Metadata-Version: 2.1
Name: TrollFactory
Version: 2.1.4
Summary: An amazingly accurate fake personality generator.
Home-page: UNKNOWN
Author: Stanisławowski Research & Development
Author-email: <office@stanislawowski.pl>
License: UNKNOWN
Keywords: fake,personality,personalities,generator
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Other Audience
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Utilities
Description-Content-Type: text/markdown
License-File: LICENSE

# TrollFactory

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**Fake personality generator for the 21st century!**

## About
TrollFactory is a fairly advanced fake personality generator written in Python. It aims to generate as much data about a person as possible. At the moment it only supports Polish personalities, we're finishing US dataset soon.

## Installation
TrollFactory is available as a PyPI package: `pip3 install trollfactory`

## Usage

### TrollFactory CLI
The TrollFactory PyPI package also contains TrollFactory CLI. It's currently the most reliable way of using TrollFactory.<br>
To use it, run the `trollfactory` binary from your terminal emulator.

If ran without any parameters, it will generate a personality with default options (Polish dataset, female gender).

At the moment, you can specify a custom dataset and gender (support for other static properties is coming soon):<br>
`trollfactory --gender male --dataset english_us`<br>

You can also generate many personalities at once:<br>
`trollfactory --amount 10`

You can run `trollfactory --help` to read the full help.

### Python library
After installing the TrollFactory Python package, you can use it in your scripts.
```python
import trollfactory.functions as tf
tf.generate_personality('polish', 'male')
```
The `generate_personality()` function returns a dict with generated data.

## TODO
-   [x] non-binary gender
-   [ ] finish english_us dataset
-   [ ] tests for english_us dataset
-   [ ] setting static properties
-   [x] cli arguments help
-   [x] add more test scripts
-   [x] improve CC prop & write test
-   [x] type hints for generated props
-   [x] docstrings
-   [ ] split datasets into separate modules


