ForgeLM/tauri-app/src-tauri/src/lib.rs

use std::sync::Arc;
use std::time::Duration;

use serde::Serialize;
use tauri::{AppHandle, Emitter, Manager, State};

mod gpu;
mod model_library;
mod model_files;
mod model_alias;
mod runtime;

use runtime::process::find_localllm_binary;
use runtime::types::{RuntimeConfig, RuntimeSnapshot};
use runtime::{
    bootstrap_runtime, get_runtime_snapshot, save_runtime_config_command, start_runtime,
    start_runtime_with_status, stop_runtime, RuntimeState,
};
use gpu::query_gpu;
use model_library::huggingface::{
    cancel_hugging_face_download, search_hugging_face_models, start_hugging_face_download,
    HuggingFaceDownloadState,
};
use model_library::local_index::list_entries;
use model_library::paths::{managed_models_dir, model_library_index_path, resolve_models_dir};
use runtime::config::{load_runtime_config, save_runtime_config};
use model_library::types::{ModelLibraryEntry, ModelLibraryState};
use model_files::{import_model, pick_model_files, pick_models_dir, scan_models};

// ─── ForgeAgent types ─────────────────────────────────────────────────────────

#[derive(Clone, Serialize)]
#[serde(rename_all = "lowercase")]
enum ForgeAgentStepStatus {
    Pending,
    Running,
    Completed,
    Error,
}

#[derive(Clone, Serialize)]
struct ForgeAgentSubStep {
    id: String,
    description: String,
    status: ForgeAgentStepStatus,
}

#[derive(Clone, Serialize)]
struct ForgeAgentStep {
    id: u32,
    title: String,
    description: String,
    status: ForgeAgentStepStatus,
    sub_steps: Vec<ForgeAgentSubStep>,
}

#[derive(Clone, Serialize)]
struct ForgeAgentPlan {
    title: String,
    overview: String,
    steps: Vec<ForgeAgentStep>,
}

#[derive(Clone, Serialize)]
struct ForgeAgentArtifact {
    filename: String,
    content: String,
    language: String,
    cta_label: String,
}

#[derive(Clone, Serialize)]
struct ForgeAgentStartPayload {
    plan_id: String,
    plan: ForgeAgentPlan,
}

#[derive(Clone, Serialize)]
struct ForgeAgentStepStartedPayload {
    plan_id: String,
    step_id: u32,
    title: String,
}

#[derive(Clone, Serialize)]
struct ForgeAgentStepCompletedPayload {
    plan_id: String,
    step_id: u32,
    message: String,
}

#[derive(Clone, Serialize)]
struct ForgeAgentArtifactReadyPayload {
    plan_id: String,
    artifact: ForgeAgentArtifact,
}

#[derive(Clone, Serialize)]
struct ForgeAgentPlanCompletedPayload {
    plan_id: String,
    summary: String,
}

// ─── Specialist event types ───────────────────────────────────────────────────

#[derive(Clone, Serialize)]
struct SpecialistThinkingStartedPayload {
    plan_id: String,
    specialist_id: String,
    specialist_name: String,
    specialist_title: String,
}

#[derive(Clone, Serialize)]
struct SpecialistThinkingUpdatePayload {
    plan_id: String,
    specialist_id: String,
    thinking: String,
}

#[derive(Clone, Serialize)]
struct SpecialistResponseCompletedPayload {
    plan_id: String,
    specialist_id: String,
    specialist_name: String,
    thinking: String,
    response: String,
}

#[derive(Clone, Serialize)]
struct AgentFinalResponsePayload {
    plan_id: String,
    summary: String,
}

// ─── Specialist definitions ───────────────────────────────────────────────────

struct Specialist {
    id: &'static str,
    name: &'static str,
    title: &'static str,
    emoji: &'static str,
    prompt_snippet: &'static str,
}

