semvec/pypi-packages huggingface dataset:
snipster-tui 0.5.3
snipster-tui is a terminal user interface (TUI) application for managing and browsing code snippets in a local sqlite database.
Requires Python: >=3.13

# Snipster-TUI

A Programming snippets TUI

This was created while doing th PyBites Developer Cohort

## Description

Goal of this is to learn the tooling for a real world App and while doing so to advance my overall knowledge of Python.

## Getting Started

### Installing

```bash
git clone https://github.com/Razhiel82/snipster-tui.git
cd snipster-tui
uv sync
```

### Executing program

* How to run the program
* Step-by-step bullets

```bash
uv run src/snipster_tui/tui.py
```

## Functionallity

This are the Main functions of snipster

```bash
![Add Snippets](add.png)
![List Snippets](list.png)
![Edit Snippets](edit.png)
![delete](delete.png)
F = Favorite
```

## Authors

Contributors names and contact info

ex. Frederick
ex. [LinkedIn](https://www.linkedin.com/in/frederick-ming-b8b517197/)

## Version History

* v0.5.2
  * Correction of Readme

* v0.5.0
  * Package cleanup

* v0.4.0
  * changes the tui:
    * Added Bindungs for favorit, edit and delete
    * It opens modal now
    * lot of debugging since the functions changed a lot

* v0.3.0
  * added snippet edit-feature
  * added testing

* v0.2.0

  * Initial alpha release
  * Basic Usage adding and deleting for snippets

## Disclaimer

This is a personal project run at own risk! The DB is still subject to change.

## License

This project is licensed under the [NAME HERE] License - see the LICENSE.md file for details

## Acknowledgments

Inspiration, code snippets, etc.

* [PyBites](https://pybit.es/)
cmShoppingList 0.21
Shopping List plugin app for Alliance Auth.
Requires Python: >=3.8

# Shopping List

This is a Shopping List app for [Alliance Auth](https://gitlab.com/allianceauth/allianceauth) (AA) 

## Features

- Paste in your Eve/FleetUp fit (with optional multiplier) and then paste in your eve list of assets.  It will run a comparison and let you know what still needs to be purchased.  Now includes equivalence checks

## Installation

Once you have cloned the repo or copied all files into place.

Make sure you are in your venv. Then install it with pip from PyPi or pip in editable mode if downloaded from source:

```bash
pip install cmShoppingList 
or
pip install -e {path_to}/cmShoppingList

```

First add your app to the Django project by adding 'cmShoppingList' to INSTALLED_APPS in `settings/local.py`.

Next perform migrations to add models to the database:

```bash
python manage.py migrate
```

Next initalize all the market types and equivalences

```bash
python manage.py populate_types
```

Next copy all the static data to the output folder

```bash
python manage.py collectstatic --noinput
```

Finally restart your AA setup.

## Permissions

There is only one permission for this application and it's to allow access and show it in the menu

View Shopping List menu item
    *cmShoppingList.basic_access*
eth-hash 0.8.0b1
eth-hash: The Ethereum hashing function, keccak256, sometimes (erroneously) called sha3
Requires Python: <4,>=3.10
Keywords: ethereum

# eth-hash

[![Join the conversation on Discord](https://img.shields.io/discord/809793915578089484?color=blue&label=chat&logo=discord&logoColor=white)](https://discord.gg/GHryRvPB84)
[![Build Status](https://circleci.com/gh/ethereum/eth-hash.svg?style=shield)](https://circleci.com/gh/ethereum/eth-hash)
[![PyPI version](https://badge.fury.io/py/eth-hash.svg)](https://badge.fury.io/py/eth-hash)
[![Python versions](https://img.shields.io/pypi/pyversions/eth-hash.svg)](https://pypi.python.org/pypi/eth-hash)
[![Docs build](https://readthedocs.org/projects/eth-hash/badge/?version=latest)](https://eth-hash.readthedocs.io/en/latest/?badge=latest)

The Ethereum hashing function, keccak256, sometimes (erroneously) called sha3

Note: the similarly named [pyethash](https://github.com/ethereum/ethash)
has a completely different use: it generates proofs of work.

This is a low-level library, intended to be used internally by other Ethereum tools.
If you're looking for a convenient hashing tool, check out
[`eth_utils.keccak()`](https://eth-utils.readthedocs.io/en/stable/utilities.html#keccak-bytes-int-bool-text-str-hexstr-str-bytes)
which will be a little friendlier, and provide access to other helpful utilities.

Read the [documentation](https://eth-hash.readthedocs.io/).

[View the change log](https://eth-hash.readthedocs.io/en/latest/release_notes.html).

