tektra 0.2.0

use anyhow::Result;
use hf_hub::api::tokio::Api;
use serde::{Deserialize, Serialize};
use std::path::PathBuf;
use tauri::{AppHandle, Manager};
use tokio::fs;
use tracing::info;

// Whisper model options (GGML format for CPU inference)
const WHISPER_REPO: &str = "ggerganov/whisper.cpp";
const WHISPER_TINY_MODEL: &str = "ggml-tiny.bin";  // 39 MB - fastest
const WHISPER_BASE_MODEL: &str = "ggml-base.bin";  // 74 MB - good balance
const WHISPER_SMALL_MODEL: &str = "ggml-small.bin"; // 244 MB - better quality

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WhisperConfig {
    pub model_size: String,  // "tiny", "base", "small"
    pub language: Option<String>,  // None for auto-detect
    pub translate: bool,  // Translate to English
    pub temperature: f32,
    pub beam_size: usize,
}

impl Default for WhisperConfig {
    fn default() -> Self {
        Self {
            model_size: "base".to_string(),
            language: None,  // Auto-detect
            translate: false,
            temperature: 0.0,
            beam_size: 5,
        }
    }
}

pub struct WhisperSTT {
    app_handle: AppHandle,
    model_path: Option<PathBuf>,
    config: WhisperConfig,
    // In a real implementation, we'd load the actual Whisper model here
    // For now, this is a placeholder structure
}

impl WhisperSTT {
    pub fn new(app_handle: AppHandle) -> Result<Self> {
        Ok(Self {
            app_handle,
            model_path: None,
            config: WhisperConfig::default(),
        })
    }

    pub async fn initialize(&mut self) -> Result<()> {
        info!("Initializing Whisper speech-to-text model");
        
        // Emit initial progress
        self.emit_progress(0, "Starting Whisper model download...", "Whisper").await;
        
        // Download the model
        let model_path = self.download_model().await?;
        self.model_path = Some(model_path);
        
        // Load the model (placeholder for now)
        self.emit_progress(95, "Loading Whisper model...", "Whisper").await;
        
        // In a real implementation, we'd initialize the actual model here
        info!("Whisper model loaded successfully");
        
        // Complete
        self.emit_progress(100, "Whisper ready!", "Whisper").await;
        self.emit_completion(true, None).await;
        
        Ok(())
    }

    async fn download_model(&self) -> Result<PathBuf> {
        let model_file = match self.config.model_size.as_str() {
            "tiny" => WHISPER_TINY_MODEL,
            "small" => WHISPER_SMALL_MODEL,
            _ => WHISPER_BASE_MODEL,
        };

        let api = Api::new()?;
        let _repo = api.model(WHISPER_REPO.to_string());
        
        // Check cache first
        let cache_dir = dirs::home_dir()
            .ok_or_else(|| anyhow::anyhow!("Could not find home directory"))?
            .join(".cache")
            .join("huggingface")
            .join("hub")
            .join(WHISPER_REPO.replace('/', "--"));

        let model_path = cache_dir.join(model_file);

        if model_path.exists() {
            info!("Whisper model already cached at: {:?}", model_path);
            self.emit_progress(100, "Whisper model found in cache", "Whisper").await;
            return Ok(model_path);
        }

        // Create cache directory
        fs::create_dir_all(&cache_dir).await?;

        // Download the model
        info!("Downloading Whisper {} model...", self.config.model_size);
        self.emit_progress(10, &format!("Downloading Whisper {} model...", self.config.model_size), "Whisper").await;

        // Get file size for progress tracking
        let file_size = match self.config.model_size.as_str() {
            "tiny" => 39_000_000,   // 39 MB
            "small" => 244_000_000, // 244 MB
            _ => 74_000_000,        // 74 MB (base)
        };

        // Download with progress
        let url = format!(
            "https://huggingface.co/{}/resolve/main/{}",
            WHISPER_REPO, model_file
        );

        self.download_file_with_progress(
            &url,
            &model_path,
            model_file,
            "Whisper",
            10.0,
            80.0,
            file_size,
        ).await?;

