v7: standalone Sing Detect app — full Tauri project tree

Promotes the v6 detection slice into a complete, buildable Tauri 2 app and renames Stream Studio → Sing Detect (detection-only; video-concat split to its own project). Proper src/ + src-tauri/ layout, Python sidecar under sidecar/, app icons, package/Cargo manifests, and dev/build/pack scripts. Pipeline unchanged in spirit: Rust drives ffmpeg to a 16 kHz mono WAV, the Python sidecar (inaSpeechSegmenter) emits line-JSON segments, and Rust writes EDL / markers CSV / CSV / JSON exports. Long ops run on spawn_blocking; cancel kills the whole py -3.10 process tree. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-26 23:12:25 +08:00
parent 765816e701
commit 4887981a2d
51 changed files with 8250 additions and 52 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,40 @@
 # deps & caches
 node_modules/
 __pycache__/
 *.pyc
 *.wav
 *.mp4
 .DS_Store
 venv/
 .venv/
 venv/
 .pytest_cache/
 .mypy_cache/
 .ruff_cache/
 # build outputs
 target/
 dist/
 dist-portable/
 build/
 # logs & databases
 *.log
 *.db
 *.sqlite
 *.sqlite3
 # secrets / credentials
 .env
 *.env
 *cookie*
 *credential*
 credentials.json
 token.json
 *token.json
 .cookies.json
 google_cred/
 *.pem
 *.key
 # model weights / large artifacts
 *.onnx
 *.pt
 *.pth
 *.ckpt
 *.safetensors
 # os cruft
 .DS_Store
 Thumbs.db
--- a/.vscode/extensions.json
+++ b/.vscode/extensions.json
@@ -0,0 +1,3 @@
 {
  "recommendations": ["tauri-apps.tauri-vscode", "rust-lang.rust-analyzer"]
 }
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -0,0 +1,52 @@
 # Sing Detect — project context for Claude
 This file is auto-loaded each session and travels with the folder, so it stays
 valid even after the folder is renamed (`stream-studio` → `sing_detect_v2`).
 ## What this project is
 A Windows desktop app (**Tauri 2**: Rust backend + TypeScript/WebView2 UI) that
 **detects singing segments** in stream recordings and exports editor markers
 (EDL / markers CSV / CSV / JSON). It is the **detection-only** spin-off of the
 former "Stream Studio"; the video-concat feature was split into a **separate
 project** (`video_concat_v5`, since moved out of this folder).
 - Crate: `sing-detect` / lib `sing_detect_lib`. Product: **Sing Detect**.
 - Detection runs a **Python 3.10 sidecar** (`sidecar/detect.py`,
  inaSpeechSegmenter + TensorFlow). ffmpeg extracts a 16 kHz WAV first.
 ## Build / run (Windows)
 - `dev.bat` — hot-reload dev.
 - `build.bat` — MSI installer → `src-tauri\target\release\bundle\msi\`.
 - `pack.bat` — **standalone portable** folder → `dist-portable\SingDetect\`
  (bundles `Sing Detect.exe` + ffmpeg/ffprobe + the Python `sidecar/`).
 - Requirements on the build machine: Node, Rust (`winget install Rustlang.Rustup`),
  VS 2022 C++ tools, ffmpeg at `C:\ffmpeg\bin` (or on PATH).
 ## ⚠️ State left by the rename (READ FIRST if a build fails)
 - `src-tauri\target\` and `src-tauri\gen\` were **intentionally deleted** before
  the folder rename, because cargo bakes absolute paths into `target\` and a
  build under the old path name fails with `os error 3 (系统找不到指定的路径)`
  referencing the old folder. **The first build after rename is a full ~5–8 min
  recompile** — this is expected, not an error.
 - `node_modules\` and `dist-portable\SingDetect\` were kept (relocatable /
  self-contained). The existing `Sing Detect.exe` in `dist-portable\` works
  regardless of the folder name.
 - Do **not** keep `target\` in a Nextcloud-synced location — it re-introduces the
  stale-absolute-path build failure across machines.
 ## Detection environment (per-machine)
 - Needs **Python 3.10** (TensorFlow has no 3.12+ support). The app's in-UI
  **"Test detection engine"** button checks the env and auto-installs the ML
  packages + downloads the model on first run (or run `sidecar\setup_sidecar.bat`).
 - Setup uses **default international PyPI** and does no proxy manipulation — pip
  and the model download just inherit the system network settings, so a VPN/proxy
  (e.g. Clash) routes them normally. Keep pip reasonably current: the very old pip
  21.2.3 bundled with Python **3.10.0** crashes through an HTTP proxy
  (`check_hostname requires server_hostname`); installing **Python 3.10.11**
  (ships a modern pip) or running `py -3.10 -m pip install -U pip` once avoids it.
 ## Notable backend behavior
 - Long ops (`detect_songs`, `setup_detection`, `export_segments`) run via
  `spawn_blocking` so the window stays responsive (not "未响应").
 - `cancel` kills the whole **process tree** (`taskkill /F /T`), because
  `py -3.10` spawns `python.exe` as a child — killing only `py` left it running.
--- a/README.md
+++ b/README.md
@@ -1,41 +1,85 @@
-# sing_detect
+# Sing Detect
-Singing/music-segment detection for long live-stream recordings: find the
+A fast, native-feeling desktop app that **finds singing segments in a stream
-segments where someone is singing and export markers (CSV / EDL / JSON) for fast
+recording** and exports editor-friendly markers (DaVinci Resolve EDL / markers
-editing in DaVinci Resolve.
+CSV / CSV / JSON). Built with Tauri 2 (Rust backend + TypeScript UI) and a small
 Python ML sidecar.
-**This branch is the latest version (v6).** A plain `git pull` gives you only the
+This is the detection-only spin-off of Stream Studio (the video-concat feature
-current code — no old versions clutter the working tree. The earlier iterations
+lives in its own project now).
 live in the git history and are tagged, so you can inspect or check out any of
 them:
-```bash
+## How it works
-git checkout v3   # or v1 … v6
+
-git checkout -    # back to latest
+```
 recording ──▶ [Rust ▸ ffmpeg ▸ 16 kHz mono WAV]
                                  │
              [Python sidecar ▸ inaSpeechSegmenter] ──json──▶ segments
                                  │
              [Rust ▸ EDL / markers / CSV / JSON exports]
 ```
-## Evolution
+| Layer | Tech | Why |
 |-------|------|-----|
 | UI | **WebView2** via **Tauri 2** | GPU-composited, tiny app, not Electron-sized. |
 | Core | **Rust** | Audio extraction, drives the sidecar, writes exports. No console windows. |
 | Audio | **ffmpeg / ffprobe** | 16 kHz mono WAV extraction. |
 | ML | **Python 3.10 sidecar** | `inaSpeechSegmenter` (TensorFlow) classifies singing as "music"; TensorFlow needs Python 3.10. |
-The history is ordered by **technical & functional evolution** (not file
+## Prerequisites
 timestamp). Each tag is a complete, standalone version at the repository root.
-| Tag | Engine | Interface | Leap |
+- **Node** (18+) and **Rust** (`rustup`, MSVC toolchain) — to build.
-|-----|--------|-----------|------|
+- **ffmpeg + ffprobe** on PATH (or beside the exe) — bundled by `pack.bat`.
-| `v1` | HuggingFace `matthijs/svd` (torch.jit) | tkinter file picker | First prototype — single file, proves the idea |
+- **Python 3.10** on the machine that runs detection. The app's built-in
-| `v2` | `transformers` pipeline + pyannote | CustomTkinter GUI | Overlapping-chunk inference, CSV timecode export |
+  **"Test detection engine"** button installs the ML packages + downloads the
-| `v3` | inaSpeechSegmenter | CLI (argparse) | Robust pipeline: gap-merge, min-duration, EDL+CSV |
+  model for you on first run (or run `sidecar\setup_sidecar.bat` manually).
 | `v4` | inaSpeechSegmenter | CLI batch | Process whole folders, `--recursive`, `--auto-cut` |
 | `v5` | inaSpeechSegmenter | CustomTkinter multi-tab app | Modular `core/` engine + UI tab, JSON + ffmpeg-script export |
 | `v6` | inaSpeechSegmenter (Python sidecar) | Tauri (Rust + TS) | Cross-platform rewrite; Python reduced to a thin ML sidecar emitting line-JSON |
-Engine evolution: `svd model` → `transformers pipeline` →
+## Run / build
 **`inaSpeechSegmenter`** (settled from v3 on, which classifies singing voice as
 "music").
-## Notes
+```bat
- **v4** carries a copy of `detect_singing.py` because `batch_detect.py` imports it at runtime.
+dev.bat        REM hot-reload dev (UI + Rust)
- **v5** keeps the original package layout (`core/`, `ui/`, `utils/`) so the relative imports resolve; it was extracted from the larger `stream_tools` app.
+build.bat      REM release MSI installer → src-tauri\target\release\bundle\msi\
- **v6** is the detection slice only (`sidecar_detect.py`, `detect.rs`, `detect.ts`), extracted from the `stream-studio` Tauri app — `detect.ts` still references the app's `../dom`, `../ui`, `../api` modules.
+pack.bat       REM STANDALONE portable folder → dist-portable\SingDetect\
 ```
-Excluded: `song_detection_cn.py` (an ACRCloud-style fingerprint *song-identification*
+`pack.bat` produces a copy-paste folder with `Sing Detect.exe`, ffmpeg, and the
-client) — it names a track rather than detecting singing segments.
+Python `sidecar/` bundled. Copy it to any Windows PC; the only thing the target
 needs is Python 3.10 (the in-app engine test handles the rest).
 Detection auto-discovers `py -3.10`; override the interpreter with the
 `STREAM_STUDIO_PYTHON` env var, or the script path with `STREAM_STUDIO_SIDECAR`.
 ## Project layout
 ```
 sing_detect_v2/
 ├── index.html
 ├── src/                      # Frontend (TypeScript, no framework)
 │   ├── main.ts               #   shell: single view, theme, drag-drop
 │   ├── api.ts                #   typed bridge to Rust commands + events
 │   ├── dom.ts / ui.ts        #   tiny DOM + toast/progress/song helpers
 │   ├── styles.css            #   Fluent-ish design system (light/dark)
 │   └── views/detect.ts       #   the detection view + engine setup card
 ├── src-tauri/
 │   ├── src/
 │   │   ├── lib.rs            #   commands, state, app wiring
 │   │   ├── tools.rs          #   ffmpeg/ffprobe discovery + helpers
 │   │   ├── detect.rs         #   drives the Python sidecar; env check/setup
 │   │   └── export.rs         #   EDL / markers / CSV / JSON
 │   ├── tauri.conf.json       #   window + bundles sidecar/ as a resource
 │   └── capabilities/         #   dialog + opener permissions
 └── sidecar/
    ├── detect.py             #   the ONLY Python — ML step only
    ├── requirements.txt
    └── setup_sidecar.bat
 ```
 ## Detection parameters
 | Parameter | Default | When to change |
 |-----------|---------|----------------|
 | Merge gap | 10s | Raise if the singer pauses >10s mid-song |
 | Min duration | 20s | Lower for short covers/snippets |
 | Padding | 2s | Raise if songs get clipped at the edges |
 | FPS | 29.97 | Match your video (25 PAL, 23.976 film) — affects timecode export only |
 Singing is classified as **"music"** by inaSpeechSegmenter; talking is "speech".