const SPECIALISTS: &[Specialist] = &[
    Specialist {
        id: "analyst",
        name: "Analista",
        title: "Análise Técnica",
        emoji: "🔍",
        prompt_snippet: "Você é um analista técnico rigoroso. Analise o problema decompondo-o em componentes claros, identificando requisitos funcionais e não-funcionais, e avaliando trade-offs.",
    },
    Specialist {
        id: "architect",
        name: "Arquiteto",
        title: "Arquitetura de Software",
        emoji: "🏗️",
        prompt_snippet: "Você é um arquiteto de software experiente. Analise a estrutura, identifique padrões relevantes, proponha abordagens escaláveis e considere a evolução do sistema.",
    },
    Specialist {
        id: "developer",
        name: "Desenvolvedor",
        title: "Implementação",
        emoji: "💻",
        prompt_snippet: "Você é um desenvolvedor pragmático. Foque em código funcional, práticas modernas, legibilidade e manutenibilidade.",
    },
    Specialist {
        id: "critic",
        name: "Crítico",
        title: "Revisão e Riscos",
        emoji: "⚖️",
        prompt_snippet: "Você é um crítico técnico. Identifique riscos, pontos fracos, casos edge e possíveis problemas antes de recomendar uma solução.",
    },
];

fn build_forgeagent_plan(prompt: &str) -> ForgeAgentPlan {
    let trimmed = prompt.trim().trim_end_matches('?');
    let title = if trimmed.is_empty() {
        "Planejando a tarefa".to_string()
    } else {
        trimmed.chars().take(48).collect()
    };
    let mut title_chars = title.chars();
    let capitalized_title = match title_chars.next() {
        Some(first) => first.to_uppercase().collect::<String>() + title_chars.as_str(),
        None => "Planejando a tarefa".to_string(),
    };

    ForgeAgentPlan {
        title: capitalized_title,
        overview: "Vou estruturar um plano curto, executar cada etapa visualmente e entregar a resposta final no estilo ForgeAgent.".to_string(),
        steps: vec![
            ForgeAgentStep {
                id: 1,
                title: "Entender a solicitação".to_string(),
                description: "Quebrar o pedido em objetivo, stack e formato de entrega.".to_string(),
                status: ForgeAgentStepStatus::Pending,
                sub_steps: vec![ForgeAgentSubStep {
                    id: "1.1".to_string(),
                    description: "Extrair o objetivo principal do prompt".to_string(),
                    status: ForgeAgentStepStatus::Pending,
                }],
            },
            ForgeAgentStep {
                id: 2,
                title: "Preparar a solução".to_string(),
                description: "Montar a estrutura, dependências e fluxo recomendado.".to_string(),
                status: ForgeAgentStepStatus::Pending,
                sub_steps: vec![ForgeAgentSubStep {
                    id: "2.1".to_string(),
                    description: "Organizar a implementação em blocos claros".to_string(),
                    status: ForgeAgentStepStatus::Pending,
                }],
            },
            ForgeAgentStep {
                id: 3,
                title: "Gerar a resposta final".to_string(),
                description: "Entregar o conteúdo final com orientação prática e artefato.".to_string(),
                status: ForgeAgentStepStatus::Pending,
                sub_steps: vec![ForgeAgentSubStep {
                    id: "3.1".to_string(),
                    description: "Consolidar a saída final para o usuário".to_string(),
                    status: ForgeAgentStepStatus::Pending,
                }],
            },
        ],
    }
}

fn resolve_runtime_url(config: &RuntimeConfig) -> String {
    match config.connection_mode {
        runtime::types::ConnectionMode::Local => format!("http://{}", config.addr.trim()),
        runtime::types::ConnectionMode::Remote => config.server_url.trim().trim_end_matches('/').to_string(),
    }
}

fn build_multiagent_system_prompt(user_prompt: &str) -> String {
    let mut specialist_prompts = String::new();
    for spec in SPECIALISTS {
        specialist_prompts.push_str(&format!(
            "- **{}** ({}) {}: {}\n",
            spec.name, spec.title, spec.emoji, spec.prompt_snippet
        ));
    }

    format!(
        r#"Você é o agente principal de um sistema multi-agente. Sua tarefa é coordenar múltiplos especialistas para responder à pergunta do usuário da melhor forma possível.

