use serde_json::{json, Value};

use crate::llama::{LlamaConfig, LlamaManager, LlamaStatus};

use std::path::PathBuf;
use std::sync::OnceLock;

/// Global llama manager — persists across command invocations.
fn llama_manager() -> &'static LlamaManager {
    static INSTANCE: OnceLock<LlamaManager> = OnceLock::new();
    INSTANCE.get_or_init(LlamaManager::new)
}

/// Start the local llama-server with a GGUF model.
#[tauri::command]
pub fn llama_start(
    model_path: String,
    binary_path: Option<String>,
    port: Option<u16>,
    n_gpu_layers: Option<i32>,
    context_size: Option<u32>,
    threads: Option<u32>,
) -> Result<LlamaStatus, String> {
    let config = LlamaConfig {
        binary_path: PathBuf::from(
            binary_path.unwrap_or_else(|| "llama-server".to_string()),
        ),
        model_path: PathBuf::from(model_path),
        port: port.unwrap_or(0),
        n_gpu_layers: n_gpu_layers.unwrap_or(0),
        context_size: context_size.unwrap_or(4096),
        threads: threads.unwrap_or(4),
    };

    llama_manager().start(&config)
}

/// Stop the local llama-server.
#[tauri::command]
pub fn llama_stop() -> Result<(), String> {
    llama_manager().stop()
}

/// Get the status of the local llama-server.
#[tauri::command]
pub fn llama_status() -> LlamaStatus {
    llama_manager().status()
}

/// List available GGUF models in the models directory.
#[tauri::command]
pub fn llama_list_models() -> Value {
    let models = LlamaManager::list_models();
    json!({
        "models": models,
        "models_dir": LlamaManager::models_dir().to_string_lossy(),
    })
}

/// Get the app data directory path.
#[tauri::command]
pub fn get_data_dir() -> String {
    LlamaManager::data_dir().to_string_lossy().to_string()
}