--- a/build.bat
+++ b/build.bat
@@ -0,0 +1,15 @@
@echo off
 REM Sing Detect - build a distributable Windows app (MSI installer).
 REM Output: src-tauri\target\release\bundle\msi\
 setlocal
 set "PATH=%USERPROFILE%\.cargo\bin;%PATH%"
 if not exist "node_modules" call npm install
 echo Building release bundle (this takes a few minutes the first time)...
 call npm run tauri build
 echo.
 echo Done. Find the installer under:
 echo   src-tauri\target\release\bundle\msi\
 pause
--- a/dev.bat
+++ b/dev.bat
@@ -0,0 +1,13 @@
@echo off
 REM Sing Detect - run in development (hot-reload UI + Rust).
 REM Requires: Node, Rust (cargo), ffmpeg on PATH.
 setlocal
 set "PATH=%USERPROFILE%\.cargo\bin;%PATH%"
 if not exist "node_modules" (
  echo Installing npm dependencies...
  call npm install
 )
 echo Starting Sing Detect (dev)...
 call npm run tauri dev
--- a/index.html
+++ b/index.html
@@ -0,0 +1,14 @@
 <!doctype html>
 <html lang="en">
  <head>
    <meta charset="UTF-8" />
    <link rel="stylesheet" href="/src/styles.css" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>Sing Detect</title>
  </head>
  <body>
    <div id="app"></div>
    <div class="toast-host" id="toasts"></div>
    <script type="module" src="/src/main.ts"></script>
  </body>
 </html>
--- a/pack.bat
+++ b/pack.bat
@@ -0,0 +1,54 @@
@echo off
 REM ============================================================
 REM Sing Detect - build a PORTABLE folder for ANY Windows PC.
 REM
 REM   Run this instead of build.bat when you want a copy-paste app.
 REM   Output:  dist-portable\SingDetect\
 REM   Usage:   copy that whole folder anywhere, double-click
 REM            "Sing Detect.exe". ffmpeg is bundled inside.
 REM ============================================================
 setlocal EnableDelayedExpansion
 set "PATH=%USERPROFILE%\.cargo\bin;%PATH%"
 cd /d "%~dp0"
 REM --- 1. Locate ffmpeg.exe + ffprobe.exe --------------------
 set "FFMPEG="
 set "FFPROBE="
 if exist "C:\ffmpeg\bin\ffmpeg.exe"  set "FFMPEG=C:\ffmpeg\bin\ffmpeg.exe"
 if exist "C:\ffmpeg\bin\ffprobe.exe" set "FFPROBE=C:\ffmpeg\bin\ffprobe.exe"
 if not defined FFMPEG  for /f "delims=" %%i in ('where ffmpeg 2^>nul')  do if not defined FFMPEG  set "FFMPEG=%%i"
 if not defined FFPROBE for /f "delims=" %%i in ('where ffprobe 2^>nul') do if not defined FFPROBE set "FFPROBE=%%i"
 if not defined FFMPEG  ( echo [ERROR] ffmpeg.exe not found. Install ffmpeg or place it in C:\ffmpeg\bin & pause & exit /b 1 )
 if not defined FFPROBE ( echo [ERROR] ffprobe.exe not found. Install ffmpeg or place it in C:\ffmpeg\bin & pause & exit /b 1 )
 echo Using ffmpeg:  !FFMPEG!
 echo Using ffprobe: !FFPROBE!
 REM --- 2. Build the release app ------------------------------
 if not exist "node_modules" call npm install
 echo Building release (this takes a few minutes the first time)...
 call npm run tauri build
 if errorlevel 1 ( echo [ERROR] Build failed. & pause & exit /b 1 )
 REM --- 3. Assemble the portable folder ----------------------
 set "OUT=dist-portable\SingDetect"
 if exist "dist-portable" rmdir /s /q "dist-portable"
 mkdir "%OUT%"
 copy /y "src-tauri\target\release\sing-detect.exe" "%OUT%\Sing Detect.exe" >nul
 copy /y "!FFMPEG!"  "%OUT%\ffmpeg.exe"  >nul
 copy /y "!FFPROBE!" "%OUT%\ffprobe.exe" >nul
 xcopy /e /i /y "sidecar" "%OUT%\sidecar" >nul
 echo.
 echo ============================================================
 echo  DONE. Your portable app is here:
 echo    %CD%\%OUT%
 echo.
 echo  Copy that whole "SingDetect" folder to any Windows PC and
 echo  double-click "Sing Detect.exe". ffmpeg is included.
 echo.
 echo  Detection needs Python 3.10 on the target PC. The app has a
 echo  built-in "Test detection engine" button that installs the
 echo  ML packages and model for you on first run.
 echo ============================================================
 pause
--- a/package-lock.json
+++ b/package-lock.json
--- a/package.json
+++ b/package.json
@@ -0,0 +1,22 @@
 {
  "name": "sing-detect",
  "private": true,
  "version": "2.0.0",
  "type": "module",
  "scripts": {
    "dev": "vite",
    "build": "tsc && vite build",
    "preview": "vite preview",
    "tauri": "tauri"
  },
  "dependencies": {
    "@tauri-apps/api": "^2",
    "@tauri-apps/plugin-opener": "^2",
    "@tauri-apps/plugin-dialog": "^2"
  },
  "devDependencies": {
    "@tauri-apps/cli": "^2",
    "vite": "^6.0.3",
    "typescript": "~5.6.2"
  }
 }
--- a/sidecar/detect.py
+++ b/sidecar/detect.py
@@ -68,14 +68,80 @@ def merge_singing_segments(segments, max_gap, min_duration, padding):
    return results
 def _model_present() -> bool:
    """Best-effort check for cached model weights, without downloading."""
    try:
        import glob
        import os
        import inaSpeechSegmenter
        roots = [
            os.path.dirname(inaSpeechSegmenter.__file__),
            os.path.join(os.path.expanduser("~"), ".keras"),
        ]
        for root in roots:
            for ext in ("*.hdf5", "*.h5", "*.keras", "*.pb"):
                if glob.glob(os.path.join(root, "**", ext), recursive=True):
                    return True
        return False
    except Exception:
        return False
 def run_check() -> int:
    """Report whether the ML packages (and weights) are available."""
    record = {"type": "check", "packages": False, "model": False, "detail": ""}
    try:
        import inaSpeechSegmenter  # noqa: F401
        import tensorflow  # noqa: F401
        record["packages"] = True
    except Exception as exc:  # noqa: BLE001
        record["detail"] = f"Packages not installed ({type(exc).__name__})."
        emit(record)
        return 0
    record["model"] = _model_present()
    record["detail"] = (
        "Ready — packages and model weights are installed."
        if record["model"]
        else "Packages installed; model weights will download on first run."
    )
    emit(record)
    return 0
 def run_warmup() -> int:
    """Instantiate the model once, forcing the weight download if needed."""
    try:
        progress("Loading detection model (downloading weights if needed)…")
        from inaSpeechSegmenter import Segmenter
        Segmenter(vad_engine="smn", detect_gender=False)
        emit({"type": "done", "message": "Model ready."})
        return 0
    except Exception as exc:  # noqa: BLE001
        emit({"type": "error", "message": f"{type(exc).__name__}: {exc}"})
        return 1
 def main() -> int:
    ap = argparse.ArgumentParser()
-    ap.add_argument("--wav", required=True)
+    ap.add_argument("--wav")
    ap.add_argument("--gap", type=float, default=10.0)
    ap.add_argument("--min-duration", type=float, default=20.0)
    ap.add_argument("--padding", type=float, default=2.0)
    ap.add_argument("--check", action="store_true", help="probe environment, then exit")
    ap.add_argument("--warmup", action="store_true", help="download/verify weights, then exit")
    args = ap.parse_args()
    if args.check:
        return run_check()
    if args.warmup:
        return run_warmup()
    if not args.wav:
        emit({"type": "error", "message": "--wav is required for detection."})
        return 1
    try:
        progress("Loading detection model (first run downloads weights)…")
        from inaSpeechSegmenter import Segmenter
--- a/sidecar/requirements.txt
+++ b/sidecar/requirements.txt
@@ -0,0 +1,12 @@
 # Stream Studio detection sidecar — install into a Python 3.10 environment.
 #   py -3.10 -m pip install -r requirements.txt
 #
 # TensorFlow does NOT support Python 3.12+, which is why the sidecar pins 3.10
 # even though the rest of the toolchain can use newer Python.
 # NumPy MUST stay <2: TensorFlow 2.11 is built against NumPy 1.x and crashes on
 # NumPy 2.x with "_pywrap_bfloat16.TF_bfloat16_type() () -> handle". Keep this pin
 # above tensorflow so the resolver installs the compatible NumPy.
 numpy<2
 inaSpeechSegmenter
 tensorflow            # use: tensorflow[and-cuda] for NVIDIA GPU acceleration
--- a/sidecar/setup_sidecar.bat
+++ b/sidecar/setup_sidecar.bat
@@ -0,0 +1,25 @@
@echo off
 REM Install the singing-detection ML dependencies into Python 3.10.