**Especialistas disponíveis:**
{}

**Tarefa:**
A pergunta do usuário é: "{}"

**Instruções:**
1. Analise a pergunta e identifique quais especialistas são relevantes (1 a 4)
2. Para cada especialista relevante, gere uma resposta no seguinte formato JSON:
{{
  "specialist_id": "id_do_especialista",
  "specialist_name": "Nome",
  "thinking": "Sua cadeia de raciocínio - pense passo a passo sobre o problema",
  "response": "Sua resposta final ao usuário sobre o assunto"
}}

3. After all specialists have responded, provide your synthesis as the main agent:
{{
  "type": "final_response",
  "summary": "Resumo consolidado de todas as perspectivas, com a recomendação final para o usuário"
}}

**Importante:**
- Responda TODOS os especialistas relevantes em uma única mensagem JSON
- O campo "thinking" deve mostrar a cadeia de raciocínio completa
- O campo "response" deve ser uma resposta substancial, não um resumo
- Após os especialistas, inclua sempre a resposta final do agente principal
- NÃO gere artefatos de arquivo a menos que o usuário solicite explicitamente
- Responda em português brasileiro"#,
        specialist_prompts, user_prompt
    )
}

// ─── Server lifecycle ─────────────────────────────────────────────────────────

#[tauri::command]
fn bootstrap_runtime_command(
    app: AppHandle,
    state: State<RuntimeState>,
) -> Result<RuntimeSnapshot, String> {
    bootstrap_runtime(app, state)
}

#[tauri::command]
fn get_runtime_snapshot_command(state: State<RuntimeState>) -> Result<RuntimeSnapshot, String> {
    get_runtime_snapshot(state)
}

#[tauri::command]
fn save_runtime_config_command_handler(
    app: AppHandle,
    state: State<RuntimeState>,
    config: RuntimeConfig,
) -> Result<RuntimeSnapshot, String> {
    save_runtime_config_command(app, state, config)
}

#[tauri::command]
fn start_runtime_command(
    app: AppHandle,
    state: State<RuntimeState>,
) -> Result<RuntimeSnapshot, String> {
    start_runtime(app, state)
}

#[tauri::command]
fn stop_runtime_command(state: State<RuntimeState>) -> Result<RuntimeSnapshot, String> {
    stop_runtime(state)
}

/// Returns the resolved path to the localllm binary (useful for debugging).
#[tauri::command]
fn find_server_binary() -> Result<String, String> {
    find_localllm_binary()
        .map(|p| p.to_string_lossy().to_string())
        .ok_or_else(|| {
            "localllm binary not found. Build with: cargo build --release -p inference-server"
                .to_string()
        })
}

/// Returns the app data directory path (useful for debugging).
#[tauri::command]
fn get_app_data_dir(app: AppHandle) -> Result<String, String> {
    app.path()
        .app_data_dir()
        .map(|p| p.to_string_lossy().to_string())
        .map_err(|e| format!("Cannot resolve app data dir: {e}"))
}

#[tauri::command]
async fn search_hugging_face_models_command(
    query: String,
    token: Option<String>,
) -> Result<Vec<model_library::types::HuggingFaceModelResult>, String> {
    search_hugging_face_models(&query, token.as_deref()).await
}

#[tauri::command]
async fn start_hugging_face_download_command(
    app: AppHandle,
    state: State<'_, Arc<HuggingFaceDownloadState>>,
    repo: String,
    file_name: String,
    token: Option<String>,
) -> Result<String, String> {
    // Load the user-configured models_dir (defaults to ./models if None/empty)
    let config = load_runtime_config(&app)?;
    let models_root = resolve_models_dir(config.models_dir.as_deref());

    start_hugging_face_download(
        app,
        state.inner().clone(),
        repo,
        file_name,
        token,
        models_root,
    )
    .await
}

#[tauri::command]
fn cancel_hugging_face_download_command(
    state: State<'_, Arc<HuggingFaceDownloadState>>,
    download_id: String,
) -> Result<(), String> {
    cancel_hugging_face_download(state.inner(), &download_id)
}

/// Swap the active model: stops the runtime, updates model_path, restarts.
/// This is the hot-swap primitive — used when user picks a different local model.
#[tauri::command]
async fn swap_model_command(
    app: AppHandle,
    state: State<'_, RuntimeState>,
    new_model_path: String,
) -> Result<RuntimeSnapshot, String> {
    // Stop first — this is a no-op if already stopped
    stop_runtime(state.clone())?;

    // Load, update model_path, save
    let mut config = load_runtime_config(&app)?;
    config.model_path = Some(new_model_path.clone());
    save_runtime_config(&app, &config)
        .map_err(|e| format!("swap save failed: {e}"))?;

