Gradiome / Gradio

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!

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. The general way it's used is something like this:

import tensorflow as tf
import gradio

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()

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:

Class
Textbox

Screenshots

Here are a few screenshots that show examples of gradio interfaces

MNIST Digit Recognition (Input: Sketchpad, Output: Class)

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

Facial Emotion Detector (Input: Webcam, Output: Class)

iface = gradio.Interface(input='webcam', output='class', model=model, model_type='keras')
iface.launch()

Sentiment Analysis (Input: Textbox, Output: Class)

iface = gradio.Interface(input='textbox', output='class', model=model, model_type='keras')
iface.launch()

2.1 KiB Raw Blame History