_severity': error_mapping.severity.value,
                    'technical_details': error_mapping.technical_details
                }
            )

        finally:
            # Cleanup temporary files
            self._cleanup_temp_files()

    def _validate_request(self, request: ProcessingRequestDto) -> None:
        """
        Validate processing request.

        Args:
            request: Processing request to validate

        Raises:
            ValueError: If request is invalid
        """
        if not isinstance(request, ProcessingRequestDto):
            raise ValueError("Request must be a ProcessingRequestDto instance")

        # Additional validation beyond DTO validation
        processing_config = self._config.get_processing_config()

        # Check file size limits
        max_size_bytes = processing_config['max_file_size_mb'] * 1024 * 1024
        if request.audio.size > max_size_bytes:
            raise ValueError(
                f"Audio file too large: {request.audio.size} bytes. "
                f"Maximum allowed: {max_size_bytes} bytes"
            )

        # Check supported audio formats
        supported_formats = processing_config['supported_audio_formats']
        file_ext = request.audio.file_extension.lstrip('.')
        if file_ext not in supported_formats:
            raise ValueError(
                f"Unsupported audio format: {file_ext}. "
                f"Supported formats: {supported_formats}"
            )

    @contextmanager
    def _create_temp_directory(self, correlation_id: str):
        """
        Create temporary directory for processing.

        Args:
            correlation_id: Correlation ID for tracking

        Yields:
            str: Path to temporary directory
        """
        processing_config = self._config.get_processing_config()
        base_temp_dir = processing_config['temp_dir']

        # Create unique temp directory
        temp_dir = os.path.join(base_temp_dir, f"processing_{correlation_id}")

        try:
            os.makedirs(temp_dir, exist_ok=True)
            logger.debug(f"Created temporary directory: {temp_dir}")
            yield temp_dir

        finally:
            # Cleanup temp directory if configured
            if processing_config.get('cleanup_temp_files', True):
                try:
                    import shutil
                    shutil.rmtree(temp_dir, ignore_errors=True)
                    logger.debug(f"Cleaned up temporary directory: {temp_dir}")
                except Exception as e:
                    logger.warning(f"Failed to cleanup temp directory {temp_dir}: {e}")

    def _convert_upload_to_audio_content(
        self,
        upload: AudioUploadDto,
        temp_dir: str
    ) -> AudioContent:
        """
        Convert uploaded audio to domain AudioContent.

        Args:
            upload: Audio upload DTO
            temp_dir: Temporary directory for file operations

        Returns:
            AudioContent: Domain audio content model

        Raises:
            AudioProcessingException: If conversion fails
        """
        try:
            # Save uploaded content to temporary file
            temp_file_path = os.path.join(temp_dir, f"input_{upload.filename}")

            with open(temp_file_path, 'wb') as f:
                f.write(upload.content)

            # Track temp file for cleanup
            self._temp_files[temp_file_path] = temp_file_path

            # Determine audio format from file extension
            audio_format = upload.file_extension.lstrip('.').lower()

            # Create AudioContent (simplified - in real implementation would extract metadata)
            audio_content = AudioContent(
                data=upload.content,
                format=audio_format,
                sample_rate=16000,  # Default, would be extracted from actual file
                duration=0.0  # Would be calculated from actual file
            )

            logger.debug(f"Converted upload to AudioContent: {upload.filename}")
            return audio_content

        except Exception as e:
            logger.error(f"Failed to convert upload to AudioContent: {e}")
            raise AudioProcessingException(f"Failed to process uploaded audio: {str(e)}")

    def _perform_speech_recognition(
        self,
        audio: AudioContent,
        model: str,
        correlation_id: str
    ) -> TextContent:
        """
        Perform speech-to-text recognition.

        Args:
            audio: Audio content to transcribe
            model: STT model to use
            correlation_id: Correlation ID for tracking

        Returns:
            TextContent: Transcribed text

        Raises:
            SpeechRecognitionException: If STT fails
        """
        try:
            logger.debug(f"Starting STT with model: {model} [correlation_id={correlation_id}]")

            # Get STT provider from container
            stt_provider = self._container.get_stt_provider(model)

            # Perform transcription
            text_content = stt_provider.transcribe(audio, model)

            logger.info(
                f"STT completed successfully [correlation_id={correlation_id}, "
                f"text_length={len(text_content.text)}]"
            )

            return text_content

        except Exception as e:
            logger.error(f"STT failed: {e} [correlation_id={correlation_id}]")
            raise SpeechRecognitionException(f"Speech recognition failed: {str(e)}")

    def _perform_translation(
        self,
        text: TextContent,
        source_language: Optional[str],
        target_language: str,
        correlation_id: str
    ) -> TextContent:
        """
        Perform text translation.