    // CRITICAL: Update the internal state config so start_runtime uses the new model
    {
        let mut inner = state.0.lock().map_err(|e| e.to_string())?;
        inner.config = config;
    }

    // Restart with the updated config
    start_runtime_with_status(app, state, runtime::types::RuntimeStatus::Loading)
}

/// Delete a local .gguf model file from disk.
#[tauri::command]
fn delete_model_file(file_path: String) -> Result<(), String> {
    let path = std::path::Path::new(&file_path);
    if !path.exists() {
        return Err(format!("File not found: {file_path}"));
    }
    std::fs::remove_file(path).map_err(|e| format!("Delete failed: {e}"))
}

/// Resolve which model should be used at startup based on alias policy.
/// Returns None if no suitable model is found.
#[tauri::command]
fn resolve_startup_model_command(
    app: AppHandle,
) -> Result<Option<model_alias::StartupModelResolution>, String> {
    use crate::model_library::paths::resolve_models_dir;

    let config = runtime::config::load_runtime_config(&app).unwrap_or_default();
    let models_dir = resolve_models_dir(config.models_dir.as_deref());

    let models = if models_dir.exists() {
        let dir_str = models_dir.to_string_lossy().to_string();
        model_files::scan_models(dir_str).unwrap_or_default()
    } else {
        Vec::new()
    };

    let preferred = config.preferred_startup_model_alias.as_deref();
    Ok(model_alias::resolve_startup_model(preferred, &models))
}

/// Open the folder containing a model file in the native file explorer
/// and optionally select/highlight the file.
#[tauri::command]
fn open_model_folder_command(file_path: String) -> Result<(), String> {
    let path = std::path::Path::new(&file_path);
    let _folder = path.parent().unwrap_or(path);

    #[cfg(target_os = "windows")]
    {
        std::process::Command::new("explorer")
            .args(["/select,", &file_path])
            .spawn()
            .map_err(|e| format!("Failed to open Explorer: {e}"))?;
    }
    #[cfg(target_os = "macos")]
    {
        std::process::Command::new("open")
            .args(["-R", &file_path])
            .spawn()
            .map_err(|e| format!("Failed to open Finder: {e}"))?;
    }
    #[cfg(target_os = "linux")]
    {
        std::process::Command::new("xdg-open")
            .arg(folder)
            .spawn()
            .map_err(|e| format!("Failed to open file manager: {e}"))?;
    }
    #[cfg(not(any(target_os = "windows", target_os = "macos", target_os = "linux")))]
    {
        let _ = folder; // folder is only used on some platforms
        return Err("Unsupported platform".into());
    }

    Ok(())
}

#[tauri::command]
fn list_model_library_entries_command(app: AppHandle) -> Result<Vec<ModelLibraryEntry>, String> {
    let app_data_dir = app
        .path()
        .app_data_dir()
        .map_err(|error| format!("Cannot resolve app data dir: {error}"))?;
    let index_path = model_library_index_path(&app_data_dir);
    list_entries(&index_path)
}

#[tauri::command]
fn get_model_library_state_command(app: AppHandle) -> Result<ModelLibraryState, String> {
    let app_data_dir = app
        .path()
        .app_data_dir()
        .map_err(|error| format!("Cannot resolve app data dir: {error}"))?;
    let index_path = model_library_index_path(&app_data_dir);

    Ok(ModelLibraryState {
        entries: list_entries(&index_path)?,
        managed_dir: managed_models_dir(&app_data_dir).to_string_lossy().to_string(),
    })
}

#[derive(serde::Deserialize)]
struct ChatCompletionMessage {
    role: String,
    content: String,
}

#[derive(serde::Deserialize)]
struct ChatCompletionChoice {
    message: ChatCompletionMessage,
}

#[derive(serde::Deserialize)]
struct ChatCompletionResponse {
    choices: Vec<ChatCompletionChoice>,
}

#[tauri::command]
async fn start_forgeagent_session_command(
    app: AppHandle,
    state: State<'_, RuntimeState>,
    prompt: String,
) -> Result<ForgeAgentStartPayload, String> {
    let now = std::time::SystemTime::now()
        .duration_since(std::time::UNIX_EPOCH)
        .map_err(|error| format!("clock error: {error}"))?
        .as_millis();
    let plan_id = format!("forgeagent-{now}");
    let plan = build_forgeagent_plan(&prompt);

    let response = ForgeAgentStartPayload {
        plan_id: plan_id.clone(),
        plan: plan.clone(),
    };

    let app_handle = app.clone();

