semvec/pypi-packages huggingface dataset:
app-name-generator 0.1.1
Generate available app names

# App name generator

[![pypi icon](https://pypi.org/static/images/logo-small.6eef541e.svg) View on pypy](https://pypi.org/project/app-name-generator)

## Installation

### For end user
```
pip install app-name-generator
```

### For development

* Clone the project 
```
git clone https://github.com/sylvanld/app-name-generator.git
cd app-name-generator
```

* Install dev dependencies

Using pipenv

```
pipenv install --dev
```

Using pip

```
pip install requirements/development
```

## Example usage

Generate 5 randoms domains, available for either .io, .com, or .fr TLD with a max length of 6 characters.
```bash
appname generate 5 --max-length 6 --tlds io,com,fr
```

Output lists of domains on which names are available.

```python
['stics.io']
['slewer.io', 'slewer.fr']
['nbaci.io', 'nbaci.com', 'nbaci.fr']
['affixe.io', 'affixe.fr']
```

## Documentation

```
appname generate -h
```

```
usage: appname generate [-h] [--min-length MIN_LENGTH] [--max-length MAX_LENGTH] [--tlds TLDS] [--exclusive] number

positional arguments:
  number                number names to generate

optional arguments:
  -h, --help            show this help message and exit
  --min-length MIN_LENGTH
                        minimum length of names to generates
  --max-length MAX_LENGTH
                        maximum length of names to generates
  --tlds TLDS           comma separated list of TLDs without spaces (e.g: com,fr)
  --exclusive           Decide wether domain should be available for all TLDs
```
textdetection 0.2.1
A blackbox detection on text against adversarial attacks
Requires Python: >=3.4

## Adversarial examples detection on text (BlackBox Attacks)

[![PyPI version](https://badge.fury.io/py/textdetection.svg)](https://badge.fury.io/py/textdetection)
[![GitHub license](https://img.shields.io/github/license/threemmm/text-blackbox-detection)](https://github.com/threemmm/text-blackbox-detection/blob/master/LICENSE)

This repo tries to detect black box attacks on text models.
 It doesn't need any models, but a list of strings that 
 an attacker uses to find a perturbed versions 
 of sentences that fool the target model. It uses fuzzywuzzy package to 
 calculate similarity between different sentences.
 

### Requirements
The main requirements are:
- Python 3
- fuzzywuzzy