## Installation

```sh
python -m pip install "eth-hash[pycryptodome]"
```

```py
>>> from eth_hash.auto import keccak
>>> keccak(b'')
b"\xc5\xd2F\x01\x86\xf7#<\x92~}\xb2\xdc\xc7\x03\xc0\xe5\x00\xb6S\xca\x82';{\xfa\xd8\x04]\x85\xa4p"
```

See the [docs](http://eth-hash.readthedocs.io/en/latest/quickstart.html#quickstart)
for more about choosing and installing backends.
vgf-adapter-model-explorer 0.4.1
Adapter for Model Explorer to support VGF files
Requires Python: <3.14,>=3.10
Keywords: model-explorer, vgf, visualization

# VGF Adapter for Model Explorer

VGF Adapter for [google-ai-edge/model-explorer](https://github.com/google-ai-edge/model-explorer) that enables visualization of VGF files from the [Arm ML SDK for Vulkan®](https://github.com/arm/ai-ml-sdk-for-vulkan).

![](https://raw.githubusercontent.com/arm/vgf-adapter-model-explorer/main/screenshots/vgf-adapter-readme-screenshot.png)

## Requirements

- Python >=3.10, <3.14

## Supported Platforms

- Linux   x86_64
- Linux   AArch64
- MacOS   AArch64
- Windows x86_64


## Installation

### pip + PyPI
    pip install vgf-adapter-model-explorer

### GitHub

    PYTHON_VERSION_TAG=311 &&
    gh release download \
    --repo arm/vgf-adapter-model-explorer \
    --pattern "*py${PYTHON_VERSION_TAG}*.whl" &&
    pip install *py${PYTHON_VERSION_TAG}*.whl

Or through the [GitHub Releases](https://github.com/arm/vgf-adapter-model-explorer/releases) UI.

## Usage

Install Model Explorer:

    pip install torch ai-edge-model-explorer

Launch Model Explorer with the VGF adapter enabled:

    model-explorer --extensions=vgf_adapter_model_explorer

See the [Model Explorer wiki](https://github.com/google-ai-edge/model-explorer/wiki) for more information.

## Trademark notice
Arm® is a registered trademark of Arm Limited (or its subsidiaries) in the US and/or elsewhere.

Khronos® and Vulkan® are registered trademarks, and SPIR-V™ is a trademark of the Khronos Group Inc.

## Contributions

We are not accepting direct contributions at this time.
If you have any feedback or feature requests, please use the repository issues section.
mdpres 0.1.0
Markdown to HTML Presentation Generator
Requires Python: >=3.11
Keywords: html, markdown, mermaid, presentation, slides

# mdpres

Markdown to HTML presentation generator.

## Installation

```bash
# Clone the repository
git clone <repo-url>
cd html-presentation

# Install dependencies
uv sync
```

## Quick Start

```bash
# Create an example presentation
uv run mdpres example

# Build HTML
uv run mdpres build example.md

# Open in browser
open example.html
```

## Usage

### Commands

```bash
# Build presentation
uv run mdpres build slides.md

# With output file and theme
uv run mdpres build slides.md -o presentation.html --theme nord

# Create example file
uv run mdpres example

# List available themes
uv run mdpres themes
```

### Markdown Format

Slides are separated by horizontal rule `---`:

```markdown
---
title: Presentation Title
author: Author Name
---

# Title

First slide (title slide)

---

## Second Slide

- Item 1
- Item 2
- Item 3

---

## Code

```python
def hello(name):
    return f"Hello, {name}!"
```

---

## Diagram

```mermaid
graph LR
    A[Input] --> B[Process]
    B --> C[Output]
```
```

### Supported Elements

| Element | Syntax |
|---------|--------|
| Headers | `# H1`, `## H2`, `### H3` |
| Bold | `**text**` or `__text__` |
| Italic | `*text*` or `_text_` |
| Code | `` `code` `` |
| Links | `[text](url)` |
| Images | `![alt](url)` |
| Lists | `- item` or `1. item` |
| Blockquotes | `> quote` |
| Code blocks | ` ```language ... ``` ` |
| Mermaid | ` ```mermaid ... ``` ` |

### Slide Directives

Add HTML comments to customize slides:

```markdown
<!-- class: centered special -->
<!-- type: custom -->

# Slide with classes
```

## Themes

| Theme | Description |
|-------|-------------|
| `dark` | Dark theme (default) |
| `light` | Light theme |
| `nord` | Nordic color palette |
| `dracula` | Popular dark theme |

```bash
uv run mdpres build slides.md --theme dracula
```

## Presentation Navigation

| Key | Action |
|-----|--------|
| `Right` / `Space` / `PageDown` | Next slide |
| `Left` / `PageUp` | Previous slide |
| `Home` | First slide |
| `End` | Last slide |
| `f` | Toggle fullscreen |

On mobile devices use swipe gestures.