 REM TensorFlow does not support Python 3.12+, so we pin 3.10 via the py launcher.
 py -3.10 --version >nul 2>&1
 if errorlevel 1 (
  echo Python 3.10 not found via the "py" launcher.
  echo Install Python 3.10 from https://www.python.org/downloads/release/python-31011/
  echo Then re-run this script.
  pause
  exit /b 1
 )
 echo Installing inaSpeechSegmenter + TensorFlow into Python 3.10...
 py -3.10 -m pip install --upgrade pip
 py -3.10 -m pip install -r "%~dp0requirements.txt"
 REM Re-pin NumPy last: TensorFlow can drag NumPy 2.x back in, which crashes TF
 REM with "_pywrap_bfloat16.TF_bfloat16_type() () -> handle".
 py -3.10 -m pip install "numpy<2"
 echo.
 echo Verifying...
 py -3.10 -c "from inaSpeechSegmenter import Segmenter; print('inaSpeechSegmenter OK')"
 pause
--- a/src-tauri/.gitignore
+++ b/src-tauri/.gitignore
@@ -0,0 +1,7 @@
 # Generated by Cargo
 # will have compiled files and executables
 /target/
 # Generated by Tauri
 # will have schema files for capabilities auto-completion
 /gen/schemas
--- a/src-tauri/Cargo.lock
+++ b/src-tauri/Cargo.lock
--- a/src-tauri/Cargo.toml
+++ b/src-tauri/Cargo.toml
@@ -0,0 +1,33 @@
 [package]
 name = "sing-detect"
 version = "2.0.0"
 description = "Sing Detect — find singing segments in stream recordings"
 authors = ["youfu"]
 edition = "2021"
 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 [lib]
 # The `_lib` suffix may seem redundant but it is necessary
 # to make the lib name unique and wouldn't conflict with the bin name.
 # This seems to be only an issue on Windows, see https://github.com/rust-lang/cargo/issues/8519
 name = "sing_detect_lib"
 crate-type = ["staticlib", "cdylib", "rlib"]
 [build-dependencies]
 tauri-build = { version = "2", features = [] }
 [dependencies]
 tauri = { version = "2", features = [] }
 tauri-plugin-opener = "2"
 tauri-plugin-dialog = "2"
 serde = { version = "1", features = ["derive"] }
 serde_json = "1"
 [profile.release]
 opt-level = "s"
 lto = true
 codegen-units = 1
 strip = true
 panic = "abort"
--- a/src-tauri/build.rs
+++ b/src-tauri/build.rs
@@ -0,0 +1,3 @@
 fn main() {
    tauri_build::build()
 }
--- a/src-tauri/capabilities/default.json
+++ b/src-tauri/capabilities/default.json
@@ -0,0 +1,15 @@
 {
  "$schema": "../gen/schemas/desktop-schema.json",
  "identifier": "default",
  "description": "Capability for the main window",
  "windows": ["main"],
  "permissions": [
    "core:default",
    "core:event:default",
    "core:window:allow-start-dragging",
    "opener:default",
    "opener:allow-reveal-item-in-dir",
    "dialog:default",
    "dialog:allow-open"
  ]
 }
--- a/src-tauri/icons/128x128.png
+++ b/src-tauri/icons/128x128.png
--- a/src-tauri/icons/128x128@2x.png
+++ b/src-tauri/icons/128x128@2x.png
--- a/src-tauri/icons/32x32.png
+++ b/src-tauri/icons/32x32.png
--- a/src-tauri/icons/Square107x107Logo.png
+++ b/src-tauri/icons/Square107x107Logo.png
--- a/src-tauri/icons/Square142x142Logo.png
+++ b/src-tauri/icons/Square142x142Logo.png
--- a/src-tauri/icons/Square150x150Logo.png
+++ b/src-tauri/icons/Square150x150Logo.png
--- a/src-tauri/icons/Square284x284Logo.png
+++ b/src-tauri/icons/Square284x284Logo.png
--- a/src-tauri/icons/Square30x30Logo.png
+++ b/src-tauri/icons/Square30x30Logo.png
--- a/src-tauri/icons/Square310x310Logo.png
+++ b/src-tauri/icons/Square310x310Logo.png
--- a/src-tauri/icons/Square44x44Logo.png
+++ b/src-tauri/icons/Square44x44Logo.png
--- a/src-tauri/icons/Square71x71Logo.png
+++ b/src-tauri/icons/Square71x71Logo.png
--- a/src-tauri/icons/Square89x89Logo.png
+++ b/src-tauri/icons/Square89x89Logo.png
--- a/src-tauri/icons/StoreLogo.png
+++ b/src-tauri/icons/StoreLogo.png
--- a/src-tauri/icons/icon.icns
+++ b/src-tauri/icons/icon.icns
--- a/src-tauri/icons/icon.ico
+++ b/src-tauri/icons/icon.ico
--- a/src-tauri/icons/icon.png
+++ b/src-tauri/icons/icon.png
--- a/src-tauri/src/detect.rs
+++ b/src-tauri/src/detect.rs
@@ -40,10 +40,166 @@ struct DetectProgress {
    message: String,
 }
 /// Environment readiness for the detection feature.
 #[derive(Clone, Serialize)]
 pub struct DetectionStatus {
    pub python: bool,
    pub packages: bool,
    pub model: bool,
    pub ready: bool,
    pub detail: String,
 }
 #[derive(Clone, Serialize)]
 struct SetupProgress {
    message: String,
    pct: f64,
 }
 fn emit(app: &AppHandle, message: impl Into<String>) {
    let _ = app.emit("detect-progress", DetectProgress { message: message.into() });
 }
 fn emit_setup(app: &AppHandle, message: impl Into<String>, pct: f64) {
    let _ = app.emit("detect-setup-progress", SetupProgress { message: message.into(), pct });
 }
 /// Probe whether Python 3.10 + the ML packages (and weights) are available.
 /// Never installs or downloads anything — purely a read-only check.
 pub fn check_env() -> DetectionStatus {
    let not_ready = |python: bool, detail: &str| DetectionStatus {
        python,
        packages: false,
        model: false,
        ready: false,
        detail: detail.into(),
    };
    let script = match sidecar_script() {
        Some(s) => s,
        None => return not_ready(false, "detect.py was not found next to the app."),
    };
    let mut cmd = python_command(&script);
    cmd.arg("--check");
    cmd.stdout(Stdio::piped()).stderr(Stdio::piped());
    cmd.env("PYTHONIOENCODING", "utf-8");
    hide_window(&mut cmd);
    let out = match cmd.output() {
        Ok(o) => o,
        Err(_) => {
            return not_ready(
                false,
                "Python 3.10 was not found. Install Python 3.10, then click Test again.",
            )
        }
    };
    let stdout = String::from_utf8_lossy(&out.stdout);
    for line in stdout.lines().rev() {
        if let Ok(v) = serde_json::from_str::<Value>(line.trim()) {
            if v.get("type").and_then(|t| t.as_str()) == Some("check") {
                let packages = v.get("packages").and_then(|b| b.as_bool()).unwrap_or(false);
                let model = v.get("model").and_then(|b| b.as_bool()).unwrap_or(false);
                let detail = v.get("detail").and_then(|s| s.as_str()).unwrap_or("").to_string();
                return DetectionStatus { python: true, packages, model, ready: packages, detail };
            }
        }
    }
    // Python launcher ran but produced no status — most likely 3.10 isn't installed.
    not_ready(
        false,
        "Python 3.10 was not found. Install Python 3.10, then click Test again.",
    )
 }
 /// Stream a child command's output to the setup progress channel, holding the
 /// bar at `pct`. Returns an error (with captured stderr) on non-zero exit.
 fn run_streamed(app: &AppHandle, mut cmd: Command, pct: f64) -> Result<(), String> {
    cmd.stdout(Stdio::piped()).stderr(Stdio::piped());
    cmd.env("PYTHONUNBUFFERED", "1").env("PYTHONIOENCODING", "utf-8");
    hide_window(&mut cmd);
    let mut child = cmd.spawn().map_err(|e| format!("Could not start Python ({e})."))?;
    let stdout = child.stdout.take();
    let stderr = child.stderr.take();
    if let Some(out) = stdout {
        for line in BufReader::new(out).lines().map_while(Result::ok) {
            let line = line.trim();
            if !line.is_empty() {
                // detect.py emits JSON; pip emits plain text — show whichever.
                let msg = serde_json::from_str::<Value>(line)
                    .ok()
                    .and_then(|v| v.get("message").and_then(|m| m.as_str()).map(String::from))
                    .unwrap_or_else(|| line.to_string());
                emit_setup(app, msg, pct);
            }
        }
    }
    let mut stderr_text = String::new();
    if let Some(mut se) = stderr {
        use std::io::Read;
        let _ = se.read_to_string(&mut stderr_text);
    }
    let status = child.wait().map_err(|e| format!("process wait failed: {e}"))?;
    if !status.success() {
        let tail: String = stderr_text.chars().rev().take(900).collect::<String>().chars().rev().collect();
        return Err(if tail.trim().is_empty() { "Step failed.".into() } else { tail });
    }
    Ok(())
 }
 /// Install the detection ML stack into Python 3.10 and pre-download the model
 /// weights, streaming progress to the UI. Requires Python 3.10 to be present.
 pub fn setup_env(app: &AppHandle) -> Result<(), String> {
    let script = sidecar_script().ok_or("detect.py was not found next to the app.")?;
    // Preflight: confirm Python 3.10 exists before we try to install into it.
    let mut ver = python_base();
    ver.arg("--version");
    ver.stdout(Stdio::piped()).stderr(Stdio::piped());
    hide_window(&mut ver);
    match ver.output() {
        Ok(o) if o.status.success() => {}
        _ => {
            return Err(
                "Python 3.10 was not found.\n\nInstall it from \
                 https://www.python.org/downloads/release/python-31011/ \
                 (or run: winget install Python.Python.3.10), then click Test again."
                    .into(),
            )
        }
    }
    emit_setup(app, "Upgrading pip…", 0.05);
    let mut pip_up = python_base();
    pip_up.args(["-m", "pip", "install", "--upgrade", "pip"]);
    run_streamed(app, pip_up, 0.05)?;
    emit_setup(app, "Installing inaSpeechSegmenter + TensorFlow (large — a few minutes)…", 0.15);
    let mut pip_main = python_base();
    pip_main.args(["-m", "pip", "install", "inaSpeechSegmenter", "tensorflow"]);
    run_streamed(app, pip_main, 0.15)?;
    // TensorFlow can drag NumPy 2.x back in, which crashes it; pin <2 last.
    emit_setup(app, "Pinning NumPy < 2…", 0.70);
    let mut pip_np = python_base();
    pip_np.args(["-m", "pip", "install", "numpy<2"]);
    run_streamed(app, pip_np, 0.70)?;
    emit_setup(app, "Downloading model weights…", 0.82);
    let mut warm = python_command(&script);
    warm.arg("--warmup");
    run_streamed(app, warm, 0.82)?;
    emit_setup(app, "Setup complete.", 1.0);
    Ok(())
 }
 /// Locate the sidecar script: bundled resources first, dev tree as fallback.
 fn sidecar_script() -> Option<PathBuf> {
    if let Ok(p) = std::env::var("STREAM_STUDIO_SIDECAR") {
@@ -70,27 +226,29 @@ fn sidecar_script() -> Option<PathBuf> {
    None
 }
-/// Build the python invocation. Prefers the Windows `py -3.10` launcher
+/// Build the bare python invocation (no script/args). Prefers the Windows
-/// (TensorFlow does not yet support 3.12+), then a venv, then plain `python`.
