semvec/pypi-packages huggingface dataset:
zuba 0.0.3
A lightweight Nigerian language classifier.
Requires Python: >=3.12

# 🇳🇬 **Zuba Language Classifier**

**Zuba Language Classifier** is a lightweight Python package for classifying text into four major Nigerian languages:

* **Hausa**
* **Yoruba**
* **Igbo**
* **Broken English (Pidgin)**

The model is built using a fine-tuned **GPT-2 architecture** and loads automatically from the **HuggingFace Hub**, so you do **not** need to download or bundle any model files manually.

The package supports both single-sentence and batch predictions.

---

##  Features

* Predicts **four Nigerian languages**
*  Works with **single strings or lists**
* Auto-downloads model from **HuggingFace Hub**
* Fast inference and minimal dependencies

---

##  Installation

Install the package from **PyPI**:

```bash
pip install zuba
```

Install **PyTorch** if you do not already have it:

```bash
pip install torch
```

---

##  Model Setup

The classifier automatically downloads the trained GPT-2 model from the **HuggingFace Hub** the first time you use it.

No manual setup is required.

You may also use a custom model if your architecture differs.

---

## 🚀 Quick Start

```python
from Zuba.Prediction import Classify

Classifier = Classify()

classifier=Classify()
result=classifier.predict([
    
        "Ina jin dadin wannan rana.",
        "Mo fẹ jẹ iyan.",
        "A na m aga ahịa echi."
    
])
print(result)
```

**Output:**

```text
["hausa", "yoruba", "igbo"]
```

---

## ⚙️ How It Works

1. Your model class is loaded by the `Classify` constructor.
2. The package downloads the trained weights from HuggingFace automatically.
3. Text is tokenized with **GPT-2 tokenizer (tiktoken)**.
4. Input is encoded, padded, and truncated to the required sequence length.
5. The model performs inference and outputs one of the four language labels.

---



### **classifier.py**

Handles tokenization, preprocessing, batching, and predictions.

### **model_loader.py**

Downloads and loads the trained model weights from HuggingFace.

---

## 🔧 Configuration

**Tokenization Length**

The default maximum token length is:

```text
277
```


## 🔗 Important Constants

```python
REPO_ID = "your-username/your-repo-name"
PACKAGE_LINK = "PYPI_link"
```

---

## 📜 License

This project is licensed under the **MIT License**.

---
dsptoolbox 0.7.3
Collection of dsp algorithms to be used for analysis of audio signals
Requires Python: >=3.11
Keywords: Digital Signal Processing, Prototyping, Research, dsp

.. image:: docs/logo/logo.png
   :width: 800
   :align: center

------------------------------------------------------------------------------

.. image:: https://readthedocs.org/projects/dsptoolbox/badge/?version=latest
    :target: https://dsptoolbox.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

.. image:: https://img.shields.io/pypi/l/dsptoolbox?color=gr
    :target: https://en.wikipedia.org/wiki/MIT_License
    :alt: License

.. image:: https://img.shields.io/pypi/pyversions/dsptoolbox
    :target: https://www.python.org/downloads/release/python-3100/
    :alt: Python version

.. image:: https://img.shields.io/pypi/v/dsptoolbox?color=orange
    :target: https://pypi.org/project/dsptoolbox/
    :alt: PyPI version

Readme
======

This is a toolbox in form of a python package that contains algorithms to be used in dsp (digital signal processing) research projects.

This is kind of a "sandbox" project with many different experimental implementations across a variety of DSP-related topics. Some parts are more
thoroughly tested and validated than others, so "caution" is advised. Please feel free to reach out in case you find bugs or want
to talk about certain functionality.

It is under active development and it will take some time until it reaches a certain level of maturity. Beware that backwards compatibility is not an actual concern and significant
changes to the API might come in the future. If you find some implementations interesting or useful, please feel free to use it for your projects
and expand or change functionalities.

Getting Started
===============

Check out the `examples`_ for some basic examples of the dsptoolbox package
and refer to the `documentation`_ for the complete description of classes and functions.

