Introducing Gradio Clients


Related Spaces:

Using Hugging Face Integrations


The Hugging Face Hub is a central platform that has hundreds of thousands of models, datasets and demos (also known as Spaces).

Gradio has multiple features that make it extremely easy to leverage existing models and Spaces on the Hub. This guide walks through these features.

Demos with the Hugging Face Inference Endpoints

Hugging Face has a service called Serverless Inference Endpoints, which allows you to send HTTP requests to models on the Hub. The API includes a generous free tier, and you can switch to dedicated Inference Endpoints when you want to use it in production. Gradio integrates directly with Serverless Inference Endpoints so that you can create a demo simply by specifying a model's name (e.g. Helsinki-NLP/opus-mt-en-es), like this:

import gradio as gr

demo = gr.load("Helsinki-NLP/opus-mt-en-es", src="models")


For any Hugging Face model supported in Inference Endpoints, Gradio automatically infers the expected input and output and make the underlying server calls, so you don't have to worry about defining the prediction function.

Notice that we just put specify the model name and state that the src should be models (Hugging Face's Model Hub). There is no need to install any dependencies (except gradio) since you are not loading the model on your computer.

You might notice that the first inference takes a little bit longer. This happens since the Inference Endpoints is loading the model in the server. You get some benefits afterward:

  • The inference will be much faster.
  • The server caches your requests.
  • You get built-in automatic scaling.

Hosting your Gradio demos on Spaces

Hugging Face Spaces allows anyone to host their Gradio demos freely, and uploading your Gradio demos take a couple of minutes. You can head to, select the Gradio SDK, create an file, and voila! You have a demo you can share with anyone else. To learn more, read this guide how to host on Hugging Face Spaces using the website.

Alternatively, you can create a Space programmatically, making use of the huggingface_hub client library library. Here's an example:

from huggingface_hub import (
create_repo(name=target_space_name, token=hf_token, repo_type="space", space_sdk="gradio")
repo_name = get_full_repo_name(model_id=target_space_name, token=hf_token)
file_url = upload_file(

Here, create_repo creates a gradio repo with the target name under a specific account using that account's Write Token. repo_name gets the full repo name of the related repo. Finally upload_file uploads a file inside the repo with the name

Loading demos from Spaces

You can also use and remix existing Gradio demos on Hugging Face Spaces. For example, you could take two existing Gradio demos on Spaces and put them as separate tabs and create a new demo. You can run this new demo locally, or upload it to Spaces, allowing endless possibilities to remix and create new demos!

Here's an example that does exactly that:

import gradio as gr

with gr.Blocks() as demo:
  with gr.Tab("Translate to Spanish"):
    gr.load("gradio/en2es", src="spaces")
  with gr.Tab("Translate to French"):
    gr.load("abidlabs/en2fr", src="spaces")


Notice that we use gr.load(), the same method we used to load models using Inference Endpoints. However, here we specify that the src is spaces (Hugging Face Spaces).

Note: loading a Space in this way may result in slight differences from the original Space. In particular, any attributes that apply to the entire Blocks, such as the theme or custom CSS/JS, will not be loaded. You can copy these properties from the Space you are loading into your own Blocks object.

Demos with the Pipeline in transformers

Hugging Face's popular transformers library has a very easy-to-use abstraction, pipeline() that handles most of the complex code to offer a simple API for common tasks. By specifying the task and an (optional) model, you can build a demo around an existing model with few lines of Python:

import gradio as gr

from transformers import pipeline

pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")

def predict(text):
  return pipe(text)[0]["translation_text"]

demo = gr.Interface(


But gradio actually makes it even easier to convert a pipeline to a demo, simply by using the gradio.Interface.from_pipeline methods, which skips the need to specify the input and output components:

from transformers import pipeline
import gradio as gr

pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")

demo = gr.Interface.from_pipeline(pipe)

The previous code produces the following interface, which you can try right here in your browser:


That's it! Let's recap the various ways Gradio and Hugging Face work together:

  1. You can build a demo around Inference Endpoints without having to load the model, by using gr.load().
  2. You host your Gradio demo on Hugging Face Spaces, either using the GUI or entirely in Python.
  3. You can load demos from Hugging Face Spaces to remix and create new Gradio demos using gr.load().
  4. You can convert a transformers pipeline into a Gradio demo using from_pipeline().