+/// `py -3.10` launcher (TensorFlow does not yet support 3.12+), then a custom
-fn python_command(script: &Path) -> Command {
+/// override, then plain `python`. Callers append a script or `-m pip …`.
 fn python_base() -> Command {
    if let Ok(custom) = std::env::var("STREAM_STUDIO_PYTHON") {
-        let mut c = Command::new(custom);
+        return Command::new(custom);
        c.arg(script);
        return c;
    }
    #[cfg(windows)]
    {
        // The py launcher lets us pin 3.10 regardless of the default install.
        let mut c = Command::new("py");
-        c.args(["-3.10"]).arg(script);
+        c.args(["-3.10"]);
        return c;
    }
    #[allow(unreachable_code)]
-    {
+    Command::new("python")
-        let mut c = Command::new("python");
+}
-        c.arg(script);
+
-        c
+/// `python <script>` ready for detection args.
-    }
+fn python_command(script: &Path) -> Command {
    let mut c = python_base();
    c.arg(script);
    c
 }
 /// Extract 16 kHz mono WAV (the format inaSpeechSegmenter expects).
--- a/src-tauri/src/export.rs
+++ b/src-tauri/src/export.rs
@@ -0,0 +1,129 @@
 //! Export detected singing segments to editor-friendly formats.
 //! EDL + DaVinci marker CSV ported 1:1 from the proven Python implementation.
 use std::fmt::Write as _;
 use std::path::{Path, PathBuf};
 use serde::Serialize;
 use crate::detect::Segment;
 #[derive(Clone, Serialize)]
 pub struct ExportResult {
    pub files: Vec<String>,
 }
 fn timecode(seconds: f64, fps: f64) -> String {
    let total_frames = (seconds * fps) as i64;
    let fps_i = fps.round() as i64;
    let ff = total_frames % fps_i;
    let total_seconds = total_frames / fps_i;
    let ss = total_seconds % 60;
    let total_minutes = total_seconds / 60;
    let mm = total_minutes % 60;
    let hh = total_minutes / 60;
    format!("{hh:02}:{mm:02}:{ss:02}:{ff:02}")
 }
 fn hms(seconds: f64) -> String {
    let s = seconds as i64;
    format!("{:02}:{:02}:{:02}", s / 3600, (s % 3600) / 60, s % 60)
 }
 fn write_edl(segs: &[Segment], path: &Path, fps: f64, title: &str, source: &str) -> std::io::Result<()> {
    let reel = Path::new(source)
        .file_stem()
        .and_then(|s| s.to_str())
        .unwrap_or("001")
        .chars()
        .take(32)
        .collect::<String>()
        .replace(' ', "_");
    let mut out = String::new();
    let _ = writeln!(out, "TITLE: {title}");
    let _ = writeln!(out, "FCM: NON-DROP FRAME\n");
    let mut rec_pos = 3600.0_f64; // start at 01:00:00:00
    for s in segs {
        let edit = format!("{:03}", s.index);
        let src_in = timecode(s.start, fps);
        let src_out = timecode(s.end, fps);
        let rec_in = timecode(rec_pos, fps);
        let rec_out = timecode(rec_pos + s.duration, fps);
        let _ = writeln!(out, "{edit}  {reel}  V     C        {src_in} {src_out} {rec_in} {rec_out}");
        let _ = writeln!(out, "* FROM CLIP NAME: {source}");
        let _ = writeln!(out, "* COMMENT: Song {} - Duration {:.0}s\n", s.index, s.duration);
        rec_pos += s.duration;
    }
    std::fs::write(path, out)
 }
 fn write_markers_csv(segs: &[Segment], path: &Path, fps: f64) -> std::io::Result<()> {
    let mut out = String::from("#,Color,Name,Start TC,End TC,Duration TC,Notes\n");
    for s in segs {
        let _ = writeln!(
            out,
            "{},Blue,Song {},{},{},{},Duration: {:.0}s",
            s.index,
            s.index,
            timecode(s.start, fps),
            timecode(s.end, fps),
            timecode(s.duration, fps),
            s.duration
        );
    }
    std::fs::write(path, out)
 }
 fn write_csv(segs: &[Segment], path: &Path) -> std::io::Result<()> {
    let mut out = String::from("index,start_sec,end_sec,duration_sec,start_timecode,end_timecode\n");
    for s in segs {
        let _ = writeln!(
            out,
            "{},{:.2},{:.2},{:.1},{},{}",
            s.index, s.start, s.end, s.duration, hms(s.start), hms(s.end)
        );
    }
    std::fs::write(path, out)
 }
 fn write_json(segs: &[Segment], path: &Path) -> std::io::Result<()> {
    let body = serde_json::to_string_pretty(segs).unwrap_or_else(|_| "[]".into());
    std::fs::write(path, body)
 }
 /// Write all four formats next to (or into `out_dir` for) the source video.
 pub fn export_all(
    segments: &[Segment],
    source_video: &str,
    out_dir: Option<String>,
    fps: f64,
 ) -> Result<ExportResult, String> {
    let src = Path::new(source_video);
    let stem = src.file_stem().and_then(|s| s.to_str()).unwrap_or("output").to_string();
    let source_name = src.file_name().and_then(|s| s.to_str()).unwrap_or(source_video).to_string();
    let dir: PathBuf = match out_dir {
        Some(d) if !d.trim().is_empty() => PathBuf::from(d),
        _ => src.parent().map(|p| p.to_path_buf()).unwrap_or_else(|| PathBuf::from(".")),
    };
    std::fs::create_dir_all(&dir).map_err(|e| format!("Cannot create output dir: {e}"))?;
    let edl = dir.join(format!("{stem}_singing.edl"));
    let csv = dir.join(format!("{stem}_singing.csv"));
    let markers = dir.join(format!("{stem}_markers.csv"));
    let json = dir.join(format!("{stem}_singing.json"));
    write_edl(segments, &edl, fps, &format!("{stem} - Singing"), &source_name)
        .map_err(|e| format!("EDL write failed: {e}"))?;
    write_markers_csv(segments, &markers, fps).map_err(|e| format!("Markers write failed: {e}"))?;
    write_csv(segments, &csv).map_err(|e| format!("CSV write failed: {e}"))?;
    write_json(segments, &json).map_err(|e| format!("JSON write failed: {e}"))?;
    Ok(ExportResult {
        files: [edl, markers, csv, json]
            .iter()
            .map(|p| p.to_string_lossy().into_owned())
            .collect(),
    })
 }
--- a/src-tauri/src/lib.rs
+++ b/src-tauri/src/lib.rs
@@ -0,0 +1,142 @@
 //! Sing Detect — Tauri backend.
 //! Detects singing segments in a recording via a Python ML sidecar
 //! (inaSpeechSegmenter + TensorFlow), with ffmpeg for audio extraction, and
 //! exports editor-friendly markers (EDL / CSV / JSON).
 mod detect;
 mod export;
 mod tools;
 use std::process::Child;
 use std::sync::atomic::AtomicBool;
 use std::sync::Mutex;
 use serde::Serialize;
 use tauri::{AppHandle, Manager, State};
 use detect::{DetectResult, Segment};
 use export::ExportResult;
 pub struct AppState {
    pub proc: Mutex<Option<Child>>,
    pub cancel: AtomicBool,
    pub ffmpeg: Option<String>,
    pub ffprobe: Option<String>,
 }
 impl AppState {
    fn new() -> Self {
        Self {
            proc: Mutex::new(None),
            cancel: AtomicBool::new(false),
            ffmpeg: tools::find_tool("ffmpeg"),
            ffprobe: tools::find_tool("ffprobe"),
        }
    }
 }
 #[derive(Serialize)]
 struct ToolsInfo {
    ffmpeg: Option<String>,
    ffprobe: Option<String>,
 }
 // ── Commands ─────────────────────────────────────────────────────────────────
 #[tauri::command]
 fn get_tools(state: State<AppState>) -> ToolsInfo {
    ToolsInfo {
        ffmpeg: state.ffmpeg.clone(),
        ffprobe: state.ffprobe.clone(),
    }
 }
 /// Probe whether the detection environment (Python 3.10 + ML packages) is
 /// ready. Read-only — installs nothing.
 #[tauri::command]
 async fn check_detection() -> detect::DetectionStatus {
    tauri::async_runtime::spawn_blocking(detect::check_env)
        .await
        .unwrap_or_else(|e| detect::DetectionStatus {
            python: false,
            packages: false,
            model: false,
            ready: false,
            detail: format!("Environment check failed: {e}"),
        })
 }
 /// Install the detection ML stack and pre-download the model weights,
 /// streaming progress via the `detect-setup-progress` event.
 #[tauri::command]
 async fn setup_detection(app: AppHandle) -> Result<(), String> {
    tauri::async_runtime::spawn_blocking(move || detect::setup_env(&app))
        .await
        .map_err(|e| format!("setup task failed: {e}"))?
 }
 // Detection runs the ML sidecar for a long time, so it MUST run off the main
 // thread or the window goes "Not responding". `spawn_blocking` keeps the UI
 // responsive while still resolving with the final result.
 #[tauri::command]
 async fn detect_songs(
    app: AppHandle,
    video: String,
    gap: f64,
    min_duration: f64,
    padding: f64,
 ) -> Result<DetectResult, String> {
    tauri::async_runtime::spawn_blocking(move || {
        let state = app.state::<AppState>();
        let ffmpeg = state.ffmpeg.clone().ok_or("ffmpeg not found.")?;
        detect::run(&app, &state, &ffmpeg, video, gap, min_duration, padding)
    })
    .await
    .map_err(|e| format!("detect task failed: {e}"))?
 }
 #[tauri::command]
 async fn export_segments(
    segments: Vec<Segment>,
    source_video: String,
    out_dir: Option<String>,
    fps: f64,
 ) -> Result<ExportResult, String> {
    tauri::async_runtime::spawn_blocking(move || {
        export::export_all(&segments, &source_video, out_dir, fps)
    })
    .await
    .map_err(|e| format!("export task failed: {e}"))?