## Development

```bash
# Install dev dependencies
uv sync

# Run tests
uv run pytest

# Linter
uv run ruff check .

# Format code
uv run ruff format .

# Type checking
uv run pyrefly check
```

## License

MIT
hw2-latex-generator 0.1.0
Простой функциональный генератор LaTeX для таблиц и картинок
Requires Python: >=3.10

# HW 2 — functional LaTeX generator

Minimal, dependency-free helpers to generate LaTeX for tables and images, plus a ready-made example that produces `artifacts/table_and_image.tex` using the committed `artifacts/sample.png`.

## What’s included
- `latex_gen.py` — pure functions for table/image blocks and full document rendering.
- `create_example.py` — builds the example `.tex` using the provided `sample.png`.
- `artifacts/` — generated `.tex` and the sample PNG image.
- `Makefile` — convenience targets `example` (generate `.tex`) and `pdf` (requires `pdflatex`).
- `Dockerfile` and `docker-compose.yml` — containerized build of `.tex` and `.pdf`.

## Run locally
```bash
cd hw_2
python3 create_example.py          # writes artifacts/table_and_image.tex and sample.png
# optional if LaTeX is installed:
make pdf                           # pdflatex -> artifacts/table_and_image.pdf
```

## Docker
Build image once:
```bash
cd hw_2
docker build -t hw2-latex .
```

Generate pdf using the built image (no rebuild needed):
```bash
cd hw_2
docker run --rm -v "$PWD/artifacts:/app/artifacts" hw2-latex
# or make docker-run (after make docker-build once)
```

Or via docker-compose (builds first time, then reuses):
```bash
cd hw_2
docker-compose up --build
```
Artifacts land in `artifacts/` on the host.
kytchen-sdk 1.0.0
Python SDK for Kytchen - BYOLLM Context Orchestration
Requires Python: >=3.9
Keywords: ai, context, llm, ml, orchestration

# Kytchen Python SDK

Python client for [Kytchen](https://github.com/Shannon-Labs/Kytchen) - BYOLLM Context Orchestration.

## Installation

```bash
pip install kytchen
```

## Quick Start

```python
import asyncio
from kytchen_sdk import KytchenClient, Budget

async def main():
    # Initialize client
    client = KytchenClient(
        api_key="kyt_sk_...",
        base_url="http://localhost:8000",  # For self-hosted
    )

    # Upload a dataset
    dataset = await client.datasets.create("my-data", "document.txt")

    # Query with your own LLM key (BYOLLM)
    result = await client.query(
        query="What is the main topic?",
        dataset_ids=[dataset.id],
        provider="anthropic",
        provider_api_key="sk-ant-...",  # Your Anthropic key
        budget=Budget(max_iterations=10, max_cost_usd=0.50),
    )

    print(result.answer)

    await client.close()

asyncio.run(main())
```

## Features

- **Async-first**: Built on `httpx` for efficient async HTTP
- **BYOLLM**: Bring your own LLM API key (Anthropic, OpenAI)
- **Streaming**: Real-time query progress via SSE
- **Self-host ready**: Works with Docker self-hosted instances
- **Type-safe**: Full type hints and dataclasses

## API Reference

### KytchenClient

```python
client = KytchenClient(
    api_key="kyt_sk_...",
    base_url="https://api.kytchen.dev",  # Optional
    timeout=60.0,  # Optional
)
```

### Datasets

```python
# Upload
dataset = await client.datasets.create("name", "path/to/file.txt")

# List
datasets = await client.datasets.list()

# Get
dataset = await client.datasets.get("dataset-id")

# Delete
await client.datasets.delete("dataset-id")
```

### Query

```python
# Standard query
result = await client.query(
    query="Your question",
    dataset_ids=["id1", "id2"],
    budget=Budget(max_cost_usd=0.50),
    provider="anthropic",
    provider_api_key="your-llm-key",
)

# Streaming query
async for event in client.query_stream(...):
    print(event.type, event.data)
```

## Self-Hosting

For self-hosted Kytchen instances:

```python
client = KytchenClient(
    api_key="kyt_sk_selfhost_dev_key_12345",
    base_url="http://localhost:8000",
)
```

See the [Docker self-host guide](../docker-compose.yml) for setup instructions.

## License

MIT