semvec/pypi-packages huggingface dataset:
minion-agent 0.0.2
Agents for menial tasks
License: MIT
Requires Python: >=3.7

<!-- Light/Dark Mode Banner Hack -->
<p align="center">
  <img src="static/minion-works-cover-light.png#gh-light-mode-only" alt="Minion Works" width="100%" />
  <img src="static/minion-works-cover-dark.png#gh-dark-mode-only" alt="Minion Works" width="100%" />
</p>

<h1 align="center"> MinionWorks – Modular browser agents that work for bananas 🍌</h1>

<p align="center">
  <em>Modular. Extensible. AI-native browser agents for modern web automation.</em>
</p>

---

## 🚀 Overview

Minion Works is a modular AI agent framework that connects to your browser and executes complex tasks autonomously. Built for developers, researchers, and curious builders.

### ✨ Features
- 🌐 Perform Google searches and scrape content
- 🤖 Use LLMs (like GPT-4) to plan actions
- 🔗 Modular architecture for plug-and-play use cases
- 🔎 DOM interaction & content extraction
- 🔄 Run workflows via Python or UI

---

## 🛠️ Installation

1. **Clone the repo**
   ```bash
   git clone https://github.com/minionworks/minions.git
   cd minions
   ```

2. **Create a virtual environment**
   ```bash
   python -m venv venv
   source venv/bin/activate  # or venv\Scripts\activate on Windows
   ```

3. **Install dependencies**
   ```bash
   pip install -r requirements.txt
   ```

4. **Configure environment**
   ```bash
   cp .env.example .env
   # Edit your .env file with OpenAI or other API keys
   ```

---

## 🧪 Quick Start

```bash
python -m src.minion_agent.browser.main
```

Or run with PYTHONPATH if you’re outside the root:

```powershell
$env:PYTHONPATH = "C:\path\to\minions"
python -m src.minion_agent.browser.main
```

---

## 🧠 Example Use Case

```python
agent = BrowserMinion(
    task="Find the top 3 ML conferences in 2025 and summarize each.",
    model="gpt-4"
)
agent.run()
```

Or check out this Notebook Example.

---

## 🧪 Testing

```bash
pytest --maxfail=1 --disable-warnings -q
```

Ensure you’re in the root folder where `tests/` lives.

---

## 🤝 Contributing

We welcome PRs, feedback, and creative ideas!
1. Fork → Branch → Commit
2. Add tests
3. Submit a Pull Request
4. Tell your friends 🚀

---

## 📖 Citation

```bibtex
@software{minion_works2025,
  author = {Sairaam, Aman, Cheena},
  title = {Minion Works: Let AI take the helm of your browser.},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/minionworks/minions}
}
```

---
whisper-live-client 0.6.3
Lightweight Python client for WhisperLive — fewer dependencies, optimized for client-only use and easy integration.
License: MIT
Requires Python: >=3.8


# whisper-live-client

A lightweight Python client for [WhisperLive](https://github.com/collabora/WhisperLive),  
designed for client-only use cases with significantly fewer dependencies than the official server library.

## Features

- Sends PCM audio to a WhisperLive WebSocket endpoint
- Real-time transcription support with simple callback interface
- Fewer dependencies – ideal for minimal environments or custom frontends
- Focused on client-side use

## Installation

```bash
pip install whisper-live-client
```

_or install from source:_

```bash
git clone https://github.com/B4dT0bi/whisper-live-client
cd whisper-live-client
pip install .
```

## Usage

```python
from whisper_live.transcription_client import TranscriptionClient

def my_segment_callback(segments):
    print(f"Received segments:{segments}")

    transcription_client = TranscriptionClient(
        host="localhost",         # or your server address
        port=9090,                # or your server port
        lang="en",                # or your desired language
        model="small",            # or your desired model
        use_vad=True,             # use voice activity detection
        pyaudio_input_device_id=1 # optional: specify input device ID for PyAudio
    )
    transcription_client.register_callback("process_segments", my_segment_callback) # register callback
    transcription_client() # start the client
```

## Dependencies

This library uses only a minimal set of runtime dependencies:

- `websockets` – WebSocket client
- `numpy` – for handling audio data

Optional (for examples):

- `pyaudio` – audio capture in demos

This is in contrast to the official WhisperLive server code, which depends on heavy libs like `torch`.

## 📜 License

MIT License

---

> **Disclaimer:** This is an independent client library and is not affiliated with Collabora or OpenAI.
randog 0.19.0
Generate data randomly
License: MIT

**randog 0.19.0 — Randomly object generator**
        
        **randog** is a package which helps to generate data randomly.
        
        ## Install
        
        You can install from PyPI.
        
        ```shell
        pip install -U randog
        ```
        
        ## Usage
        For example:
        
        ```python
        import uuid
        import randog.factory
        
        factory = randog.factory.from_example({"uuid": uuid.uuid4, "name": "", "age": 20})
        generated = factory.next()
        
        assert isinstance(generated, dict)
        assert isinstance(generated["uuid"], uuid.UUID)
        assert isinstance(generated["name"], str)
        assert isinstance(generated["age"], int)
        ```
        
        See the [documentation](https://unaguna.github.io/random-obj-generator/) for more details.
        
        
        ## Types that can be generated
        
        - int, float, str, etc.
        - [tuple](https://unaguna.github.io/random-obj-generator/en/doc.list_factory.html), [list](https://unaguna.github.io/random-obj-generator/en/doc.list_factory.html), [dict](https://unaguna.github.io/random-obj-generator/en/doc.dict_factory.html)
        - datetime, date, time, timedelta
        - [IPv4Address](https://unaguna.github.io/random-obj-generator/en/doc.ipv4_factory.html)
        - (Experimental feature) [Model instance of SQLAlchemy](https://unaguna.github.io/random-obj-generator/en/doc.sqlalchemy.html)