Function download_model

src-tauri/src/main.rs:208–253

(window: Window, model: transcriber::Model)

#[tauri::command]
fn download_model(window: Window, model: transcriber::Model) -> String {
    let channel_name = std::time::SystemTime::now().duration_since(std::time::UNIX_EPOCH).unwrap().as_nanos().to_string();

    println!("Downloading model {model:?}");

    let channel = channel_name.clone();
    tauri::async_runtime::spawn(async move {
        let mut response = reqwest::get(model.download_url()).await.unwrap();

        let mut file = std::fs::File::create(model.path()).unwrap();

        let mut downloaded_size = 0;
        let predicted_size = response.content_length().unwrap_or((model.disk_usage() * 1000000) as _);
        
        while let Some(chunk) = match response.chunk().await {
            Ok(chunk) => chunk,
            Err(err) => {
                window.emit(&channel, serde_json::json!({
                    "type": "error",
                    "value": format!("{err:?}"),
                })).unwrap();
                return;
            },
        } {
            match file.write(&chunk) {
                Ok(length) => downloaded_size += dbg!(length),
                Err(err) => window.emit(&channel, serde_json::json!({
                    "type": "error",
                    "value": format!("{err:?}"),
                })).unwrap(),
            }

            window.emit(&channel, serde_json::json!({
                "type": "progress",
                "value": (downloaded_size as f32 / (predicted_size) as f32) * 100.0,
            })).unwrap();
        }

        window.emit(&channel, serde_json::json!({
            "type": "done",
            "value": "",
        })).unwrap();
    });

    channel_name
}

#[tauri::command]
fn select_language(transcriber: State<'_, Arc<Mutex<transcriber::Transcriber>>>, language: String) {