Installation

Gradio requires Python 3. Once you have Python, you can download the latest version of gradio using pip, like this:

pip install gradio

Or you may need to do pip3 install gradio if you have multiple installations of Python.

Basic Usage

Creating an interface using gradio involves just adding a few lines to your existing code. For example, here's an how to create a gradio interface using a pretrained keras model:

import gradio, 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()

Running the code above will open a new browser window with the following interface running:

 

 

Basic Parameters

Running a GradIO interface requires creating an Interface(inputs : str, outputs : str, model_type : str, model : Any) object, which takes as input arguments:
inputs – the string representing the input interface to be used, or a subclass of gradio.AbstractInput for additional customization (see below).
outputs – the string representing the output interface to be used, , or a subclass of gradio.AbstractOutput for additional customization (see below).
model_type – the string representing type of model being passed in. Supported types include keras.
model – the actual model to use for processing.

Instead of providing the string names for inputs and outputs, objects that represent input and output interfaces can be provided. For example, the code in the Basic Usage section executes identically as:

import gradio, tensorflow as tf
image_mdl = tf.keras.applications.inception_v3.InceptionV3()
inp = gradio.inputs.ImageUpload()
out = gradio.outputs.Label()
io = gradio.Interface(inputs=inp, outputs=out, model_type="keras", model=mdl)
io.launch()

This allows for customization of the interfaces, by passing in arguments to the input and output constructors. The parameters that each interface constructor accepts is described below.

Supported Interfaces

This is the list of currently supported interfaces in GradIO. All input interfaces can be paired with any output interface.

Customizing Interfaces

In practice, it is fairly typical to customize the input and output interfaces so they preprocess the inputs in way your model accepts, or postprocesses the result of your model in the appropriate way so that the output interface can display the result. For example, you may need to adapt the preprocessing of the image upload interface so that the image is resized to the correct dimensions before being fed into your model. This can be done in one of two ways: (1) instantiating gradio.Input / gradio.Output objects with custom parameters, or (2) supplying custom preprocessing/postprocessing functions.

Input/Output Objects with Custom Parameters

For small, common changes to the input and output interfaces, you can often simply change the parameters in the constructor of the input and output objects to affect the preprocessing/postprocessing. Here is an example that resizing the image to a different size before feeding it into the model, and tweaks the output interface to hide the confidence bars and show the top 5 classes rather than the default 3:

import gradio, tensorflow as tf
image_mdl = tf.keras.applications.inception_v3.InceptionV3()
inp = gradio.inputs.ImageUpload(shape=(299, 299, 3))
out = gradio.outputs.Label(num_top_classes=5, show_confidences=False)
io = gradio.Interface(inputs=inp, outputs=out, model_type="keras", model=mdl)
io.launch()

Custom Preprocessing/Postprocessing Functions

Alternatively, you can completely override the default preprocessing/postprocessing functions by supplying your own. For example, here we modify the preprocessing function of the ImageUpload interface to add some noise to the image before feeding it into the model.

import gradio, base64, numpy as np, tensorflow as tf
from io import BytesIO
from PIL import Image
image_mdl = tf.keras.applications.inception_v3.InceptionV3()

def pre(inp):
    im = gradio.preprocessing_utils.encoding_to_image(inp)
    im = gradio.preprocessing_utils.resize_and_crop(im, (299, 299))
    im = np.array(im).flatten()
    im = im * 1/127.5 - 1
    im = im + np.random.normal(0, 0.1, im.shape)  # Adding the noise
    array = im.reshape(1, 299, 299, 3)
    return array

inp = gradio.inputs.ImageUpload(preprocessing_fn=pre)
io = gradio.Interface(inputs=inp, outputs="label", model_type="keras", model=mdl)
io.launch()

Model Types

We currently support the following kinds of models:

model_type="sklearn"

This allows you to pass in scikit-learn models, and get predictions from the model. Here's a complete example of training a sklearn model and creating a gradio interface around it.

model_type="keras"

This allows you to pass in scikit-learn models, and get predictions from the model. Here's a complete example of training a sklearn model and creating a gradio interface around it.

model_type="pytorch"

This allows you to pass in scikit-learn models, and get predictions from the model. Here's a complete example of training a sklearn model and creating a gradio interface around it.

model_type="pyfunc"

This allows you to pass in scikit-learn models, and get predictions from the model. Here's a complete example of training a sklearn model and creating a gradio interface around it.

Launch Options

When launching the interface, you have the option to pass in several boolean parameters that determine how the interface is displayed. Here is an example showing all of the possible parameters:

io.launch(inbrowser=True, inline=False, validate=False, share=True)

inbrowser – the string representing the input interface to be used, or a subclass of gradio.AbstractInput for additional customization (see below).
inline – the string representing the output interface to be used, , or a subclass of gradio.AbstractOutput for additional customization (see below).
validate – the string representing type of model being passed in. Supported types include keras.
share – the actual model to use for processing.