 }
 #[tauri::command]
 fn cancel(state: State<AppState>) {
    state.cancel.store(true, std::sync::atomic::Ordering::SeqCst);
    if let Some(child) = state.proc.lock().unwrap().as_mut() {
        // Kill the whole tree: detection runs `py -3.10` → `python.exe`, and
        // killing only `py` would leave the Python/TensorFlow process alive.
        tools::kill_process_tree(child.id());
        let _ = child.kill();
    }
 }
 #[cfg_attr(mobile, tauri::mobile_entry_point)]
 pub fn run() {
    tauri::Builder::default()
        .plugin(tauri_plugin_opener::init())
        .plugin(tauri_plugin_dialog::init())
        .setup(|app| {
            app.manage(AppState::new());
            Ok(())
        })
        .invoke_handler(tauri::generate_handler![
            get_tools,
            check_detection,
            setup_detection,
            detect_songs,
            export_segments,
            cancel,
        ])
        .run(tauri::generate_context!())
        .expect("error while running tauri application");
 }
--- a/src-tauri/src/main.rs
+++ b/src-tauri/src/main.rs
@@ -0,0 +1,6 @@
 // Prevents additional console window on Windows in release, DO NOT REMOVE!!
 #![cfg_attr(not(debug_assertions), windows_subsystem = "windows")]
 fn main() {
    sing_detect_lib::run()
 }
--- a/src-tauri/src/tools.rs
+++ b/src-tauri/src/tools.rs
@@ -0,0 +1,81 @@
 //! ffmpeg / ffprobe discovery, probing, and small shared helpers.
 use std::path::PathBuf;
 use std::process::Command;
 /// On Windows, prevent child processes from flashing a console window.
 #[cfg(windows)]
 pub const CREATE_NO_WINDOW: u32 = 0x0800_0000;
 /// Apply the no-window flag to a Command (no-op on non-Windows).
 pub fn hide_window(cmd: &mut Command) {
    #[cfg(windows)]
    {
        use std::os::windows::process::CommandExt;
        cmd.creation_flags(CREATE_NO_WINDOW);
    }
    #[cfg(not(windows))]
    {
        let _ = cmd;
    }
 }
 /// Forcibly kill a process *and all of its descendants*.
 ///
 /// `Child::kill()` only signals the direct child. That is fine for ffmpeg
 /// (a single process), but detection runs through the `py` launcher, which
 /// spawns the real `python.exe` as a child — killing `py` would orphan the
 /// TensorFlow process and the job would keep running. `taskkill /T` walks the
 /// whole tree.
 pub fn kill_process_tree(pid: u32) {
    #[cfg(windows)]
    {
        let mut cmd = Command::new("taskkill");
        cmd.args(["/F", "/T", "/PID", &pid.to_string()]);
        hide_window(&mut cmd);
        let _ = cmd.output();
    }
    #[cfg(not(windows))]
    {
        // Best-effort on Unix: kill the process group.
        let _ = Command::new("pkill").args(["-TERM", "-P", &pid.to_string()]).output();
    }
 }
 /// Directory the executable lives in (resources dir when bundled).
 fn app_dir() -> Option<PathBuf> {
    std::env::current_exe().ok()?.parent().map(|p| p.to_path_buf())
 }
 /// Locate ffmpeg / ffprobe: next to the exe, then on PATH.
 pub fn find_tool(name: &str) -> Option<String> {
    let exe = format!("{name}.exe");
    if let Some(base) = app_dir() {
        for sub in ["", "bin", "resources", "_internal", ".."] {
            let cand = base.join(sub).join(&exe);
            if cand.exists() {
                return Some(cand.to_string_lossy().into_owned());
            }
        }
    }
    // Fall back to PATH discovery via `where` semantics.
    if which(&exe).is_some() {
        return Some(exe);
    }
    if which(name).is_some() {
        return Some(name.to_string());
    }
    None
 }
 /// Minimal PATH lookup so we don't pull in an extra crate.
 fn which(name: &str) -> Option<PathBuf> {
    let path = std::env::var_os("PATH")?;
    for dir in std::env::split_paths(&path) {
        let full = dir.join(name);
        if full.is_file() {
            return Some(full);
        }
    }
    None
 }
--- a/src-tauri/tauri.conf.json
+++ b/src-tauri/tauri.conf.json
@@ -0,0 +1,42 @@
 {
  "$schema": "https://schema.tauri.app/config/2",
  "productName": "Sing Detect",
  "version": "2.0.0",
  "identifier": "com.youfu.sing-detect",
  "build": {
    "beforeDevCommand": "npm run dev",
    "devUrl": "http://localhost:1420",
    "beforeBuildCommand": "npm run build",
    "frontendDist": "../dist"
  },
  "app": {
    "withGlobalTauri": true,
    "windows": [
      {
        "title": "Sing Detect",
        "width": 960,
        "height": 820,
        "minWidth": 720,
        "minHeight": 620,
        "dragDropEnabled": true
      }
    ],
    "security": {
      "csp": null
    }
  },
  "bundle": {
    "active": true,
    "targets": ["msi"],
    "resources": {
      "../sidecar": "sidecar"
    },
    "icon": [
      "icons/32x32.png",
      "icons/128x128.png",
      "icons/128x128@2x.png",
      "icons/icon.icns",
      "icons/icon.ico"
    ]
  }
 }
--- a/src/api.ts
+++ b/src/api.ts
@@ -0,0 +1,43 @@
 // Typed bridge to the Rust backend (detection-only build).
 import { invoke } from "@tauri-apps/api/core";
 import { listen, type UnlistenFn } from "@tauri-apps/api/event";
 import { getCurrentWebview } from "@tauri-apps/api/webview";
 import { open } from "@tauri-apps/plugin-dialog";
 import { revealItemInDir } from "@tauri-apps/plugin-opener";
 export interface Tools { ffmpeg: string | null; ffprobe: string | null; }
 export interface Segment { index: number; start: number; end: number; duration: number; }
 export interface DetectResult { segments: Segment[]; stats: Record<string, number>; }
 export interface ExportResult { files: string[]; }
 export interface DetectionStatus { python: boolean; packages: boolean; model: boolean; ready: boolean; detail: string; }
 export interface SetupProgress { message: string; pct: number; }
 export const getTools = () => invoke<Tools>("get_tools");
 export const checkDetection = () => invoke<DetectionStatus>("check_detection");
 export const setupDetection = () => invoke<void>("setup_detection");
 export const detectSongs = (video: string, gap: number, minDuration: number, padding: number) =>
  invoke<DetectResult>("detect_songs", { video, gap, minDuration, padding });
 export const exportSegments = (segments: Segment[], sourceVideo: string, outDir: string | null, fps: number) =>
  invoke<ExportResult>("export_segments", { segments, sourceVideo, outDir, fps });
 export const cancel = () => invoke<void>("cancel");
 export const onDetectProgress = (cb: (msg: string) => void): Promise<UnlistenFn> =>
  listen<{ message: string }>("detect-progress", (e) => cb(e.payload.message));
 export const onDetectSetupProgress = (cb: (p: SetupProgress) => void): Promise<UnlistenFn> =>
  listen<SetupProgress>("detect-setup-progress", (e) => cb(e.payload));
 export const onFilesDropped = (cb: (paths: string[]) => void): Promise<UnlistenFn> =>
  getCurrentWebview().onDragDropEvent((e) => {
    if (e.payload.type === "drop") cb(e.payload.paths);
  });
 export async function pickVideo(): Promise<string | null> {
  const sel = await open({
    multiple: false,
    filters: [{ name: "Video", extensions: ["mp4", "mov", "mkv", "avi", "flv", "ts", "wmv", "m4v", "webm", "mts", "m2ts"] }],
  });
  return typeof sel === "string" ? sel : null;
 }
 export const reveal = (path: string) => revealItemInDir(path);
--- a/src/assets/tauri.svg
+++ b/src/assets/tauri.svg
@@ -0,0 +1,6 @@
 <svg width="206" height="231" viewBox="0 0 206 231" fill="none" xmlns="http://www.w3.org/2000/svg">
 <path d="M143.143 84C143.143 96.1503 133.293 106 121.143 106C108.992 106 99.1426 96.1503 99.1426 84C99.1426 71.8497 108.992 62 121.143 62C133.293 62 143.143 71.8497 143.143 84Z" fill="#FFC131"/>
 <ellipse cx="84.1426" cy="147" rx="22" ry="22" transform="rotate(180 84.1426 147)" fill="#24C8DB"/>
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 </svg>
--- a/src/assets/typescript.svg
+++ b/src/assets/typescript.svg
@@ -0,0 +1,25 @@
 <?xml version="1.0" standalone="no"?>
 <!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 20010904//EN"
 "http://www.w3.org/TR/2001/REC-SVG-20010904/DTD/svg10.dtd">
 <svg version="1.0" xmlns="http://www.w3.org/2000/svg"
 width="512.000000pt" height="512.000000pt" viewBox="0 0 512.000000 512.000000"
 preserveAspectRatio="xMidYMid meet">
 <g transform="translate(0.000000,512.000000) scale(0.100000,-0.100000)"
 fill="#2D79C7" stroke="none">
 <path d="M430 5109 c-130 -19 -248 -88 -325 -191 -53 -71 -83 -147 -96 -247
 -6 -49 -9 -813 -7 -2166 l3 -2090 22 -65 c54 -159 170 -273 328 -323 l70 -22
 2140 0 2140 0 66 23 c160 55 272 169 322 327 l22 70 0 2135 0 2135 -22 70
 c-49 157 -155 265 -319 327 l-59 23 -2115 1 c-1163 1 -2140 -2 -2170 -7z
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 -175 -110 -175 -193 0 -110 97 -197 335 -300 140 -61 309 -146 375 -189 30
 -20 87 -68 126 -107 119 -117 164 -234 164 -426 0 -310 -145 -518 -430 -613
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 682 492 109 13 401 6 506 -13z m-1391 -241 l0 -205 -320 0 -320 0 0 -915 0
 -915 -255 0 -255 0 0 915 0 915 -320 0 -320 0 0 205 0 205 895 0 895 0 0 -205z"/>
 </g>
 </svg>
--- a/src/assets/vite.svg
+++ b/src/assets/vite.svg
@@ -0,0 +1 @@
 <svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
--- a/src/dom.ts
+++ b/src/dom.ts
@@ -0,0 +1,78 @@
 // Minimal DOM helpers — keeps the views declarative without a framework.
