semvec/pypi-packages huggingface dataset:
androidtvremote2 0.3.0
A Python library for interacting with Android TV using the Android TV Remote protocol v2
Requires Python: >=3.10

# androidtvremote2

A Python library for interacting with Android TV using the Android TV Remote protocol v2. This is the same protocol the Google TV mobile app is using. It doesn't require ADB or enabling developer tools on the Android TV device. It only requires the [Android TV Remote Service](https://play.google.com/store/apps/details?id=com.google.android.tv.remote.service) that comes pre-installed on most Android TV devices.

For a list of the most common commands you can send to the Android TV see: [TvKeys](https://github.com/tronikos/androidtvremote2/blob/main/TvKeys.txt).
For a full list see [remotemessage.proto](https://github.com/tronikos/androidtvremote2/blob/b4c49ac03043b1b9c40c2f2960e466d5a3b8bd67/src/androidtvremote2/remotemessage.proto#L90).
In addition to commands you can send URLs to open apps registered to handle them. See [this guide](https://community.home-assistant.io/t/android-tv-remote-app-links-deep-linking-guide/567921) for how to find deep links for apps.

Voice commands can also be sent as PCM 16-bit mono 8 kHz audio data.

## Credits

- Official [implementation](https://android.googlesource.com/platform/external/google-tv-pairing-protocol/+/refs/heads/master) of the pairing protocol in Java
- [Implementation](https://github.com/farshid616/Android-TV-Remote-Controller-Python) in Python but for the old v1 protocol
- [Implementation](https://github.com/louis49/androidtv-remote) in Node JS for the v2 protocol
- [Description](https://github.com/Aymkdn/assistant-freebox-cloud/wiki/Google-TV-(aka-Android-TV)-Remote-Control-(v2)) of the v2 protocol

## Example

See [demo.py](https://github.com/tronikos/androidtvremote2/blob/main/src/demo.py)

## Development environment

```sh
python3 -m venv .venv
source .venv/bin/activate
# for Windows CMD:
# .venv\Scripts\activate.bat
# for Windows PowerShell:
# .venv\Scripts\Activate.ps1

# Install dependencies
python -m pip install --upgrade pip
python -m pip install -e .

# Generate *_pb2.py from *.proto
python -m pip install grpcio-tools mypy-protobuf
python -m grpc_tools.protoc src/androidtvremote2/*.proto --python_out=src/androidtvremote2 --mypy_out=src/androidtvremote2 -Isrc/androidtvremote2

# Run pre-commit
python -m pip install pre-commit
pre-commit install
pre-commit run --all-files

# Run tests
python -m pip install -e ".[test]"
pytest

# Run demo
python -m pip install -e ".[demo]"
python src/demo.py

# Build package
python -m pip install build
python -m build
```

The voice demo requires the [PyAudio](https://pypi.org/project/PyAudio/) library.
Depending on the target platform, [PortAudio](https://www.portaudio.com/) might have to be installed manually,
see [PyAudio installation](https://people.csail.mit.edu/hubert/pyaudio/) for more information.
ryzenai 0.0.1
Efficient inference library (ONNX) with pluggable backends (CPU / DirectML).
Requires Python: >=3.9
Keywords: onnx, inference, deep-learning, machine-learning, directml

ryzenai-lite
==============

ryzenai-lite is a small, minimal ONNX-based inference helper library that
provides a clean public API to load ONNX models and run inference on CPU or
GPU (DirectML/CUDA/ROCm when available). The library is intentionally
minimal to make it easy to plug into small projects and demos.

Quick start
-----------

1. Inspect model inputs:

```powershell
python example/inspect_model.py
```

2. Run a small SLM generation (greedy) with GPU (DirectML) preferred and CPU fallback:

```powershell
python example/slm_run.py --model example/models/bert-tiny.onnx --prompt "Once upon a time" --max-new-tokens 16 --device dml
```

3. GPU test (verify DirectML usage):

```powershell
python example/gpu_simple_test.py
```

Public API
----------

- `load_model(model_path: str, device_preference: Optional[List[str]] = None) -> Model`
- `Model` class with methods: `warmup`, `run`, `run_once`, `dispose`
- `device_info()` -> dict of available providers and platform
- `benchmark_model(...)` helper

See `DOCUMENTATION.md` for full details, examples and troubleshooting tips.

License
-------
MIT

Generated on: 2025-11-01