semvec/pypi-packages huggingface dataset:
pymaya 0.5.1
Python client to interact with maya.tase.co.il
Requires Python: >=3.5

# pymaya intract with 
- https://maya.tase.co.il/
- https://api.tase.co.il/

pip install pymaya
example:

```
from datetime import date

from pymaya.maya import Maya


def main():
    maya = Maya()
    all_securities = maya.get_all_securities()
    print(all_securities)

    maya.get_details("739037")
    historical_prices = maya.get_price_history(security_id="739037", from_date=date(2017, 12, 31))
    for fund_object in historical_prices:
        print(fund_object)

    maya.get_details("5118393")
    historical_prices = maya.get_price_history(security_id="5118393", from_date=date(2017, 12, 31))
    for fund_object in historical_prices:
        print(fund_object)


if __name__ == "__main__":
    main()

```
voice-of-python 0.1.0
Offline multilingual voice bot with Vosk ASR and pyttsx3 TTS
Requires Python: >=3.7

# voice_of_python

Offline Multilingual Voice Bot Library for Python  
===============================================

`voice_of_python` is a Python library that provides offline multilingual speech recognition and text-to-speech (TTS) capabilities. It uses the Vosk toolkit for offline speech-to-text and pyttsx3 for offline TTS, supporting multiple languages through automatic language detection.

---

## Features

- Record audio from microphone or accept existing WAV audio files.
- Offline speech-to-text using Vosk models (no API or internet required).
- Automatic language detection with `langid`.
- Text-to-speech in detected language voice (if available locally).
- Works fully on CPU; suitable for local and privacy-sensitive applications.
- Designed for easy integration with chatbots and LLMs.

---

## Installation

Install dependencies:

``` pip install vosk langid pyttsx3 sounddevice numpy soundfile```


*Note:* Download and provide local Vosk models for supported languages separately.

---

## Usage

### 1. Initialize the library with Vosk model paths

from voice_of_python import MultiLingualVoiceBot

model_paths = {
<br>"en": "/path/to/vosk-model-en-us-0.22",<br>
"hi": "/path/to/vosk-model-small-hi-0.22"<br>
}

bot = MultiLingualVoiceBot(model_paths)

### 2. Record from microphone and get transcription

text, lang_code = bot.record_and_transcribe(record_duration=5)<br>
print(f"Detected language: {lang_code}")<br>
print(f"Recognized text: {text}")


### 3. Integrate with your LLM or AI model

Pass `text` to your backend model to generate a reply message.

reply_text = "यहाँ आपका उत्तर है"


### 4. Speak reply in detected language

bot.speak_text(reply_text, lang_code)


### 5. Alternatively, process external audio file and speak reply

audio_file = "user.wav"
reply_text = "Thank you for your question."
bot.process_audio_and_reply(audio_file, reply_text)

---

## API Reference

- **`MultiLingualVoiceBot(model_paths: dict, sample_rate: int = 16000)`**  
  Initialize with `{language_code: vosk_model_path}` mapping.

- **`record_and_transcribe(record_duration: int = 5) -> (str, str)`**  
  Record from mic and return transcription and detected language code.

- **`process_audio_and_reply(audio_input: Union[str, np.ndarray], reply_text: str)`**  
  Transcribe audio (file path or raw numpy array), detect language, speak reply text.

- **`speak_text(text: str, lang_code: str)`**  
  Speak given text in specified language voice.

---

## Contributing

Contributions welcome! Please open issues or pull requests on GitHub.

---

## Acknowledgements

- Vosk: https://alphacephei.com/vosk/  
- pyttsx3: https://pyttsx3.readthedocs.io/en/latest/  
- langid: https://github.com/saffsd/langid.py

---

## Support

Please report issues or questions on the GitHub repository.

---

*Enjoy building multilingual, offline voice-enabled applications with voice_of_python!*
simple-virtual-assistant 0.0.5
A simple voice assistant meant to be easily extensible.
Requires Python: >=3.9

# Description
`simple-virtual-assistant` is an extremely simple Python module that allows one to create their own voice assistants, using the transcription ability of `faster-whisper`, the messaging ability of a configurable LLM, and the tooling ability of `langchain`.

## Caveat

This is not meant for large projects. I primarily wrote it to use in my [frame-assist](https://github.com/atmaranto/frame-assist) package.

## Compatbility

Tested on Debian Linux and Windows 10.

## Example

[main.py](main.py) contains a fairly simple example of an Ollama-powered large language model chatbot that answers user questions.

**Requirements**:
- Ollama with the `--model` you specify already pulled (defaults to `qwen3:8b`)
- `ffmpeg` (on Windows) for voice recording
- `arecord` (on Linux) for voice recording

#### Usage

```bash
python -m venv virtual-assistant-venv
source virtual-assistant-venv/bin/activate 
python -m pip install -r requirements.txt
python main.py
```
emr-py 0.6.0
My custom library for data science, trading and ML projects
Requires Python: <3.13,>=3.11

# EMR-Py

## Documentation
[Read the docs](https://ezemriv.github.io/EMR-Py/)

---

## Current Implementation Status
- ✅ Logging utilities
- ✅ ML encoders
- ✅ Telegram trading bot
- ✅ Performance decorators
- 🚧 Time series visualization
- 📋 Planned: GCP utilities, trading indicators, backtesting tools

---

## Library Structure (Tentative)

