gradio/README.md

Gradio

At Gradio, we often try to understand what inputs a model is particularly sensitive to. To help facilitate this, we've developed and open-sourced gradio, a python library that allows you to quickly 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 can also generate a share link which allows anyone, anywhere to use the interface as the model continues to run on your machine.

Gradio is useful for:

• Creating demos of your machine learning code for clients / collaborators / users
• Getting feedback on model performance from users
• Debugging your model interactively during development

To get a sense of gradio, take a look at a few of these examples, and find more on our 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 are a few working examples:

0. Hello World

Let's start with a basic function (no machine learning yet!) that greets an input name. We'll wrap the function with a Text to Text interface.

import gradio as gr

def greet(name):
  return "Hello " + name + "!"

gr.Interface(fn=greet, inputs="text", outputs="text").launch()

The core Interface class is initialized with three parameters:

• fn: the function to wrap
• inputs: the name of the input interface
• outputs: the name of the output interface

Calling the launch() function of the Interface object produces the interface shown in image below. Click on the gif to go the live interface in our getting started page.

1. Inception Net

Now, let's do a machine learning example. We're going to wrap an interface around the InceptionV3 image classifier, which we'll load using Tensorflow! Since this is an image classification model, we will use the Image input interface. We'll output a dictionary of labels and their corresponding confidence scores with the Label output interface. (The original Inception Net architecture can be found here)

import gradio as gr
import tensorflow as tf
import numpy as np
import requests

inception_net = tf.keras.applications.InceptionV3() # load the model

# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")

def classify_image(inp):
  inp = inp.reshape((-1, 299, 299, 3))
  inp = tf.keras.applications.inception_v3.preprocess_input(inp)
  prediction = inception_net.predict(inp).flatten()
  return {labels[i]: float(prediction[i]) for i in range(1000)}

image = gr.inputs.Image(shape=(299, 299, 3))
label = gr.outputs.Label(num_top_classes=3)

gr.Interface(fn=classify_image, inputs=image, outputs=label).launch()

This code will produce the interface below. The interface gives you a way to test Inception Net by dragging and dropping images, and also allows you to use naturally modify the input image using image editing tools that appear when you click EDIT. Notice here we provided actual gradio.inputs and gradio.outputs objects to the Interface function instead of using string shortcuts. This lets us use built-in preprocessing (e.g. image resizing) and postprocessing (e.g. choosing the number of labels to display) provided by these interfaces.

You can supply your own model instead of the pretrained model above, as well as use different kinds of models or functions. Here's a list of the interfaces we currently support, along with their preprocessing / postprocessing parameters:

Input Interfaces:

• Sketchpad(shape=(28, 28), invert_colors=True, flatten=False, scale=1/255, shift=0, dtype='float64')
• Webcam(image_width=224, image_height=224, num_channels=3, label=None)
• Textbox(lines=1, placeholder=None, label=None, numeric=False)
• Radio(choices, label=None)
• Dropdown(choices, label=None)
• CheckboxGroup(choices, label=None)
• Slider(minimum=0, maximum=100, default=None, label=None)
• Image(shape=(224, 224, 3), image_mode='RGB', scale=1/127.5, shift=-1, label=None)
• Microphone()

Output Interfaces:

• Label(num_top_classes=None, label=None)
• KeyValues(label=None)
• Textbox(lines=1, placeholder=None, label=None)
• Image(label=None, plot=False)

Interfaces can also be combined together, for multiple-input or multiple-output models.

2. Real-Time MNIST

Let's wrap a fun Sketchpad-to-Label UI around MNIST. For this example, we'll take advantage of the live feature in the library. Set live=True inside Interface()> to have it run continuous predictions. We've abstracted the model training from the code below, but you can see the full code on the colab link.

import tensorflow as tf
import gradio as gr
from urllib.request import urlretrieve

urlretrieve("https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5","mnist-model.h5")
model = tf.keras.models.load_model("mnist-model.h5")

def recognize_digit(inp):
    prediction = model.predict(inp.reshape(1, 28, 28, 1)).tolist()[0]
    return {str(i): prediction[i] for i in range(10)}

sketchpad = gr.inputs.Sketchpad()
label = gr.outputs.Label(num_top_classes=3)

gr.Interface(fn=recognize_digit, inputs=sketchpad,
  outputs=label, live=True).launch()

This code will produce the interface below.

Contributing:

If you would like to contribute and your contribution is small, you can directly open a pull request (PR). If you would like to contribute a larger feature, we recommend first creating an issue with a proposed design for discussion. Please see our contributing guidelines for more info.

License:

Gradio is licensed under the Apache License 2.0

See more:

You can find many more examples (like GPT-2, model comparison, multiple inputs, and numerical interfaces) as well as more info on usage on our website: www.gradio.app

See, also, the accompanying paper: "Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild", 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}
}