 type Props = Record<string, unknown>;
 type Child = Node | string | null | undefined | false;
 export function h<K extends keyof HTMLElementTagNameMap>(
  tag: K,
  props: Props = {},
  ...children: Child[]
 ): HTMLElementTagNameMap[K] {
  const el = document.createElement(tag);
  for (const [k, v] of Object.entries(props)) {
    if (v == null || v === false) continue;
    if (k === "class") el.className = String(v);
    else if (k === "html") el.innerHTML = String(v);
    else if (k.startsWith("on") && typeof v === "function") {
      el.addEventListener(k.slice(2).toLowerCase(), v as EventListener);
    } else if (k === "dataset" && typeof v === "object") {
      Object.assign(el.dataset, v);
    } else if (k in el) {
      // direct property (value, disabled, checked, textContent…)
      (el as any)[k] = v;
    } else {
      el.setAttribute(k, String(v));
    }
  }
  for (const c of children.flat()) {
    if (c == null || c === false) continue;
    el.append(c instanceof Node ? c : document.createTextNode(String(c)));
  }
  return el;
 }
 export const $ = <T extends Element = HTMLElement>(sel: string, root: ParentNode = document) =>
  root.querySelector<T>(sel);
 export function clear(el: Element) {
  while (el.firstChild) el.removeChild(el.firstChild);
 }
 export function basename(p: string): string {
  return p.replace(/[/\\]+$/, "").split(/[/\\]/).pop() ?? p;
 }
 export function stem(p: string): string {
  const b = basename(p);
  const i = b.lastIndexOf(".");
  return i > 0 ? b.slice(0, i) : b;
 }
 export function dirname(p: string): string {
  const norm = p.replace(/[/\\]+$/, "");
  const i = Math.max(norm.lastIndexOf("/"), norm.lastIndexOf("\\"));
  return i >= 0 ? norm.slice(0, i) : "";
 }
 export function joinPath(dir: string, name: string): string {
  if (!dir) return name;
  const sep = dir.includes("\\") ? "\\" : "/";
  return dir.replace(/[/\\]+$/, "") + sep + name;
 }
 export function fmtDur(seconds: number): string {
  if (seconds < 0) return "…";
  if (!seconds) return "—";
  const s = Math.round(seconds);
  const h = Math.floor(s / 3600);
  const m = Math.floor((s % 3600) / 60);
  const sec = s % 60;
  return h ? `${h}:${String(m).padStart(2, "0")}:${String(sec).padStart(2, "0")}`
           : `${m}:${String(sec).padStart(2, "0")}`;
 }
 export function fmtElapsed(s: number): string {
  if (s < 60) return `${Math.round(s)}s`;
  const m = Math.floor(s / 60);
  return `${m}m ${String(Math.round(s % 60)).padStart(2, "0")}s`;
 }
--- a/src/main.ts
+++ b/src/main.ts
@@ -0,0 +1,60 @@
 // Sing Detect — app shell: title bar, theme, drag-drop.
 import "./styles.css";
 import { h } from "./dom";
 import { toast } from "./ui";
 import { getTools, onFilesDropped } from "./api";
 import { buildDetect } from "./views/detect";
 async function boot() {
  // ── Theme (system default, persisted toggle) ────────────────────────────
  const saved = localStorage.getItem("theme");
  const prefersDark = matchMedia("(prefers-color-scheme: dark)").matches;
  let theme = saved ?? (prefersDark ? "dark" : "light");
  const applyTheme = () => document.documentElement.setAttribute("data-theme", theme);
  applyTheme();
  // ── Single view: detection ──────────────────────────────────────────────
  const detect = buildDetect();
  const themeBtn = h("button", { class: "icon-btn", title: "Toggle theme" },
    theme === "dark" ? "☀" : "☾") as HTMLButtonElement;
  themeBtn.addEventListener("click", () => {
    theme = theme === "dark" ? "light" : "dark";
    localStorage.setItem("theme", theme);
    applyTheme();
    themeBtn.textContent = theme === "dark" ? "☀" : "☾";
  });
  const app = document.getElementById("app")!;
  app.append(
    h("div", { class: "titlebar" },
      h("div", { class: "brand" },
        h("div", { class: "logo" }, "🎤"),
        h("span", {}, "Sing Detect")),
      h("div", { class: "spacer" }),
      themeBtn,
    ),
    h("div", { class: "content" }, detect.el),
  );
  // First-show hook: highlight the engine test + silent env probe.
  detect.onShow?.();
  // ── Drag-and-drop routes to the detection view ──────────────────────────
  await onFilesDropped((paths) => detect.onDrop(paths));
  // ── ffmpeg health check ─────────────────────────────────────────────────
  try {
    const tools = await getTools();
    if (!tools.ffmpeg) {
      const banner = h("div", { class: "banner", style: "margin:0 22px 12px" },
        "⚠ ffmpeg was not found. Put ffmpeg.exe & ffprobe.exe next to the app or on your PATH — detection needs it to extract audio.");
      app.querySelector(".content")!.before(banner);
    }
  } catch (e) {
    toast(`Backend not ready: ${e}`, "err");
  }
 }
 boot();
--- a/src/styles.css
+++ b/src/styles.css
@@ -0,0 +1,349 @@
 /* ============================================================================
   Stream Studio — design system
   Native-feeling on Windows 11: Segoe UI Variable, Fluent-ish surfaces,
   GPU-friendly transitions, custom scrollbars. Light/dark via [data-theme].
   ========================================================================= */
 :root {
  --accent: #6d5efc;
  --accent-2: #8b7bff;
  --accent-ink: #ffffff;
  --bg: #f4f5fb;
  --bg-grad-1: #eef0fb;
  --bg-grad-2: #f7f5ff;
  --surface: #ffffff;
  --surface-2: #f6f7fc;
  --surface-hover: #eef0fa;
  --border: #e4e6f0;
  --border-strong: #d3d6e6;
  --text: #1b1c28;
  --text-dim: #5a5d70;
  --text-faint: #8b8fa6;
  --ok: #16a34a;
  --warn: #f59e0b;
  --danger: #e11d48;
  --shadow-sm: 0 1px 2px rgba(20, 22, 50, 0.06), 0 1px 3px rgba(20, 22, 50, 0.05);
  --shadow-md: 0 4px 16px rgba(20, 22, 50, 0.08), 0 2px 6px rgba(20, 22, 50, 0.05);
  --shadow-lg: 0 18px 50px rgba(20, 22, 50, 0.16);
  --radius: 12px;
  --radius-lg: 16px;
  --radius-sm: 8px;
  --ease: cubic-bezier(0.22, 1, 0.36, 1);
 }
 [data-theme="dark"] {
  --accent: #8b7bff;
  --accent-2: #a594ff;
  --accent-ink: #14121f;
  --bg: #0e0f17;
  --bg-grad-1: #11111d;
  --bg-grad-2: #161427;
  --surface: #1a1b27;
  --surface-2: #20212f;
  --surface-hover: #262838;
  --border: #2a2c3c;
  --border-strong: #383b50;
  --text: #eceefb;
  --text-dim: #a6a9bf;
  --text-faint: #71748c;
  --shadow-sm: 0 1px 2px rgba(0, 0, 0, 0.3);
  --shadow-md: 0 6px 22px rgba(0, 0, 0, 0.4);
  --shadow-lg: 0 22px 60px rgba(0, 0, 0, 0.55);
 }
 * { box-sizing: border-box; margin: 0; padding: 0; }
 html, body {
  height: 100%;
  overflow: hidden;
 }
 body {
  font-family: "Segoe UI Variable Text", "Segoe UI", system-ui, -apple-system, sans-serif;
  font-size: 14px;
  color: var(--text);
  background: radial-gradient(1200px 600px at 80% -10%, var(--bg-grad-2), transparent 60%),
              radial-gradient(900px 500px at -10% 110%, var(--bg-grad-1), transparent 55%),
              var(--bg);
  -webkit-font-smoothing: antialiased;
  text-rendering: optimizeLegibility;
  user-select: none;
 }
 button, input, select { font-family: inherit; font-size: inherit; color: inherit; }
 :focus-visible { outline: 2px solid var(--accent); outline-offset: 2px; }
 /* ── Scrollbars ───────────────────────────────────────────────────────── */
 ::-webkit-scrollbar { width: 10px; height: 10px; }
 ::-webkit-scrollbar-thumb {
  background: var(--border-strong);
  border-radius: 99px;
  border: 3px solid transparent;
  background-clip: content-box;
 }
 ::-webkit-scrollbar-thumb:hover { background: var(--text-faint); background-clip: content-box; }
 /* ── App shell ────────────────────────────────────────────────────────── */
 #app { display: flex; flex-direction: column; height: 100vh; }
 .titlebar {
  display: flex; align-items: center; gap: 12px;
  padding: 14px 22px;
  -webkit-app-region: drag;
 }
 .titlebar .brand {
  display: flex; align-items: center; gap: 10px;
  font-weight: 650; font-size: 16px; letter-spacing: 0.2px;
 }
 .titlebar .logo {
  width: 26px; height: 26px; border-radius: 8px;
  background: linear-gradient(135deg, var(--accent), var(--accent-2));
  display: grid; place-items: center; color: var(--accent-ink);
  box-shadow: var(--shadow-sm);
  font-size: 15px;
 }
 .titlebar .spacer { flex: 1; }
 .icon-btn {
  -webkit-app-region: no-drag;
  width: 34px; height: 34px; border-radius: 9px;
  border: 1px solid transparent; background: transparent;
  color: var(--text-dim); cursor: pointer;
  display: grid; place-items: center; font-size: 16px;
  transition: background .18s var(--ease), color .18s var(--ease);
 }
 .icon-btn:hover { background: var(--surface-hover); color: var(--text); }
 /* ── Tabs ─────────────────────────────────────────────────────────────── */
 .tabs {
  -webkit-app-region: no-drag;
  display: flex; gap: 4px;
  margin: 0 22px;
  padding: 4px;
  background: var(--surface-2);
  border: 1px solid var(--border);
  border-radius: 12px;
  width: fit-content;
 }
 .tab {
  display: flex; align-items: center; gap: 7px;
  padding: 8px 16px; border-radius: 9px;
  border: none; background: transparent; cursor: pointer;
  color: var(--text-dim); font-weight: 550;
  transition: color .18s var(--ease), background .18s var(--ease);
 }
 .tab:hover { color: var(--text); }
 .tab.active {
  color: var(--text);
  background: var(--surface);
  box-shadow: var(--shadow-sm);
 }
 /* ── Content area ─────────────────────────────────────────────────────── */
 .content { flex: 1; overflow-y: auto; padding: 18px 22px 28px; }
 .view { display: flex; flex-direction: column; gap: 16px; max-width: 880px; margin: 0 auto; }
 .view[hidden] { display: none; }