    // Get runtime state and validate before proceeding
    let (runtime_url, connection_mode) = {
        let inner = state.0.lock().map_err(|e| e.to_string())?;
        let url = resolve_runtime_url(&inner.config);
        let mode = inner.config.connection_mode.clone();
        (url, mode)
    };

    // Validate runtime state for local mode before spawning background task
    if connection_mode == runtime::types::ConnectionMode::Local {
        let snapshot = get_runtime_snapshot_command(state.clone())?;
        
        if !snapshot.process_running {
            let _ = app_handle.emit(
                "forgeagent:plan-completed",
                ForgeAgentPlanCompletedPayload {
                    plan_id: plan_id.clone(),
                    summary: "O runtime local não está iniciado. Inicie o runtime antes de usar o ForgeAgent.".to_string(),
                },
            );
            return Err("Runtime not running".into());
        }
        
        if !snapshot.ready {
            let error_message = match snapshot.status.as_str() {
                "loading" => "O modelo está carregando. Aguarde antes de enviar mensagens.".to_string(),
                "error" => {
                    snapshot.last_error
                        .map(|e| format!("Erro no runtime: {}", e.message))
                        .unwrap_or_else(|| "O runtime encontrou um erro. Verifique os logs.".to_string())
                },
                "starting" => "O runtime está iniciando. Aguarde...".to_string(),
                "stopping" => "O runtime está parando. Aguarde finalizar.".to_string(),
                _ => "O runtime não está pronto. Tente reiniciar.".to_string(),
            };
            
            let _ = app_handle.emit(
                "forgeagent:plan-completed",
                ForgeAgentPlanCompletedPayload {
                    plan_id: plan_id.clone(),
                    summary: error_message,
                },
            );
            return Err("Runtime not ready".into());
        }
    }

    let system_prompt = build_multiagent_system_prompt(&prompt);

    // Spawn background task
    std::thread::spawn(move || {
        // Simulate step progression
        for step in &plan.steps {
            let _ = app_handle.emit(
                "forgeagent:step-started",
                ForgeAgentStepStartedPayload {
                    plan_id: plan_id.clone(),
                    step_id: step.id,
                    title: step.title.clone(),
                },
            );
            std::thread::sleep(Duration::from_millis(300));
            let _ = app_handle.emit(
                "forgeagent:step-completed",
                ForgeAgentStepCompletedPayload {
                    plan_id: plan_id.clone(),
                    step_id: step.id,
                    message: match step.id {
                        1 => "Analisei a solicitação e defini o objetivo principal.".to_string(),
                        2 => "Estruturei a solução e organizei a execução em blocos claros.".to_string(),
                        _ => "Consolidei a entrega final.".to_string(),
                    },
                },
            );
        }

        // Build HTTP client
        let Ok(client) = reqwest::blocking::Client::builder()
            .timeout(Duration::from_secs(120))
            .build()
        else {
            eprintln!("Failed to create HTTP client");
            let _ = app_handle.emit(
                "forgeagent:plan-completed",
                ForgeAgentPlanCompletedPayload {
                    plan_id: plan_id.clone(),
                    summary: "Desculpe, houve um erro ao processar sua solicitação. Tente novamente.".to_string(),
                },
            );
            return;
        };

        let request_body = serde_json::json!({
            "model": "local",
            "messages": [
                { "role": "system", "content": system_prompt },
                { "role": "user", "content": &prompt }
            ],
            "stream": false,
            "max_tokens": 2048
        });

        let Ok(resp) = client
            .post(format!("{}/v1/chat/completions", runtime_url))
            .header("Content-Type", "application/json")
            .json(&request_body)
            .send()
        else {
            eprintln!("Failed to call LLM");
            let _ = app_handle.emit(
                "forgeagent:plan-completed",
                ForgeAgentPlanCompletedPayload {
                    plan_id: plan_id.clone(),
                    summary: "Desculpe, houve um erro de comunicação com o servidor de inferência.".to_string(),
                },
            );
            return;
        };

