semvec/pypi-packages huggingface dataset:
ppig 0.2.2
An AI terminal assistant
Requires Python: >=3.8,<4.0

# PPIG: A simple AI terminal assistant

Example:

* Input:

```bash
➜ ppig list only directories        

      ls -d */
The command has been copied to the clipboard

➜ 
```

Make sure your device has some sort of clipboard system such as `xclip`. If it doesn't you can install it executing: 

```bash
➜ sudo apt-get install xclip
```

## Config
`ppig` uses OpenAI API. In order to use this API you need to be signed up in OpenAI as well as an API key. ([get it here](https://platform.openai.com/account/api-keys))

Once you have your API key, you have two options:

1. Create a `ppig_cfg.yaml` file in the directory from which you will call `ppig`. Choosing this option you won't be able to use ppig outside of that foler.
2. Create the path `~/.ppig/ppig_cfg.yaml`. Choosing this option you will be able to use ppig from any folder.

In both cases, the `ppig_cfg.yaml` muste be like this:

```YAML
api_key: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
```

Replace `XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX` with your API key.
perception 0.6.8
A package for image hashing

# perception ![ci](https://github.com/thorn-oss/perception/workflows/ci/badge.svg)

`perception` provides flexible, well-documented, and comprehensively tested tooling for perceptual hashing research, development, and production use. See [the documentation](https://perception.thorn.engineering/en/latest/) for details.

## Background

`perception` was initially developed at [Thorn](https://www.thorn.org) as part of our work to eliminate child sexual abuse material from the internet. For more information on the issue, check out [our CEO's TED talk](https://www.thorn.org/blog/time-is-now-eliminate-csam/).

## Getting Started

### Installation

`pip install opencv-python perception`

### Hashing

Hashing with different functions is simple with `perception`.

```python
from perception import hashers

file1, file2 = 'test1.jpg', 'test2.jpg'
hasher = hashers.PHash()
hash1, hash2 = hasher.compute(file1), hasher.compute(file2)
distance = hasher.compute_distance(hash1, hash2)
```

### Examples

See below for end-to-end examples for common use cases for perceptual hashes.

- [Detecting child sexual abuse material](https://perception.thorn.engineering/en/latest/examples/detecting_csam.html)
- [Deduplicating media](https://perception.thorn.engineering/en/latest/examples/deduplication.html)
- [Benchmarking perceptual hashes](https://perception.thorn.engineering/en/latest/examples/benchmarking.html)

## Supported Hashing Algorithms

`perception` currently ships with:

- pHash (DCT hash) (`perception.hashers.PHash`)
- Facebook's PDQ Hash (`perception.hashers.PDQ`)
- dHash (difference hash) (`perception.hashers.DHash`)
- aHash (average hash) (`perception.hashers.AverageHash`)
- Marr-Hildreth (`perception.hashers.MarrHildreth`)
- Color Moment (`perception.hashers.ColorMoment`)
- Block Mean (`perception.hashers.BlockMean`)
- wHash (wavelet hash) (`perception.hashers.WaveletHash`)

## Contributing

To work on the project, start by doing the following.

```bash
# Install local dependencies for
# code completion, etc.
make init

# Build the Docker container to run
# tests and such.
make build
```

- Running `make init` will update this repository's dependencies and preserve the results in `Pipfile.lock`. We strongly recommend including changes to the lock alongside any other changes that may have been made.
- You can get a JupyterLab server running to experiment with using `make lab-server`.
- To do a (close to) comprehensive check before committing code, you can use `make precommit`.
- To view the documentation, use `make documentation-server`.

To implement new features, please first file an issue proposing your change for discussion.

To report problems, please file an issue with sample code, expected results, actual results, and a complete traceback.

## Alternatives

There are other packages worth checking out to see if they meet your needs for perceptual hashing. Here are some
examples.

- [dedupe](https://github.com/dedupeio/dedupe)
- [imagededup](https://idealo.github.io/imagededup/)
- [ImageHash](https://github.com/JohannesBuchner/imagehash)
- [PhotoHash](https://github.com/bunchesofdonald/photohash)
TkinterSharp 1.0.1
A library to simplify Tkinter
Keywords: python,tkinter,addon

[FR]     TkinterSharp / TKinter# (TK#) est une librairie dont le but est de simplifier la librairie Tkinter \qui permet de créer des interfaces graphiques     , d'un coté elle permet d'éviter d'écrire trop de code en compressent juste simplement le code et     d'un autre coté elle rajoute des definition avec des paramètres déjà préparer comme la defenition :     title qui permet de générer un texte avec comme paramètre : 30 de taille et en gras .