//! Whisper Speech Recognition
//!
//! Local speech-to-text using Whisper models.

use crate::error::{DrivenError, Result};
use std::path::{Path, PathBuf};

/// Whisper model size
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum WhisperModel {
    /// Tiny model (~39M params)
    Tiny,
    /// Base model (~74M params)
    Base,
    /// Small model (~244M params)
    Small,
    /// Medium model (~769M params)
    Medium,
    /// Large model (~1.5B params)
    Large,
}

impl WhisperModel {
    /// Get model filename
    pub fn filename(&self) -> &'static str {
        match self {
            Self::Tiny => "whisper-tiny.bin",
            Self::Base => "whisper-base.bin",
            Self::Small => "whisper-small.bin",
            Self::Medium => "whisper-medium.bin",
            Self::Large => "whisper-large.bin",
        }
    }

    /// Get approximate model size in MB
    pub fn size_mb(&self) -> u32 {
        match self {
            Self::Tiny => 75,
            Self::Base => 142,
            Self::Small => 466,
            Self::Medium => 1500,
            Self::Large => 2900,
        }
    }
}

/// Whisper transcription options
#[derive(Debug, Clone)]
pub struct TranscriptionOptions {
    /// Language code (e.g., "en", "es", "auto")
    pub language: String,
    /// Whether to translate to English
    pub translate: bool,
    /// Number of threads for inference
    pub threads: u32,
    /// Temperature for sampling
    pub temperature: f32,
}

impl Default for TranscriptionOptions {
    fn default() -> Self {
        Self {
            language: "en".to_string(),
            translate: false,
            threads: 4,
            temperature: 0.0,
        }
    }
}

/// Transcription result segment
#[derive(Debug, Clone)]
pub struct TranscriptionSegment {
    /// Segment text
    pub text: String,
    /// Start time in milliseconds
    pub start_ms: u64,
    /// End time in milliseconds
    pub end_ms: u64,
    /// Confidence score
    pub confidence: f32,
}

/// Whisper speech recognition client
pub struct WhisperClient {
    model_path: PathBuf,
    options: TranscriptionOptions,
}

impl WhisperClient {
    /// Create a new Whisper client
    pub fn new(model_path: impl AsRef<Path>) -> Result<Self> {
        let model_path = model_path.as_ref().to_path_buf();
        
        if !model_path.exists() {
            return Err(DrivenError::NotFound(format!(
                "Whisper model not found: {}",
                model_path.display()
            )));
        }

        Ok(Self {
            model_path,
            options: TranscriptionOptions::default(),
        })
    }

    /// Set transcription options
    pub fn with_options(mut self, options: TranscriptionOptions) -> Self {
        self.options = options;
        self
    }

    /// Set language
    pub fn with_language(mut self, language: impl Into<String>) -> Self {
        self.options.language = language.into();
        self
    }

    /// Transcribe audio samples
    /// Returns (text, confidence)
    pub async fn transcribe(&self, samples: &[f32]) -> Result<(String, f32)> {
        // This would use whisper-rs or similar binding
        // For now, return placeholder
        self.transcribe_internal(samples).await
    }

    /// Transcribe with timestamps
    pub async fn transcribe_with_timestamps(&self, samples: &[f32]) -> Result<Vec<TranscriptionSegment>> {
        // This would return word-level or segment-level timestamps
        let (text, confidence) = self.transcribe(samples).await?;
        
        Ok(vec![TranscriptionSegment {
            text,
            start_ms: 0,
            end_ms: (samples.len() as f32 / 16.0) as u64, // Assuming 16kHz
            confidence,
        }])
    }

    /// Transcribe audio file
    pub async fn transcribe_file(&self, path: impl AsRef<Path>) -> Result<(String, f32)> {
        let samples = self.load_audio_file(path).await?;
        self.transcribe(&samples).await
    }

    /// Internal transcription (placeholder)
    async fn transcribe_internal(&self, _samples: &[f32]) -> Result<(String, f32)> {
        // In production, this would:
        // 1. Resample audio to 16kHz if needed
        // 2. Compute mel spectrogram
        // 3. Run Whisper model inference
        // 4. Decode tokens to text
        
        // Placeholder implementation
        tracing::debug!(
            "Whisper transcription using model: {}",
            self.model_path.display()
        );
        
        Ok(("".to_string(), 0.0))
    }

    /// Load audio file into samples
    async fn load_audio_file(&self, path: impl AsRef<Path>) -> Result<Vec<f32>> {
        let path = path.as_ref();
        
        // Would use symphonia or rodio to decode audio
        // For now, return empty
        tracing::debug!("Loading audio file: {}", path.display());
        
        Ok(Vec::new())
    }

    /// Get model info
    pub fn model_info(&self) -> ModelInfo {
        ModelInfo {
            path: self.model_path.clone(),
            language: self.options.language.clone(),
        }
    }
}

/// Model information
#[derive(Debug, Clone)]
pub struct ModelInfo {
    /// Model file path
    pub path: PathBuf,
    /// Configured language
    pub language: String,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_model_sizes() {
        assert_eq!(WhisperModel::Tiny.size_mb(), 75);
        assert_eq!(WhisperModel::Large.size_mb(), 2900);
    }

    #[test]
    fn test_default_options() {
        let opts = TranscriptionOptions::default();
        assert_eq!(opts.language, "en");
        assert!(!opts.translate);
    }
}
