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README.md
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`Gradio` is a python library that allows you to place input and output interfaces over trained models to make it easy for you to "play around" with your model. Gradio runs entirely locally using your browser.
To get a sense of `gradio`, take a look at the python notebooks in the `examples` folder, or read on below!
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
```
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## Usage
Gradio is very easy to use with your existing code. The general way it's used is something like this:
Gradio is very easy to use with your existing code. Here is a minimum working example:
```python
import tensorflow as tf
import gradio
import tensorflow as tf
image_mdl = tf.keras.applications.inception_v3.InceptionV3()
mdl = tf.keras.models.Sequential()
# ... define and train the model as you would normally
iface = gradio.Interface(input=“sketchpad”, output=“class”, model_type=“keras”, model=mdl)
iface.launch()
io = gradio.Interface(inputs="imageupload", outputs="label", model_type="keras", model=image_mdl)
io.launch()
```
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:
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
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* Textbox
**Output interfaces**:
* Class
* Label
* Textbox
## Screenshots
Here are a few screenshots that show examples of gradio interfaces
#### MNIST Digit Recognition (Input: Sketchpad, Output: Class)
#### MNIST Digit Recognition (Input: Sketchpad, Output: Label)
```python
iface = gradio.Interface(input='sketchpad', output='class', model=model, model_type='keras')
iface = gradio.Interface(input='sketchpad', output='label', model=model, model_type='keras')
iface.launch()
```
![alt text](https://raw.githubusercontent.com/abidlabs/gradio/master/screenshots/mnist4.png)
#### Facial Emotion Detector (Input: Webcam, Output: Class)
#### Facial Emotion Detector (Input: Webcam, Output: Label)
```python
iface = gradio.Interface(input='webcam', output='class', model=model, model_type='keras')
iface = gradio.Interface(inputs='webcam', outputs='label', model=model, model_type='keras')
iface.launch()
```
![alt text](https://raw.githubusercontent.com/abidlabs/gradio/master/screenshots/webcam_happy.png)
#### Sentiment Analysis (Input: Textbox, Output: Class)
#### Sentiment Analysis (Input: Textbox, Output: Label)
```python
iface = gradio.Interface(input='textbox', output='class', model=model, model_type='keras')
iface = gradio.Interface(inputs='textbox', outputs='label', model=model, model_type='keras')
iface.launch()
```
![alt text](https://raw.githubusercontent.com/abidlabs/gradio/master/screenshots/sentiment_positive.png)
### More Documentation
More detailed and up-to-date documentation can be found on the gradio website: www.gradio.app.