# tsjetpiti

A dead-simple Android chat app that runs an **uncensored Qwen3.5-2B** model
fully **on-device** via an embedded [llama.cpp](https://github.com/ggml-org/llama.cpp),
wrapped in an **extremely barebones WebView UI**.

tsjet has a deliberate personality: it **always answers**, sounds confident and
plausible, and is **hilariously, purposely wrong**. Hit **new tsjet** to wipe the
conversation and start fresh.

---

## How it works

```
┌─────────────────────────────────────────┐
│ MainActivity (Kotlin)                    │
│   • full-screen WebView  ── UI ──────────┼──> assets/web/{index.html,app.js,styles.css}
│   • JS bridge "TsjetNative"              │        (the whole chat UI, ~200 lines)
│   • model download + prompt formatting   │
└──────────────┬──────────────────────────┘
               │ JNI (LlamaBridge)
               ▼
┌─────────────────────────────────────────┐
│ cpp/llama-jni.cpp  →  libtsjet.so        │
│   talks to llama.cpp C API (pinned)      │
└─────────────────────────────────────────┘
```

- **Inference:** llama.cpp compiled from source (pinned tag `b10333`) through the
  NDK. Pulled automatically at build time via CMake `FetchContent` — no submodule
  to init.
- **Model:** `Qwen3.5-2B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf` (~1.5 GB) is
  **downloaded on first launch** from Hugging Face into the app's private storage.
  It is *not* bundled in the APK. (Swap the quant in `ModelDownloader.kt`.)
- **Persona:** set via `SYSTEM_PROMPT` in `MainActivity.kt`; sampling is a little
  hot (temp 0.9) for playful answers. The model is a Qwen3 "thinking" model, so the
  prompt disables reasoning (empty `<think></think>` prefill + `/no_think`) to keep
  replies fast and punchy instead of burning the token budget on hidden thoughts.
- **Conversation:** the web layer holds the full history and sends it each turn;
  native rebuilds the ChatML prompt and clears the KV cache before every reply, so
  "new tsjet" is just: clear JS state + reset cache.

## Requirements

- **A build machine** with the Android SDK + NDK. Easiest path: open the project
  in **Android Studio** (it will offer to install the matching NDK `27.2.12479018`
  and CMake `3.22.1`).
- **A phone:** 64-bit ARM (`arm64-v8a`), Android 11+ (minSdk 30), and enough free
  RAM to hold a ~1.5 GB Q6 model (~4 GB RAM device recommended).
- Network on first launch to download the model.

## Build

```bash
./gradlew assembleDebug
```

The APK lands in `app/build/outputs/apk/debug/`. Or just Run ▶ from Android Studio.

> First build compiles llama.cpp from source, so it takes a while and needs
> network (CMake fetches the pinned llama.cpp).

## Install (sideload)

A prebuilt `app-debug.apk` is attached to every [Release](../../releases).

The debug APK is signed with the debug key, so you can sideload it directly:
copy `app-debug.apk` to the phone, enable "install unknown apps" for your file
manager, and tap it. First launch downloads the ~1.5 GB model over Wi-Fi.

## Where things live

| What | Where |
|------|-------|
| Chat UI (HTML/CSS/JS) | `app/src/main/assets/web/` |
| Android glue + model download | `app/src/main/java/monster/autisme/tsjetpiti/` |
| Native llama.cpp bridge | `app/src/main/cpp/llama-jni.cpp` |
| Pinned llama.cpp version | `app/src/main/cpp/CMakeLists.txt` (`GIT_TAG b10333`) |
| Model URL / filename | `ModelDownloader.kt` |
| Generation params (ctx, temp, tokens) | `MainActivity.kt` + `llama-jni.cpp` |

## TODO / notes

- **Branding:** launcher icon (adaptive + legacy densities) is generated from
  `piti-icon.png` via `python3 tools/gen_launcher_icons.py`; the header wordmark is
  `piti-logo.png` (copied to `assets/web/logo.png`). Source art lives at the repo root.
- The model may emit `<think>…</think>` blocks; the UI strips them from the
  display and from history (`stripThink` in `app.js`).
- Bumping the llama.cpp tag? Re-check the C API calls in `llama-jni.cpp` against
  that tag's `include/llama.h` — it uses the raw C API directly.
- **Native is always built optimized.** AGP compiles the debug variant's C/C++ at
  `-O0` by default, which makes llama.cpp ~10x too slow; `CMakeLists.txt` forces
  `Release`/`-O3` regardless of variant. Native libs are also 16 KB page-aligned.
- **Verified on-device** (Pixel 7, Android, 4 KB pages): downloads the model, loads
  it (`n_ctx=4096`, 6 threads), streams a reply, and "new tsjet" clears the chat.
  `./gradlew assembleDebug` → ~13 MB `arm64-v8a` APK (model downloads on first launch).