xai-gradio

is a Python package that makes it very easy for developers to create machine learning apps that are powered by XAI API.

Installation

You can install xai-gradio directly using pip:

pip install xai-gradio

That's it!

Basic Usage

You'll need to save your xAI API key to the appropriate environment variable:

export XAI_API_KEY=<your token>

Then in a Python file, write:

import gradio as gr
import xai_gradio

gr.load(
    name='grok-beta',
    src=xai_gradio.registry,
).launch()

Run the Python file, and you should see a Gradio Interface connected to your chosen model!

Customization

Once you can create a Gradio UI from an OpenAI endpoint, you can customize it by setting your own input and output components, or any other arguments to gr.Interface. For example, the screenshot below was generated with:

import gradio as gr
import xai_gradio

gr.load(
    name='grok-beta',
    src=xai_gradio.registry,
    title='X.AI-Gradio Integration',
    description="Chat with grok-beta model.",
    examples=["Explain quantum gravity to a 5-year old.", "How many R are there in the word Strawberry?"]
).launch()

Composition

Or use your loaded Interface within larger Gradio Web UIs, e.g.

import gradio as gr
import xai_gradio

with gr.Blocks() as demo:
    with gr.Tab("grok-beta"):
        gr.load('grok-beta', src=xai_gradio.registry)

demo.launch()

Under the Hood

The xai-gradio Python library has two dependencies: openai and gradio. It defines a "registry" function xai_gradio.registry, which takes in a model name and returns a Gradio app.

Supported Models and Providers

The following AI models are currently supported:

• xAI (Grok-beta)

For a comprehensive list of available models and their specifications, please refer to:

• xAI Models

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Note: if you are getting authentication errors, ensure you have set the correct environment variable. You can also set it in your Python session:

import os
os.environ["XAI_API_KEY"] = ...