        if !resp.status().is_success() {
            eprintln!("LLM request failed: {}", resp.status());
            let _ = app_handle.emit(
                "forgeagent:plan-completed",
                ForgeAgentPlanCompletedPayload {
                    plan_id: plan_id.clone(),
                    summary: "Desculpe, o servidor de inferência não está disponível. Verifique se o modelo está carregado.".to_string(),
                },
            );
            return;
        }

        let Ok(data) = resp.json::<ChatCompletionResponse>() else {
            eprintln!("Failed to parse LLM response");
            let _ = app_handle.emit(
                "forgeagent:plan-completed",
                ForgeAgentPlanCompletedPayload {
                    plan_id: plan_id.clone(),
                    summary: "Desculpe, houve um erro ao processar a resposta. Tente novamente.".to_string(),
                },
            );
            return;
        };

        let content = data.choices.first()
            .map(|c| c.message.content.clone())
            .unwrap_or_default();

        let Ok(json) = serde_json::from_str::<serde_json::Value>(&content) else {
            let _ = app_handle.emit(
                "forgeagent:plan-completed",
                ForgeAgentPlanCompletedPayload {
                    plan_id: plan_id.clone(),
                    summary: content,
                },
            );
            return;
        };

        if let Some(specialists) = json.get("specialists").and_then(|v| v.as_array()) {
            for spec in specialists {
                let specialist_id = spec.get("specialist_id")
                    .and_then(|v| v.as_str()).unwrap_or("unknown").to_string();
                let specialist_name = spec.get("specialist_name")
                    .and_then(|v| v.as_str()).unwrap_or("Especialista").to_string();
                let thinking = spec.get("thinking")
                    .and_then(|v| v.as_str()).unwrap_or("").to_string();
                let spec_response = spec.get("response")
                    .and_then(|v| v.as_str()).unwrap_or("").to_string();

                let _ = app_handle.emit(
                    "forgeagent:specialist-thought-started",
                    SpecialistThinkingStartedPayload {
                        plan_id: plan_id.clone(),
                        specialist_id: specialist_id.clone(),
                        specialist_name: specialist_name.clone(),
                        specialist_title: specialist_name.clone(),
                    },
                );

                if !thinking.is_empty() {
                    let _ = app_handle.emit(
                        "forgeagent:specialist-thought-update",
                        SpecialistThinkingUpdatePayload {
                            plan_id: plan_id.clone(),
                            specialist_id: specialist_id.clone(),
                            thinking: thinking.clone(),
                        },
                    );
                }

                let _ = app_handle.emit(
                    "forgeagent:specialist-response-completed",
                    SpecialistResponseCompletedPayload {
                        plan_id: plan_id.clone(),
                        specialist_id,
                        specialist_name,
                        thinking,
                        response: spec_response,
                    },
                );
            }
        }

        let summary = json.get("summary")
            .and_then(|v| v.as_str())
            .unwrap_or(&content)
            .to_string();

        let _ = app_handle.emit(
            "forgeagent:plan-completed",
            ForgeAgentPlanCompletedPayload {
                plan_id: plan_id.clone(),
                summary,
            },
        );
    });

    Ok(response)
}

// ─── App entry ────────────────────────────────────────────────────────────────

#[cfg_attr(mobile, tauri::mobile_entry_point)]
pub fn run() {
    tauri::Builder::default()
        .plugin(tauri_plugin_shell::init())
        .plugin(tauri_plugin_dialog::init())
        .manage(RuntimeState::new())
        .manage(Arc::new(HuggingFaceDownloadState::default()))
        .invoke_handler(tauri::generate_handler![
            query_gpu,
            scan_models,
            pick_models_dir,
            pick_model_files,
            import_model,
            search_hugging_face_models_command,
            start_hugging_face_download_command,
            cancel_hugging_face_download_command,
            swap_model_command,
            delete_model_file,
            open_model_folder_command,
            list_model_library_entries_command,
            get_model_library_state_command,
            bootstrap_runtime_command,
            get_runtime_snapshot_command,
            save_runtime_config_command_handler,
            start_runtime_command,
            stop_runtime_command,
            resolve_startup_model_command,
            get_app_data_dir,
            find_server_binary,
            start_forgeagent_session_command,
        ])
        .setup(|app| {
            #[cfg(debug_assertions)]
            {
                let window = app.get_webview_window("main").unwrap();
                window.open_devtools();
            }
            Ok(())
        })
        .run(tauri::generate_context!())
        .expect("error while running tauri application");
}