        Args:
            text: Text to translate
            source_language: Source language (optional, auto-detect if None)
            target_language: Target language
            correlation_id: Correlation ID for tracking

        Returns:
            TextContent: Translated text

        Raises:
            TranslationFailedException: If translation fails
        """
        try:
            logger.debug(
                f"Starting translation: {source_language or 'auto'} -> {target_language} "
                f"[correlation_id={correlation_id}]"
            )

            # Get translation provider from container
            translation_provider = self._container.get_translation_provider()

            # Create translation request
            translation_request = TranslationRequest(
                text=text.text,
                source_language=source_language or 'auto',
                target_language=target_language
            )

            # Perform translation
            translated_text = translation_provider.translate(translation_request)

            logger.info(
                f"Translation completed successfully [correlation_id={correlation_id}, "
                f"source_length={len(text.text)}, target_length={len(translated_text.text)}]"
            )

            return translated_text

        except Exception as e:
            logger.error(f"Translation failed: {e} [correlation_id={correlation_id}]")
            raise TranslationFailedException(f"Translation failed: {str(e)}")

    def _perform_speech_synthesis(
        self,
        text: TextContent,
        voice: str,
        speed: float,
        language: str,
        temp_dir: str,
        correlation_id: str
    ) -> str:
        """
        Perform text-to-speech synthesis.

        Args:
            text: Text to synthesize
            voice: Voice to use
            speed: Speech speed
            language: Target language
            temp_dir: Temporary directory for output
            correlation_id: Correlation ID for tracking

        Returns:
            str: Path to generated audio file

        Raises:
            SpeechSynthesisException: If TTS fails
        """
        try:
            logger.debug(
                f"Starting TTS with voice: {voice}, speed: {speed} "
                f"[correlation_id={correlation_id}]"
            )

            # Get TTS provider from container
            tts_provider = self._container.get_tts_provider(voice)

            # Create voice settings
            voice_settings = VoiceSettings(
                voice_id=voice,
                speed=speed,
                language=language
            )

            # Create synthesis request
            synthesis_request = SpeechSynthesisRequest(
                text=text.text,
                voice_settings=voice_settings
            )

            # Perform synthesis
            audio_content = tts_provider.synthesize(synthesis_request)

            # Save output to file
            output_filename = f"output_{correlation_id}.{audio_content.format}"
            output_path = os.path.join(temp_dir, output_filename)

            with open(output_path, 'wb') as f:
                f.write(audio_content.data)

            # Track temp file for cleanup
            self._temp_files[output_path] = output_path

            logger.info(
                f"TTS completed successfully [correlation_id={correlation_id}, "
                f"output_file={output_path}]"
            )

            return output_path

        except Exception as e:
            logger.error(f"TTS failed: {e} [correlation_id={correlation_id}]")
            raise SpeechSynthesisException(f"Speech synthesis failed: {str(e)}")

    def _get_error_code_from_exception(self, exception: Exception) -> str:
        """
        Get error code from exception type.

        Args:
            exception: Exception instance

        Returns:
            str: Error code
        """
        if isinstance(exception, SpeechRecognitionException):
            return 'STT_ERROR'
        elif isinstance(exception, TranslationFailedException):
            return 'TRANSLATION_ERROR'
        elif isinstance(exception, SpeechSynthesisException):
            return 'TTS_ERROR'
        elif isinstance(exception, ValueError):
            return 'VALIDATION_ERROR'
        else:
            return 'SYSTEM_ERROR'

    def _cleanup_temp_files(self) -> None:
        """Cleanup tracked temporary files."""
        for file_path in list(self._temp_files.keys()):
            try:
                if os.path.exists(file_path):
                    os.remove(file_path)
                    logger.debug(f"Cleaned up temp file: {file_path}")
            except Exception as e:
                logger.warning(f"Failed to cleanup temp file {file_path}: {e}")
            finally:
                # Remove from tracking regardless of success
                self._temp_files.pop(file_path, None)

    def get_processing_status(self, correlation_id: str) -> Dict[str, Any]:
        """
        Get processing status for a correlation ID.

        Args:
            correlation_id: Correlation ID to check

        Returns:
            Dict[str, Any]: Processing status information
        """
        # This would be implemented with actual status tracking
        # For now, return basic info
        return {
            'correlation_id': correlation_id,
            'status': 'unknown',
            'message': 'Status tracking not implemented'
        }

    def get_supported_configurations(self) -> Dict[str, Any]:
        """
        Get supported configurations for the processing pipeline.

