Metadata-Version: 2.1
Name: neuralartstudio
Version: 0.1.0
Summary: App for experimenting and creating your own art using neural style transfer
Home-page: https://github.com/soumendra/neuralartstudio
License: MIT
Author: Soumendra Dhanee
Author-email: soumendra@gmail.com
Maintainer: Soumendra Dhanee
Maintainer-email: soumendra@gmail.com
Requires-Python: >=3.7,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Dist: matplotlib (>=3.2.1,<4.0.0)
Requires-Dist: numpy (>=1.18.5,<2.0.0)
Requires-Dist: pandas (>=1.0.4,<2.0.0)
Requires-Dist: plotly (>=4.8.1,<5.0.0)
Requires-Dist: streamlit (>=0.61.0,<0.62.0)
Requires-Dist: torch (>=1.5.0,<2.0.0)
Requires-Dist: torchvision (>=0.6.0,<0.7.0)
Project-URL: Documentation, https://github.com/soumendra/neuralartstudio
Project-URL: Repository, https://github.com/soumendra/neuralartstudio
Description-Content-Type: text/markdown

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# Getting started

Latest docs at https://neuralartstudio.readthedocs.io/en/latest/

Install `neuralartstudio`

```bash
pip install neuralartstudio
```

- It is possible to create meaningful output with a cpu.
- A gpu can speed up the experiments.

## Running as a streamlit app

Create a `streamlit` app (say in a file called `main.py`)

```python
from neuralartstudio.streamlit import neural_style_transfer

neural_style_transfer(
    contentpath="assets/dancing.jpg", stylepath="assets/picasso.jpg",
)
```

**Note**: You have to provide paths to your content and style image to start with. You can replace the content image later in the app.

That's it. Now run the app with streamlit

```bash
streamlit run main.py
```

## Running from a python program

**ToDo**

## Running in a Jupyter notebook

**ToDo**

