Metadata-Version: 2.1
Name: tai_chi_engine
Version: 0.0.5
Summary: UNKNOWN
Home-page: https://github.com/unpackai/tai-chi
License: GPLv3+
Project-URL: Documentation, https://github/unpackai/tai-chi/
Project-URL: Tracker, https://github.com/unpackAI/tai-chi/issues
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: End Users/Desktop
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

# Tai-Chi Engine
> Powerful deep learning for civilians

> ```太极引擎```  深度学习： 强大、多模态、灵活、平民化

## Essence of the Tai-Chi Engine
* Close to state-of-the-art Deep learning, friendly to office folks, all coding-free, clicks away.
* Flexible, supporting multiple kinds of data (image, text, category, multi-category, etc), multiple x at the same time.
* Which columns to be x? Which column to be y? You can decide and play, see if the AI finds out how to guess.

## Our big pitch
* If you're a **coding muggle** - play with the engine, you'll understand ideas around Deep Learning, and have good model.
* If you're a **pro** - it's still wildly fun to try new models in 2 minutes' click, especially you have around a dozen columns.

## Playing Tai-Chi
First, tell me you are already in a jupyter notebook environment.

Open up a table, a pandas dataframe, from excel or from csv file or from SQL database, dosn't matter.
```python
import pandas as pd
df = pd.read_csv('your_data.csv')
```

Then, you can use the following code to play with the engine.
```python
from tai_chi_engine import TaiChiEngine
# load the engine
engine = TaiChiEngine(df, project="./where/to_save/your_model")
# start the playing
engine()
```

Good to go!

## Installation
```bash
pip install tai-chi-engine
```

## Links
* The github repository is [here](https://github.com/unpackAI/tai-chi)