Installation
============

Use pip to install dsptoolbox

.. code-block:: console

    $ pip install dsptoolbox

    # Or this for activating numba parallelization
    $ pip install "dsptoolbox[use-numba]"

(Requires Python 3.11 or higher)

In order to install the package successfully using Linux, you need to install
PortAudio manually, since installing `sounddevice`_ will not do it automatically. To do this,
run the following commands on your console:

.. code-block:: console

    $ sudo apt-get install libasound-dev libportaudio2 libsndfile1

If this does not work properly for some reason, refer to the documentation for
`sounddevice`_ or `PortAudio`_.

.. _documentation: http://dsptoolbox.readthedocs.io/
.. _examples: https://github.com/nico-franco-gomez/dsptoolbox/tree/main/examples
.. _sounddevice: https://python-sounddevice.readthedocs.io/en/0.4.5/
.. _PortAudio: http://www.portaudio.com
pymergetic-os-builder 0.0.2
Modular OS builder for PymergeticOS
Requires Python: >=3.11
Keywords: os, builder, debian, iso, live-boot

# PymergeticOS Builder

Modular Python-based OS builder for creating Debian-based live ISOs.

## Features

- **Python-based**: Flexible and maintainable
- **Modular**: Plugin system for adding components
- **Fire CLI**: Easy-to-use command-line interface
- **Namespace package**: PEP 420 compliant, allows extending `pymergetic` namespace
- **PyPI package**: Installable via `pip install pymergetic-os-builder`

## Installation

### From PyPI

```bash
pip install pymergetic-os-builder
```

### From Source

```bash
# Clone the repository
git clone https://github.com/pymergetic/os-builder.git
cd os-builder

# Install in development mode
pip install -e .
```

## Quick Start

```bash
# Build ISO (uses pymergetic_os.yaml from current directory or config path)
pymergetic-build build

# Build with custom config file
pymergetic-build build --config=/path/to/config.yaml

# Or use environment variable
export PYMERGETIC_OS_BUILD_CONFIG=/path/to/config.yaml
pymergetic-build build

# Clean build artifacts
pymergetic-build clean

# Or use Python module directly
python -m pymergetic.os.builder.cli build
```

## Configuration

Edit your configuration YAML file to customize:
- Debian release (trixie, bookworm, etc.)
- Hostname
- Packages (YAML list)
- Desktop environment
- Build directories

**Config file:**
- Must be provided via `--config` parameter or `PYMERGETIC_OS_BUILD_CONFIG` environment variable
- If not provided, uses default configuration
- No automatic file discovery

The configuration uses Pydantic for type safety and validation. See `USAGE.md` for detailed usage instructions.

## Modules

Modules are pluggable components that can be added to the build:
- `base/` - Base OS (always included)
- `provision/` - Provisioning scripts
- Future: `uv/`, `python/`, `app/`

## Architecture

- `pymergetic/` - PEP 420 namespace package
- `pymergetic.os.builder/` - OS builder subpackage
  - `pymergetic.os.builder.builder.py` - Main builder class
  - `pymergetic.os.builder.config.py` - Configuration management
  - `pymergetic.os.builder.modules/` - Plugin modules
  - `pymergetic.os.builder.cli.py` - Fire CLI interface

This is structured as a namespace package, allowing other packages to be added to the `pymergetic` namespace later (e.g., `pymergetic.core`, `pymergetic.app`).

## Development

### Building and Publishing

```bash
# Build package
make build

# Publish to PyPI
make publish

# Full release (tag, build, publish)
make release
```

See `Makefile` for all available targets.

## Documentation

See `USAGE.md` for detailed usage instructions.
structurizer-theme-creator 0.2.0
A very stupid CLI to create themes for Structurizr
Requires Python: >=3.13

# Structurizr GCP Theme CLI
A very stupid CLI tool to create a Structurizr theme starting from svg or png icons.

## Next Step
[] Refactor of the code
[] Documentation
[] Repository structure
[] Load configuration from toml file 
[] Create a way to pass some metadata as json
[] Create a command to upload to a remote bucket on GCP 
[] Create a cloud function version of the cli