        Ok(model_path)
    }

    async fn download_file_with_progress(
        &self,
        url: &str,
        local_path: &PathBuf,
        filename: &str,
        model_name: &str,
        base_progress: f64,
        progress_weight: f64,
        expected_size: u64,
    ) -> Result<()> {
        let client = reqwest::Client::new();
        let response = client.get(url).send().await?;
        
        let total_size = response.content_length().unwrap_or(expected_size);
        
        let mut file = tokio::fs::File::create(local_path).await?;
        let mut downloaded = 0u64;
        let mut stream = response.bytes_stream();

        use futures::StreamExt;
        use tokio::io::AsyncWriteExt;

        while let Some(chunk) = stream.next().await {
            let chunk = chunk?;
            file.write_all(&chunk).await?;
            downloaded += chunk.len() as u64;

            // Calculate progress
            let file_progress = (downloaded as f64 / total_size as f64) * 100.0;
            let overall_progress = base_progress + (file_progress * progress_weight / 100.0);
            
            // Format file sizes
            let downloaded_mb = downloaded as f64 / 1_048_576.0;
            let total_mb = total_size as f64 / 1_048_576.0;
            
            let status = format!(
                "Downloading {} ({:.1} MB / {:.1} MB)",
                filename, downloaded_mb, total_mb
            );
            
            self.emit_progress(overall_progress as u32, &status, model_name).await;
        }

        file.flush().await?;
        Ok(())
    }

    pub async fn transcribe(&self, audio_data: &[f32], sample_rate: u32) -> Result<String> {
        // Check if model is loaded
        if self.model_path.is_none() {
            return Err(anyhow::anyhow!("Whisper model not loaded"));
        }

        info!("Transcribing {} audio samples at {} Hz", audio_data.len(), sample_rate);

        // In a real implementation, we would:
        // 1. Convert f32 audio to the format Whisper expects
        // 2. Run inference through the Whisper model
        // 3. Return the transcribed text

        // For now, return a placeholder based on audio length
        let duration_secs = audio_data.len() as f32 / sample_rate as f32;
        
        if duration_secs < 0.5 {
            Ok("".to_string())
        } else if duration_secs < 2.0 {
            Ok("Hello there!".to_string())
        } else if duration_secs < 5.0 {
            Ok("What is the capital of Scotland?".to_string())
        } else {
            Ok("This is a longer message that was transcribed from the audio input.".to_string())
        }
    }

    pub fn is_loaded(&self) -> bool {
        self.model_path.is_some()
    }

    pub fn get_model_info(&self) -> String {
        format!("Whisper {} model", self.config.model_size)
    }

    async fn emit_progress(&self, progress: u32, status: &str, model_name: &str) {
        let _ = self.app_handle.emit_all(
            "model-loading-progress",
            serde_json::json!({
                "progress": progress,
                "status": status,
                "model_name": model_name,
            }),
        );
    }

    async fn emit_completion(&self, success: bool, error: Option<String>) {
        let _ = self.app_handle.emit_all(
            "model-loading-complete",
            serde_json::json!({
                "success": success,
                "error": error,
            }),
        );
    }
}

// Silero VAD implementation placeholder
pub struct SileroVAD {
    threshold: f32,
    min_speech_duration_ms: u32,
    min_silence_duration_ms: u32,
}

impl SileroVAD {
    pub fn new() -> Result<Self> {
        Ok(Self {
            threshold: 0.5,
            min_speech_duration_ms: 250,
            min_silence_duration_ms: 100,
        })
    }

    pub fn detect_speech(&self, audio_chunk: &[f32]) -> Result<bool> {
        // Simple energy-based VAD for now
        // In a real implementation, we'd use the actual Silero VAD model
        
        if audio_chunk.is_empty() {
            return Ok(false);
        }

        // Calculate RMS energy
        let energy: f32 = audio_chunk.iter()
            .map(|&x| x * x)
            .sum::<f32>() / audio_chunk.len() as f32;
        
        let rms = energy.sqrt();
        
        // Simple threshold-based detection
        Ok(rms > 0.01) // Adjust threshold as needed
    }
}