        Returns:
            Dict[str, Any]: Supported configurations
        """
        return {
            'asr_models': ['whisper-small', 'whisper-medium', 'whisper-large', 'parakeet'],
            'voices': ['kokoro', 'dia', 'cosyvoice2', 'dummy'],
            'languages': [
                'en', 'es', 'fr', 'de', 'it', 'pt', 'ru', 'ja', 'ko', 'zh',
                'ar', 'hi', 'tr', 'pl', 'nl', 'sv', 'da', 'no', 'fi'
            ],
            'audio_formats': self._config.get_processing_config()['supported_audio_formats'],
            'max_file_size_mb': self._config.get_processing_config()['max_file_size_mb'],
            'speed_range': {'min': 0.5, 'max': 2.0}
        }

    def cleanup(self) -> None:
        """Cleanup application service resources."""
        logger.info("Cleaning up AudioProcessingApplicationService")

        # Cleanup temporary files
        self._cleanup_temp_files()

        logger.info("AudioProcessingApplicationService cleanup completed")

    def __enter__(self):
        """Context manager entry."""
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        """Context manager exit with cleanup."""
        self.cleanup()

    def _perform_speech_recognition_with_recovery(
        self,
        audio: AudioContent,
        model: str,
        correlation_id: str
    ) -> TextContent:
        """
        Perform speech-to-text recognition with retry and fallback.

        Args:
            audio: Audio content to transcribe
            model: STT model to use
            correlation_id: Correlation ID for tracking

        Returns:
            TextContent: Transcribed text

        Raises:
            SpeechRecognitionException: If all attempts fail
        """
        context = LogContext(
            correlation_id=correlation_id,
            operation="speech_recognition",
            component="AudioProcessingApplicationService"
        )

        # Configure retry for STT
        retry_config = RetryConfig(
            max_attempts=2,
            base_delay=1.0,
            retryable_exceptions=[SpeechRecognitionException, ConnectionError, TimeoutError]
        )

        def stt_operation():
            return self._perform_speech_recognition(audio, model, correlation_id)

        try:
            # Try with retry
            return self._recovery_manager.retry_with_backoff(
                stt_operation,
                retry_config,
                correlation_id
            )

        except Exception as e:
            # Try fallback models if primary fails
            stt_config = self._config.get_stt_config()
            fallback_models = [m for m in stt_config['preferred_providers'] if m != model]

            if fallback_models:
                logger.warning(
                    f"STT model {model} failed, trying fallbacks: {fallback_models}",
                    context=context,
                    exception=e
                )

                fallback_funcs = [
                    lambda m=fallback_model: self._perform_speech_recognition(audio, m, correlation_id)
                    for fallback_model in fallback_models
                ]

                return self._recovery_manager.execute_with_fallback(
                    stt_operation,
                    fallback_funcs,
                    correlation_id
                )
            else:
                raise

    def _perform_translation_with_recovery(
        self,
        text: TextContent,
        source_language: Optional[str],
        target_language: str,
        correlation_id: str
    ) -> TextContent:
        """
        Perform text translation with retry.

        Args:
            text: Text to translate
            source_language: Source language (optional, auto-detect if None)
            target_language: Target language
            correlation_id: Correlation ID for tracking

        Returns:
            TextContent: Translated text

        Raises:
            TranslationFailedException: If all attempts fail
        """
        # Configure retry for translation
        retry_config = RetryConfig(
            max_attempts=3,
            base_delay=1.0,
            exponential_backoff=True,
            retryable_exceptions=[TranslationFailedException, ConnectionError, TimeoutError]
        )

        def translation_operation():
            return self._perform_translation(text, source_language, target_language, correlation_id)

        return self._recovery_manager.retry_with_backoff(
            translation_operation,
            retry_config,
            correlation_id
        )

    def _perform_speech_synthesis_with_recovery(
        self,
        text: TextContent,
        voice: str,
        speed: float,
        language: str,
        temp_dir: str,
        correlation_id: str
    ) -> str:
        """
        Perform text-to-speech synthesis with fallback providers.

        Args:
            text: Text to synthesize
            voice: Voice to use
            speed: Speech speed
            language: Target language
            temp_dir: Temporary directory for output
            correlation_id: Correlation ID for tracking

        Returns:
            str: Path to generated audio file

        Raises:
            SpeechSynthesisException: If all providers fail
        """
        context = LogContext(
            correlation_id=correlation_id,
            operation="speech_synthesis",
            component="AudioProcessingApplicationService"
        )

        def tts_operation():
            return self._perform_speech_synthesis(text, voice, speed, language, temp_dir, correlation_id)

        try:
            # Try with circuit breaker protection
            return self._recovery_manager.execute_with_circuit_breaker(
                tts_operation,
                f"tts_{voice}",
                CircuitBreakerConfig(failure_threshold=3, recovery_timeout=30.0),
                correlation_id
            )

        except Exception as e:
            # Try fallback TTS providers
            tts_config = self._config.get_tts_config()
            fallback_voices = [v for v in tts_config['preferred_providers'] if v != voice]

            if fallback_voices:
                logger.warning(
                    f"TTS voice {voice} failed, trying fallbacks: {fallback_voices}",
                    context=context,
                    exception=e
                )

                fallback_funcs = [
                    lambda v=fallback_voice: self._perform_speech_synthesis(
                        text, v, speed, language, temp_dir, correlation_id
                    )
                    for fallback_voice in fallback_voices
                ]

                return self._recovery_manager.execute_with_fallback(
                    tts_operation,
                    fallback_funcs,
                    correlation_id
                )
            else:
                raise