semvec/pypi-packages huggingface dataset:
pystashlib 0.6.0
A python library for manipulating a stash database
License: MIT

A python library for querying and updating a stash sqlite database

pystashlib and Stash compatibility table:
| pystashlib | Stash version | Stash schema |
| ------------- | ------------- | ------------- |
| v0.6.0 | v0.27.0 | 67 |
| v0.5.0 | v0.25.0-v0.25.1 | 55 |
| v0.4.0-v0.4.5 | v0.24.0-v0.24.3 | 54 |
| v0.3.0-v0.3.1 | v0.17.0-v0.17.2 | 36 |
| v0.2.8 | v0.15.0-v0.16.1 | 31 |
| v0.2.7 | v0.14.0 | 30 |
| v0.2.5 | v0.12.0-v0.13.1 | 29 |
| v0.2.4 | v0.11.0 | 28 |

# Changelog

## v0.5.0
* Update to support Stash v0.25.0

## v0.4.5
* Add `requests` package to list of requirements

## v0.4.4
* Updated StashInterface GQL client to support Stash v0.24.3

## v0.4.3
*  Added support for performer name disambiguation

### Changed
* `query_performer_name` Returns list[PerformersRow] instead of PerformersRow.
* `create_performer_from_url` Added disambiguation argument

   `def create_performer_from_url(self, name, disambiguation, url, commit=True):`

### Added
* `query_performer_name_disambiguated` Returns a PerformersRow match on name and disambiguation.

   Pass None or "" to get a PerformersRow with no disambiguation.

## v0.4.2
*  Added back tree_from_file and scrape functions to new stashlib.html module with lxml as an optional dependency

## v0.4.1
* Fix image blob saving

## v0.4.0
* Update to support Stash v0.24.3

## v0.3.1
* Removed tree_from_file and scrape functions from stashlib.common to remove dependency on lxml
truelink 1.4.8
Extract direct download links from various URL formats
License: MIT
Requires Python: >=3.9
Keywords: direct-link, download, extractor, link, url

# TrueLink

[![PyPI version](https://img.shields.io/pypi/v/truelink.svg)](https://pypi.org/project/truelink/)
[![Downloads](https://static.pepy.tech/badge/truelink/month)](https://pepy.tech/project/truelink)

A Python library for resolving media URLs to direct download links from various file hosting services.

## Features

- **Asynchronous**: Built with `async/await` for efficient handling of multiple requests.
- **Easy to use**: Simple API with intuitive method names.
- **Extensible**: Support for multiple file hosting platforms.
- **Caching**: Built-in caching for faster resolution of repeated requests.
- **Error handling**: Robust error handling for various edge cases.
- **URL validation**: Built-in URL validation before processing.
- **Type-hinted**: Fully type-hinted codebase for better readability and maintainability.

## Installation

```bash
pip install truelink
```

## Quick Start

```python
import asyncio
from truelink import TrueLinkResolver

async def main():
    # Check if a URL is supported without creating an instance
    if TrueLinkResolver.is_supported("https://buzzheavier.com/rnk4ut0lci9y"):
        print("BuzzHeavier is supported!")

    resolver = TrueLinkResolver()
    url = "https://buzzheavier.com/rnk4ut0lci9y"

    try:
        result = await resolver.resolve(url)
        print(type(result))
        print(result)
    except Exception as e:
        print(f"Error processing {url}: {e}")

asyncio.run(main())
```

### Documentation

For more information, see the [documentation](https://5hojib.github.io/truelink/).

### Community

- [Contributing](docs/contributing.md)
- [Code of Conduct](CODE_OF_CONDUCT.md)
- [Changelog](docs/changelog.md)
LightGBMModelOptimizer 0.0.7
Trained Lightgbm Model Optimizer. Reduce size and improve inference time of the trained lightgbm model
License: MIT
Requires Python: >=3.6

LightGBMModelOptimizer Python-package
=======================

Usage
*****
LightGBMModelOptimizer optimized the trained lightgbm model. It reduces size of model & improves the inference time of the model.

To Optimize the trained model without dumping. It will create a new model object and return it.

    optimizer = Optimizer()
    model = optimizer.optimize_booster(model)

To Optimize the model dump. It will replace the same file with the optimized model file.

    optimizer = Optimizer()
    _ = optimizer.optimize_model_file('model.txt')