gradio/README.md

# Gradio
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`Gradio` is a python library that allows you to easily create input and output interfaces over trained models to make it easy for you to "play around" with your model in your browser by dragging-and-dropping in your own images (or pasting your own text, recording your own voice, etc.) and seeing what the model outputs. Gradio also creates a shareable, public link to your model so you can share the interface with others (e.g. your client, your advisor, or your dad), who can use the model without writing any code. 

Gradio is useful for:
* Creating demos for clients
* Getting feedback from collaborators
* Debugging your model during development

For more details, see the accompanying paper: ["Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild"](https://arxiv.org/pdf/1906.02569.pdf), *ICML HILL 2019*, and please use the citation below.

```
@article{abid2019gradio,
title={Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild},
author={Abid, Abubakar and Abdalla, Ali and Abid, Ali and Khan, Dawood and Alfozan, Abdulrahman and Zou, James},
journal={arXiv preprint arXiv:1906.02569},
year={2019}
}
```

To get a sense of `gradio`, take a look at the  python notebooks in the `examples` folder, or read on below! And be sure to visit the gradio website: www.gradio.app.

## Installation
```
pip install gradio
```
(you may need to replace `pip` with `pip3` if you're running `python3`).

## Usage

Gradio is very easy to use with your existing code. Here is a minimum working example:


```python
import gradio
import tensorflow as tf
image_mdl = tf.keras.applications.inception_v3.InceptionV3()

io = gradio.Interface(inputs="imageupload", outputs="label", model_type="keras", model=image_mdl)
io.launch()
```

You can supply your own model instead of the pretrained model above, as well as use different kinds of models, not just keras models. Changing the `input` and `output` parameters in the `Interface` face object allow you to create different interfaces, depending on the needs of your model. Take a look at the python notebooks for more examples. The currently supported interfaces are as follows:

**Input interfaces**:
* Sketchpad
* ImageUplaod
* Webcam
* Textbox

**Output interfaces**:
* Label
* Textbox

## Screenshots

Here are a few screenshots that show examples of gradio interfaces

#### MNIST Digit Recognition (Input: Sketchpad, Output: Label)

```python
iface = gradio.Interface(input='sketchpad', output='label', model=model, model_type='keras')
iface.launch()
```

![alt text](https://raw.githubusercontent.com/abidlabs/gradio/master/screenshots/sketchpad_interface.png)

#### Image Classifier: InceptionNet (Input: Webcam, Output: Label)

```python
iface = gradio.Interface(inputs='webcam', outputs='label', model=model, model_type='keras')
iface.launch()
```

![alt text](https://raw.githubusercontent.com/abidlabs/gradio/master/screenshots/image_interface.png)

#### Human DNA Variant Effect Prediction (Input: Textbox, Output: Label)

```python
iface = gradio.Interface(inputs='textbox', outputs='label', model=model, model_type='keras')
iface.launch()
```

![alt text](https://raw.githubusercontent.com/abidlabs/gradio/master/screenshots/label_interface.png)

### More Documentation

More detailed and up-to-date documentation can be found on the gradio website: www.gradio.app.