 .card {
  background: var(--surface);
  border: 1px solid var(--border);
  border-radius: var(--radius-lg);
  box-shadow: var(--shadow-sm);
  padding: 18px;
 }
 .card h2 {
  font-size: 13px; font-weight: 650; letter-spacing: .2px;
  color: var(--text); margin-bottom: 14px;
  display: flex; align-items: center; gap: 8px;
 }
 .card h2 .step {
  width: 20px; height: 20px; border-radius: 6px;
  background: var(--surface-2); border: 1px solid var(--border);
  display: grid; place-items: center; font-size: 11px; color: var(--text-dim);
 }
 .muted { color: var(--text-dim); }
 .faint { color: var(--text-faint); font-size: 12px; }
 /* ── Buttons ──────────────────────────────────────────────────────────── */
 .btn {
  -webkit-app-region: no-drag;
  display: inline-flex; align-items: center; gap: 8px;
  padding: 9px 16px; border-radius: 10px;
  border: 1px solid var(--border-strong); background: var(--surface);
  color: var(--text); font-weight: 550; cursor: pointer;
  transition: transform .12s var(--ease), background .18s var(--ease),
              border-color .18s var(--ease), box-shadow .18s var(--ease), opacity .18s;
 }
 .btn:hover { background: var(--surface-hover); }
 .btn:active { transform: translateY(1px) scale(0.99); }
 .btn:disabled { opacity: .45; cursor: not-allowed; }
 .btn.primary {
  background: linear-gradient(135deg, var(--accent), var(--accent-2));
  border-color: transparent; color: var(--accent-ink);
  box-shadow: 0 6px 18px color-mix(in srgb, var(--accent) 40%, transparent);
 }
 .btn.primary:hover { filter: brightness(1.05); background: linear-gradient(135deg, var(--accent), var(--accent-2)); }
 .btn.ghost { background: transparent; border-color: transparent; color: var(--text-dim); }
 .btn.ghost:hover { background: var(--surface-hover); color: var(--text); }
 .btn.danger:hover { border-color: var(--danger); color: var(--danger); }
 .btn.lg { padding: 12px 22px; font-size: 15px; }
 .btn.accent-soft {
  background: color-mix(in srgb, var(--accent) 14%, var(--surface));
  border-color: color-mix(in srgb, var(--accent) 30%, transparent);
  color: var(--accent);
 }
 /* ── Inputs ───────────────────────────────────────────────────────────── */
 .field { display: flex; flex-direction: column; gap: 6px; }
 .field label { font-size: 12px; color: var(--text-dim); font-weight: 550; }
 .input, select.input {
  -webkit-app-region: no-drag;
  width: 100%; padding: 9px 12px; border-radius: 10px;
  border: 1px solid var(--border-strong); background: var(--surface-2);
  color: var(--text); transition: border-color .18s var(--ease), background .18s var(--ease);
  user-select: text;
 }
 .input:focus { border-color: var(--accent); background: var(--surface); }
 .row { display: flex; gap: 10px; align-items: center; }
 .row.wrap { flex-wrap: wrap; }
 .grow { flex: 1; min-width: 0; }
 /* ── Dropzone ─────────────────────────────────────────────────────────── */
 .dropzone {
  border: 1.5px dashed var(--border-strong); border-radius: var(--radius);
  padding: 22px; text-align: center; color: var(--text-dim);
  background: var(--surface-2);
  transition: border-color .2s var(--ease), background .2s var(--ease), color .2s var(--ease);
 }
 .dropzone.hot {
  border-color: var(--accent); color: var(--accent);
  background: color-mix(in srgb, var(--accent) 8%, var(--surface));
 }
 /* ── File list ────────────────────────────────────────────────────────── */
 .list { display: flex; flex-direction: column; gap: 6px; max-height: 280px; overflow-y: auto; padding-right: 4px; }
 .list-head, .list-row {
  display: grid; grid-template-columns: 30px 26px 1fr 72px 32px;
  align-items: center; gap: 6px;
 }
 .list-head { padding: 4px 10px; font-size: 11px; color: var(--text-faint); font-weight: 650; }
 .list-row {
  padding: 9px 10px; border-radius: 10px;
  background: var(--surface-2); border: 1px solid transparent;
  transition: background .15s var(--ease), border-color .15s var(--ease);
 }
 .list-row:hover { background: var(--surface-hover); }
 .list-row.active { border-color: color-mix(in srgb, var(--accent) 50%, transparent);
  background: color-mix(in srgb, var(--accent) 10%, var(--surface)); }
 .list-row .idx { color: var(--text-faint); font-size: 12px; text-align: center; }
 .list-row .name { overflow: hidden; text-overflow: ellipsis; white-space: nowrap; }
 .list-row .dur { text-align: right; color: var(--text-dim); font-variant-numeric: tabular-nums; font-size: 12px; }
 .list-row .x {
  width: 24px; height: 24px; border-radius: 7px; border: none; cursor: pointer;
  background: transparent; color: var(--text-faint);
  transition: background .15s, color .15s;
 }
 .list-row .x:hover { background: color-mix(in srgb, var(--danger) 14%, transparent); color: var(--danger); }
 .checkbox { width: 17px; height: 17px; accent-color: var(--accent); cursor: pointer; }
 .toolbar { display: flex; gap: 8px; flex-wrap: wrap; }
 /* ── Progress ─────────────────────────────────────────────────────────── */
 .progress-wrap { display: flex; flex-direction: column; gap: 8px; }
 .bar { height: 10px; border-radius: 99px; background: var(--surface-2);
  overflow: hidden; border: 1px solid var(--border); }
 .bar > i {
  display: block; height: 100%; width: 0%;
  background: linear-gradient(90deg, var(--accent), var(--accent-2));
  border-radius: 99px; transition: width .25s var(--ease);
 }
 .bar.indeterminate > i {
  width: 35% !important; border-radius: 99px;
  animation: slide 1.1s var(--ease) infinite;
 }
@keyframes slide { 0% { margin-left: -35%; } 100% { margin-left: 100%; } }
 .progress-meta { display: flex; justify-content: space-between; font-size: 12px; }
 .progress-meta .status { color: var(--text); font-weight: 550; }
 .progress-meta .timing { color: var(--text-faint); font-variant-numeric: tabular-nums; }
 /* ── Log ──────────────────────────────────────────────────────────────── */
 .log {
  margin-top: 10px; max-height: 150px; overflow-y: auto;
  font-family: "Cascadia Code", "Consolas", monospace; font-size: 12px;
  background: var(--surface-2); border: 1px solid var(--border);
  border-radius: 10px; padding: 10px; color: var(--text-dim);
  white-space: pre-wrap; user-select: text;
 }
 .log:empty { display: none; }
 /* ── Songs result table ───────────────────────────────────────────────── */
 .songs { display: flex; flex-direction: column; gap: 4px; }
 .song {
  display: grid; grid-template-columns: 46px 1fr auto;
  gap: 10px; align-items: center;
  padding: 8px 12px; border-radius: 9px; background: var(--surface-2);
 }
 .song .badge {
  background: color-mix(in srgb, var(--accent) 16%, var(--surface));
  color: var(--accent); border-radius: 6px; padding: 2px 0; text-align: center;
  font-weight: 650; font-size: 12px;
 }
 .song .tc { color: var(--text-dim); font-variant-numeric: tabular-nums; }
 .song .len { color: var(--text-faint); font-variant-numeric: tabular-nums; font-size: 12px; }
 .pills { display: flex; gap: 6px; flex-wrap: wrap; }
 .pill { font-size: 11px; padding: 3px 9px; border-radius: 99px;
  background: var(--surface-2); border: 1px solid var(--border); color: var(--text-dim); }
 /* ── Toast ────────────────────────────────────────────────────────────── */
 .toast-host { position: fixed; bottom: 18px; left: 50%; transform: translateX(-50%);
  display: flex; flex-direction: column; gap: 8px; z-index: 50; }
 .toast {
  padding: 11px 18px; border-radius: 12px; box-shadow: var(--shadow-lg);
  background: var(--surface); border: 1px solid var(--border);
  display: flex; align-items: center; gap: 10px; font-weight: 550;
  animation: rise .3s var(--ease);
 }
 .toast.ok { border-color: color-mix(in srgb, var(--ok) 40%, var(--border)); }
 .toast.err { border-color: color-mix(in srgb, var(--danger) 40%, var(--border)); }
@keyframes rise { from { opacity: 0; transform: translateY(10px); } to { opacity: 1; transform: none; } }
 .banner {
  padding: 10px 14px; border-radius: 10px; font-size: 13px;
  background: color-mix(in srgb, var(--warn) 14%, var(--surface));
  border: 1px solid color-mix(in srgb, var(--warn) 35%, transparent);
  color: var(--text);
 }
 .hint { font-size: 12px; color: var(--text-faint); }
 .rename-preview { font-size: 12px; color: var(--accent); min-height: 16px; }
 /* Detection-engine environment status */
 .env-status { display: flex; align-items: center; gap: 9px; }
 .env-text { font-size: 13px; color: var(--text-dim); }
 .env-dot {
  width: 10px; height: 10px; border-radius: 50%; flex: none;
  background: var(--text-faint); box-shadow: 0 0 0 3px color-mix(in srgb, var(--text-faint) 18%, transparent);
 }
 .env-dot.ok { background: var(--ok); box-shadow: 0 0 0 3px color-mix(in srgb, var(--ok) 22%, transparent); }
 .env-dot.bad { background: var(--danger); box-shadow: 0 0 0 3px color-mix(in srgb, var(--danger) 22%, transparent); }
 .env-dot.busy { background: var(--accent); animation: env-pulse 1s ease-in-out infinite; }
@keyframes env-pulse {
  0%, 100% { opacity: 1; }
  50% { opacity: .35; }
 }
 /* First-visit attention pulse on the engine-test button */
 .btn.attention { animation: btn-attention 1.4s var(--ease) infinite; }
@keyframes btn-attention {
  0%, 100% { box-shadow: 0 0 0 0 color-mix(in srgb, var(--accent) 55%, transparent); }
  50% { box-shadow: 0 0 0 7px color-mix(in srgb, var(--accent) 0%, transparent); }
 }
--- a/src/ui.ts
+++ b/src/ui.ts
@@ -0,0 +1,80 @@
 // Shared UI helpers: toasts and a reusable progress panel.
