Advanced Interface features

Now that we can build and share a basic interface, let’s explore some more advanced features such as state, and interpretation.

Using state to persist data

Gradio supports session state, where data persists across multiple submits within a page load. Session state is useful for building demos of, for example, chatbots where you want to persist data as the user interacts with the model. Note that session state does not share data between different users of your model.

To store data in a session state, you need to do three things:

1. Pass in an extra parameter into your function, which represents the state of the interface.
2. At the end of the function, return the updated value of the state as an extra return value.
3. Add the ‘state’ input and ‘state’ output components when creating your Interface.

See the chatbot example below:

import random

import gradio as gr


def chat(message, history):
    history = history or []
    if message.startswith("How many"):
        response = random.randint(1, 10)
    elif message.startswith("How"):
        response = random.choice(["Great", "Good", "Okay", "Bad"])
    elif message.startswith("Where"):
        response = random.choice(["Here", "There", "Somewhere"])
    else:
        response = "I don't know"
    history.append((message, response))
    return history, history


iface = gr.Interface(
    chat,
    ["text", "state"],
    ["chatbot", "state"],
    allow_screenshot=False,
    allow_flagging="never",
)
iface.launch()

Notice how the state of the output component persists across submits. Note: you can pass in a default value to the state parameter, which is used as the initial value of the state.

Using interpretation to understand predictions

Most machine learning models are black boxes and the internal logic of the function is hidden from the end user. To encourage transparency, we’ve made it very easy to add interpretation to your model by simply setting the interpretation keyword in the Interface class to default. This allows your users to understand what parts of the input are responsible for the output. Take a look at the simple interface below which shows an image classifier that also includes interpretation:

import requests
import tensorflow as tf

import gradio as gr

inception_net = tf.keras.applications.MobileNetV2()  # 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, 224, 224, 3))
    inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
    prediction = inception_net.predict(inp).flatten()
    return {labels[i]: float(prediction[i]) for i in range(1000)}


image = gr.Image(shape=(224, 224))
label = gr.Label(num_top_classes=3)

title = "Gradio Image Classifiction + Interpretation Example"
gr.Interface(
    fn=classify_image, inputs=image, outputs=label, interpretation="default", title=title
).launch()

Test the interpretation function by submitting an input then clicking Interpret under the output component.

Besides the default interpretation method Gradio provides, you can also specify shap for the interpretation parameter and set the num_shap parameter. This uses Shapley-based interpretation, which you can read more about here. Lastly, you can also pass in your own interpretation function into the interpretation parameter. See an example in Gradio’s getting started page here.

This wraps up our deep dive into the Interface class of Gradio. As we’ve seen, this class makes it simple to create machine learning demos in a few lines of Python code. However, sometimes you’ll want to customise your demo by changing the layout or chaining multiple prediction functions together. Wouldn’t it be nice if we could somehow split the Interface into customizable “blocks”? Fortunately, there is! That’s the topic of the final section.