```
src/emrpy/
├── __init__.py
├── py.typed
├── decorators.py           # General utilities (timer, memory profiling)
├── logging/               # Logging configuration utilities
│   ├── __init__.py
│   └── logger_config.py
├── data/               # data utilities
│   ├── __init__.py
│   └── data_loaders.py
├── ml/                    # Machine Learning utilities
│   ├── __init__.py
│   └── encoders.py        # Categorical encoding functions
├── visualization/         # Plotting and visualization functions
│   ├── __init__.py
│   └── timeseries.py     # Time series plotting utilities
├── timeseries/           # Time series analysis tools
│   ├── __init__.py
│   ├── features.py       # Feature engineering
│   └── analysis.py       # Time series analysis functions
├── trading/              # Trading-specific utilities
│   ├── __init__.py
│   ├── indicators.py     # Technical indicators
│   ├── backtesting.py    # Backtesting utilities
│   └── telegrambot.py    # Trading notifications via Telegram
├── gcp/                  # Google Cloud Platform utilities
│   ├── __init__.py
│   ├── bigquery.py       # BigQuery helpers
│   └── storage.py        # Cloud Storage helpers
└── finance/              # Financial data utilities
    ├── __init__.py
    └── data_processing.py # Financial data preprocessing
```
quote0 0.1.1
Python API client for Quote/0 display device
Requires Python: >=3.9
Keywords: api, display, iot, quote0

# Quote/0

Everything about SSPAI's Quote/0

> Firmware version 1.6.10

## Getting Started

1. Connect Type-C
2. Bind device in Mobile App
3. Select content to show
4. (optional) Request API key in App (and get Device ID) -> use [API](#API)

- [Update Software](https://dot.mindreset.tech/tool/update)
  - update firmware
  - reset network
  - reset device

NOTE: to test if the display is normal you can use the script [checkerboard_gray.sh](scripts/image_api_test/checkerboard_gray.sh)

## API

- [了解 API](https://dot.mindreset.tech/docs/server/template/api)
  - [图像 API](https://dot.mindreset.tech/docs/server/template/api/image_api) (296px × 152px)
  - [文本 API](https://dot.mindreset.tech/docs/server/template/api/text_api)

## Todo

Bug:

- [ ] Somehow Image API's "link" didn't work => NFC is not working

## Resources

- [Quote/0 摘录 - 少数派](https://sspai.com/create/quote0)
- [关于 Quote/0](https://dot.mindreset.tech/docs/quote_0)

### Projects

> [社区共创](https://dot.mindreset.tech/docs/server/community_co_creation)

- [stvlynn/quote0-mcp](https://github.com/stvlynn/quote0-mcp)
  - [Steven Lynn on X: "昨天在少数派爆金币买了这么一个墨水屏 Quote/0 自定义程度很高，开放了 API 于是回来后火速搓了一个 MCP server，可以把和 LLM 对话的要点总结推送到屏幕上，NFC 写入快速预览的链接 https://t.co/LHN4CTF8xa" / X](https://x.com/Stv_Lynn/status/1954423827737239664)
- [onehupo/DotClient](https://github.com/onehupo/DotClient)

---

- [MCP-Playground/docs/Environment.md at main · daviddwlee84/MCP-Playground](https://github.com/daviddwlee84/MCP-Playground/blob/main/docs/Environment.md): `direnv` + `.env` / `python-dotenv`

```bash
# direnv
brew install direnv
grep -q 'direnv hook zsh' ~/.zshrc || echo 'eval "$(direnv hook zsh)"' >> ~/.zshrc

# dotenv (not sure if this is needed, but we use it in the `.envrc`)
# pip install "python-dotenv[cli]"
uv tool install "python-dotenv[cli]"
```

```bash
$ direnv allow
direnv: loading ~/Documents/Program/Personal/Quote0/.envrc                                                                       
direnv: export +DOT_API_KEY +DOT_DEVICE_ID
```

Upload package

```bash
# Build package
uv run python -m build

# Register PyPI / TestPyPI
# https://test.pypi.org/account/register/
# Update $HOME/.pypirc with PyPI API Key config
uv run twine upload --repository testpypi dist/*
uv run twine upload dist/*
```
sleamdge 0.3.0
A Steam/XMPP gateway
Requires Python: >=3.11
Keywords: xmpp, gateway, bridge, instant messaging, chat, steam

# sleamdge

A
[feature-rich](https://slidge.im/docs/sleamdge/main/user/features.html)
[Steam](https://steamcommunity.com/) to
[XMPP](https://xmpp.org/) puppeteering
[gateway](https://xmpp.org/extensions/xep-0100.html), based on
[slidge](https://slidge.im) and
[steamio](https://steam-py.github.io/docs/latest/).

[![CI pipeline status](https://ci.codeberg.org/api/badges/14070/status.svg)](https://ci.codeberg.org/repos/14070)
[![Chat](https://conference.nicoco.fr:5281/muc_badge/slidge@conference.nicoco.fr)](https://slidge.im/xmpp-web/#/guest?join=slidge@conference.nicoco.fr)
[![PyPI package version](https://badge.fury.io/py/sleamdge.svg)](https://pypi.org/project/sleamdge/)




sleamdge lets you chat with users of Steam without leaving your favorite XMPP client.

## Quickstart

```sh
docker run codeberg.org/slidge/sleamdge \  # works with podman too
    --jid steam.example.org \  # can be whatever you want it to be
    --secret some-secret \  # must match your XMPP server config
    --home-dir /somewhere/writeable  # for data persistence
```

Use the `:latest` tag for the latest release, `:vX.X.X` for release X.X.X, and `:main`
for the bleeding edge.

If you do not like containers, other installation methods are detailed
[in the docs](https://slidge.im/docs/sleamdge/main/admin/install.html).

## Documentation

Hosted on [codeberg pages](https://slidge.im/docs/sleamdge/main/).

## Contributing

Contributions are **very** welcome, and we tried our best to make it easy
to start hacking on sleamdge. See [CONTRIBUTING.md](./CONTRIBUTING.md).