 import { h, fmtElapsed, fmtDur } from "./dom";
 export function toast(message: string, kind: "ok" | "err" | "" = "") {
  const host = document.getElementById("toasts")!;
  const el = h("div", { class: `toast ${kind}` },
    h("span", {}, kind === "ok" ? "✓" : kind === "err" ? "⚠" : "•"),
    h("span", {}, message),
  );
  host.append(el);
  setTimeout(() => {
    el.style.transition = "opacity .25s, transform .25s";
    el.style.opacity = "0";
    el.style.transform = "translateY(8px)";
    setTimeout(() => el.remove(), 260);
  }, kind === "err" ? 6000 : 3200);
 }
 /** A progress bar + status/timing line + collapsible log, returned as one unit. */
 export function makeProgress() {
  const fill = h("i");
  const bar = h("div", { class: "bar" }, fill);
  const status = h("span", { class: "status" }, "Ready");
  const timing = h("span", { class: "timing" });
  const log = h("div", { class: "log" });
  const root = h("div", { class: "progress-wrap" },
    bar,
    h("div", { class: "progress-meta" }, status, timing),
    log,
  );
  return {
    root,
    reset() {
      bar.classList.remove("indeterminate");
      fill.style.width = "0%";
      status.textContent = "Ready";
      timing.textContent = "";
      log.textContent = "";
    },
    indeterminate(on: boolean, text?: string) {
      bar.classList.toggle("indeterminate", on);
      if (on) fill.style.width = "35%";
      if (text) status.textContent = text;
    },
    set(pct: number, statusText: string, opts?: { elapsed?: number; eta?: number; speed?: number }) {
      bar.classList.remove("indeterminate");
      fill.style.width = `${Math.round(pct * 100)}%`;
      status.textContent = statusText;
      if (opts) {
        const bits: string[] = [];
        if (opts.elapsed != null) bits.push(`Elapsed ${fmtElapsed(opts.elapsed)}`);
        if (opts.eta != null && opts.eta > 0) bits.push(`ETA ${fmtElapsed(opts.eta)}`);
        if (opts.speed != null && opts.speed > 0) bits.push(`${opts.speed.toFixed(1)}×`);
        timing.textContent = bits.join("  ·  ");
      }
    },
    setTiming(text: string) { timing.textContent = text; },
    appendLog(line: string) {
      log.textContent += (log.textContent ? "\n" : "") + line;
      log.scrollTop = log.scrollHeight;
    },
  };
 }
 export type Progress = ReturnType<typeof makeProgress>;
 /** Render a list of detected songs. */
 export function renderSongs(container: HTMLElement, segments: { index: number; start: number; end: number; duration: number }[]) {
  container.replaceChildren(
    ...segments.map((s) =>
      h("div", { class: "song" },
        h("div", { class: "badge" }, `#${s.index}`),
        h("div", { class: "tc" }, `${fmtDur(s.start)} → ${fmtDur(s.end)}`),
        h("div", { class: "len" }, `${Math.round(s.duration)}s`),
      ),
    ),
  );
 }
--- a/src/views/detect.ts
+++ b/src/views/detect.ts
@@ -4,10 +4,16 @@ import { h, clear, basename, fmtDur } from "../dom";
 import { makeProgress, toast, renderSongs } from "../ui";
 import {
  pickVideo, detectSongs, cancel, exportSegments, onDetectProgress, reveal,
  checkDetection, setupDetection, onDetectSetupProgress,
  type Segment,
 } from "../api";
-export interface View { el: HTMLElement; onDrop: (paths: string[]) => void; setVideo: (p: string) => void; }
+export interface View {
  el: HTMLElement;
  onDrop: (paths: string[]) => void;
  setVideo: (p: string) => void;
  onShow?: () => void;
 }
 const FPS = ["29.97", "25", "23.976", "30", "60"];
@@ -18,9 +24,81 @@ function numField(label: string, value: number, step = "1"): [HTMLElement, HTMLI
 export function buildDetect(): View {
  let running = false;
  let settingUp = false;
  let envReady = false;
  let firstShow = true;
  let segments: Segment[] = [];
  let videoPath = "";
  // ── Detection engine (Python ML) environment ────────────────────────────
  const envDot = h("span", { class: "env-dot unknown" });
  const envText = h("span", { class: "env-text" }, "Not checked yet.");
  const testBtn = h("button", { class: "btn primary" }, "🩺  Test detection engine") as HTMLButtonElement;
  const envProgress = makeProgress();
  envProgress.root.style.display = "none";
  function setEnv(state: "unknown" | "ok" | "bad" | "busy", text: string) {
    envDot.className = `env-dot ${state}`;
    envText.textContent = text;
  }
  async function runEnvCheck() {
    if (running || settingUp) return;
    testBtn.classList.remove("attention");
    testBtn.disabled = true;
    setEnv("busy", "Checking environment…");
    envProgress.root.style.display = "none";
    try {
      const st = await checkDetection();
      if (st.ready) {
        envReady = true;
        setEnv("ok", st.model
          ? "Ready ✓ — model weights cached."
          : "Ready ✓ — weights will download on first detection.");
        testBtn.textContent = "✓ Ready — re-check";
        refreshDetectBtn();
        return;
      }
      if (!st.python) {
        envReady = false;
        setEnv("bad", st.detail);
        toast(st.detail, "err");
        testBtn.textContent = "🩺  Test detection engine";
        return;
      }
      // Python is present but packages/weights are missing → install them.
      settingUp = true;
      setEnv("busy", "Setting up — installing packages & downloading model…");
      envProgress.root.style.display = "";
      envProgress.reset();
      envProgress.indeterminate(true, "Starting setup…");
      const un = await onDetectSetupProgress((p) => {
        if (p.pct >= 0) envProgress.set(p.pct, p.message);
        else envProgress.indeterminate(true, p.message);
        envProgress.appendLog(p.message);
      });
      try {
        await setupDetection();
        envProgress.indeterminate(false);
        envProgress.set(1, "Setup complete — click “Test again” to confirm.");
        setEnv("unknown", "Setup finished. Click Test again to confirm.");
        testBtn.textContent = "Test again";
        toast("Detection environment installed. Click Test again to confirm.", "ok");
      } finally {
        un();
      }
    } catch (e) {
      envReady = false;
      envProgress.indeterminate(false);
      envProgress.set(0, "Setup failed.");
      setEnv("bad", "Setup failed — see message.");
      toast(String(e), "err");
    } finally {
      settingUp = false;
      testBtn.disabled = false;
    }
  }
  const videoInput = h("input", { class: "input grow", placeholder: "Select a video…", readonly: true }) as HTMLInputElement;
  const [gapF, gapI] = numField("Merge gap (s)", 10);
  const [minF, minI] = numField("Min duration (s)", 20);
@@ -36,11 +114,15 @@ export function buildDetect(): View {
  const songsEl = h("div", { class: "songs" });
  let lastExportDir = "";
  function refreshDetectBtn() {
    detectBtn.disabled = running || settingUp || !videoPath || !envReady;
  }
  function setVideo(p: string) {
    videoPath = p;
    videoInput.value = basename(p);
    videoInput.title = p;
-    detectBtn.disabled = running || !p;
+    refreshDetectBtn();
  }
  async function detect() {
@@ -83,7 +165,7 @@ export function buildDetect(): View {
      un();
      running = false;
      stopBtn.disabled = true;
-      detectBtn.disabled = !videoPath;
+      refreshDetectBtn();
    }
  }
@@ -99,8 +181,18 @@ export function buildDetect(): View {
  revealBtn.addEventListener("click", () => lastExportDir && reveal(lastExportDir));
  detectBtn.addEventListener("click", detect);
  stopBtn.addEventListener("click", () => cancel());
  testBtn.addEventListener("click", runEnvCheck);
  const el = h("div", { class: "view" },
    h("div", { class: "card" },
      h("h2", {}, h("span", { class: "step" }, "0"), "Detection Engine"),
      h("div", { class: "row", style: "align-items:center;gap:12px" },
        h("div", { class: "env-status grow" }, envDot, envText),
        testBtn),
      h("div", { class: "hint", style: "margin-top:8px" },
        "Song detection uses a Python ML model. Run this once per computer — it checks for the required environment and installs/downloads anything missing."),
      h("div", { style: "margin-top:10px" }, envProgress.root),
    ),
    h("div", { class: "card" },
      h("h2", {}, h("span", { class: "step" }, "1"), "Source Video"),
      h("div", { class: "row" }, videoInput,
@@ -125,5 +217,31 @@ export function buildDetect(): View {
    ),
  );
-  return { el, onDrop: (paths) => { if (paths[0]) setVideo(paths[0]); }, setVideo };
+  function onShow() {
    if (!firstShow) return;
    firstShow = false;
    // Draw attention to the engine test the first time the user lands here…
    testBtn.classList.add("attention");
    // …and quietly probe in the background so an already-set-up machine
    // flips straight to "Ready ✓" without the user doing anything.
    checkDetection()
      .then((st) => {
        if (st.ready) {
          envReady = true;
          testBtn.classList.remove("attention");
          setEnv("ok", st.model
            ? "Ready ✓ — model weights cached."
            : "Ready ✓ — weights will download on first detection.");
          testBtn.textContent = "✓ Ready — re-check";
          refreshDetectBtn();
        } else if (st.python) {
          setEnv("bad", "Setup needed — click “Test detection engine”.");
        } else {
          setEnv("bad", st.detail);
        }
      })
      .catch(() => { /* leave the highlight; user can click to find out */ });
  }
  return { el, onDrop: (paths) => { if (paths[0]) setVideo(paths[0]); }, setVideo, onShow };
 }
--- a/tsconfig.json
+++ b/tsconfig.json
@@ -0,0 +1,23 @@
 {
  "compilerOptions": {
    "target": "ES2020",
    "useDefineForClassFields": true,
    "module": "ESNext",
    "lib": ["ES2020", "DOM", "DOM.Iterable"],
    "skipLibCheck": true,
    /* Bundler mode */
    "moduleResolution": "bundler",
    "allowImportingTsExtensions": true,
    "resolveJsonModule": true,
    "isolatedModules": true,
    "noEmit": true,
    /* Linting */
    "strict": true,
    "noUnusedLocals": true,
    "noUnusedParameters": true,
    "noFallthroughCasesInSwitch": true
  },
  "include": ["src"]
 }
--- a/vite.config.ts
+++ b/vite.config.ts
@@ -0,0 +1,30 @@
 import { defineConfig } from "vite";
 // @ts-expect-error process is a nodejs global
 const host = process.env.TAURI_DEV_HOST;
 // https://vite.dev/config/
 export default defineConfig(async () => ({
  // Vite options tailored for Tauri development and only applied in `tauri dev` or `tauri build`
  //
  // 1. prevent Vite from obscuring rust errors
  clearScreen: false,
  // 2. tauri expects a fixed port, fail if that port is not available
  server: {
    port: 1420,
    strictPort: true,
    host: host || false,
    hmr: host
      ? {
          protocol: "ws",
          host,
          port: 1421,
        }
      : undefined,
    watch: {
      // 3. tell Vite to ignore watching `src-tauri`
      ignored: ["**/src-tauri/**"],
    },
  },
 }));
		`@@ -0,0 +1 @@`
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