Skip to content

Desktop GUI

pathotypr includes a native desktop application built with Tauri. It wraps the same pathotypr-core engine as the CLI, so every workflow produces identical results — just with drag-and-drop, live progress, and interactive tables.

You probably don't need to build this

Signed-format installers for macOS, Linux, and Windows are published for every release. Grab one from the Installation page and skip straight to using the app — the steps below are only for building from source.

Features

  • All five workflows: train, predict, classify, split-fastq, match
  • Drag-and-drop file selection
  • Interactive result tables with sorting and filtering
  • Real-time progress bars with cancellation support
  • Training summary card (accuracy, OOB, CV metrics, model size)
  • Live CPU/RAM usage indicator
  • Light and dark themes
  • Excel export toggle per workflow
  • Configurable parameters with sensible defaults and reset buttons

Building

Prerequisites

  1. Rust — a recent stable toolchain (install via rustup).

  2. Tauri CLI v2:

    cargo install tauri-cli --version "^2"

    Must be the 2.x CLI

    pathotypr targets Tauri 2. A tauri-cli from the 1.x series already on your PATH will fail to build this project. Confirm your version with cargo tauri --version before continuing.

  3. System dependencies — these vary by platform:

    sudo apt install libgtk-3-dev libwebkit2gtk-4.1-dev libjavascriptcoregtk-4.1-dev

    These are the 4.1 packages

    Tauri 2 links against the 4.1 WebKitGTK / JavaScriptCore packages. The older 4.0 variants (all that some distributions, e.g. Ubuntu 20.04, ship by default) will not work — use a newer release or a backport that provides the 4.1 packages.

    The Xcode Command Line Tools are usually pre-installed. If they are missing, install them with:

    xcode-select --install

    The WebView2 runtime is required. It is pre-installed on Windows 11 and most up-to-date Windows 10 machines. If it is missing, install the Evergreen runtime from Microsoft.

Development

cargo tauri dev

Opens the app in development mode with hot-reload for the frontend.

First build is slow

The initial cargo tauri dev compiles the full Rust dependency tree and can take several minutes. Subsequent runs are incremental and start quickly.

Production build

cargo tauri build

With targets: "all" (set in tauri.conf.json), this produces distributable installers under src-tauri/target/release/bundle/:

  • macOS: .dmg in bundle/dmg/ (and the .app in bundle/macos/)
  • Linux: .deb in bundle/deb/, .rpm in bundle/rpm/, and .AppImage in bundle/appimage/
  • Windows: .msi in bundle/msi/ and the NSIS .exe in bundle/nsis/

Self-built apps are unsigned

Binaries you build yourself are not signed with a paid developer certificate, so macOS Gatekeeper and Windows SmartScreen will warn you the first time you open them. See the first-launch notes on the Installation page for how to get past the warning.

Architecture

src-tauri/
├── src/
│   ├── main.rs       # Tauri app setup
│   ├── commands.rs   # Tauri command handlers (bridge GUI → core)
│   ├── state.rs      # Task state management + cancellation
│   └── util.rs       # Progress events + helpers
├── Cargo.toml
└── tauri.conf.json

frontend/
├── index.html        # Main page layout
├── styles.css        # Styling
├── app.js            # App bootstrap
└── js/
    ├── main.js       # Initialization
    ├── forms.js      # Form submission handlers
    ├── results.js    # Result table rendering
    ├── visualization.js  # Charts + summary cards
    ├── progress.js   # Progress bars + cancellation
    ├── config.js     # Default parameters
    ├── state.js      # Run state management
    ├── navigation.js # Tab navigation
    ├── dropzone.js   # Drag-and-drop file handling
    ├── console.js    # Log console drawer
    ├── tauri.js      # Tauri API wrappers
    ├── theme.js      # Light/dark theme toggle
    └── utils.js      # Shared utilities

How it works

  1. Frontend collects form parameters and sends them to Tauri via invoke()
  2. commands.rs deserializes parameters and calls pathotypr_core functions
  3. Core library runs the workflow, emitting progress events back to the frontend
  4. Results are displayed in interactive tables and summary cards
  5. Cancellation token allows stopping any running workflow via the UI

See also