README.md

metadata

title: Foo
emoji: 🐢
colorFrom: indigo
colorTo: yellow
sdk: gradio
sdk_version: 3.9
app_file: app.py
pinned: false
license: mit

# Hugging Face Spaces From A Notebook

A demo of using nbdev with Hugging Face Spaces & Gradio

Hugging Face Spaces provides an easy ways to deploy a web app with python. Gradio is one of my favorite tools for building these web apps. Gradio allows you to prototype your web apps in notebooks, which allow you to iterate really fast. However, Hugging Face Spaces requires you to package your web application code as a python script named app.py. In the below tutorial, we show you how you can use nbdev to auto-generate this app directly from a notebook, so you don't have to waste time refactoring code!

1. Create a Gradio-enabled Space on Hugging Face

The first step is to create a space and select the appropriate sdk (which is Gradio in this example), per these instructions:

After you are done creating the space, clone the repo per the instructions provided in the app. In this example, I ran the command git clone https://huggingface.co/spaces/hamel/hfspace_demo.

2. Make an app with Gradio

Below, we will create a gradio app in a notebook and show you how to deploy it to Hugging Face Spaces.

First, lets specify the libraries we need, which in this case are gradio and fastcore:

#|export
import gradio as gr
from fastcore.net import urljson, HTTPError

Next, write the functions your gradio app will use. Because of nbdev, you can prototype and package your code all in one place. The special comment #|export marks which cells will be sent to a python script (more on this later). Note that there are only three cells in this notebook with the #|export directive.

#|export
def size(repo:str):
    "Returns the size in GB of a HuggingFace Dataset."
    url = f'https://huggingface.co/api/datasets/{repo}'
    try: resp = urljson(f'{url}/treesize/main')
    except HTTPError: return f'Did not find repo: {url}'
    gb = resp['size'] / 1e9
    return f'{gb:.2f} GB'

size take as an input a Hugging Face Dataset repo and returns the total size in GB of the data.

For example, we can check the size of tglcourse/CelebA-faces-cropped-128 like so:

size("tglcourse/CelebA-faces-cropped-128")

'5.49 GB'

You can construct a simple UI with the gradio.interface and then call the launch method of that interface to display a preview in a notebook. This is a great way to test your app to see if it works

#|export
iface = gr.Interface(fn=size, inputs=gr.Text(value="tglcourse/CelebA-faces-cropped-128"), outputs="text")
iface.launch(width=500)

Running on local URL:  http://127.0.0.1:7860

To create a public link, set `share=True` in `launch()`.

(<gradio.routes.App>, 'http://127.0.0.1:7860/', None)

Note how running the launch() method in a notebook runs a webserver in the background. Below, we call the close() method to close the webserver.

# this is only necessary in a notebook
iface.close()

Closing server running on port: 7860

3. Converting This Notebook Into A Gradio App

In order to host this code on Hugging Faces spaces, you will export parts of this notebook to a script named app.py. As a reminder, this is what the special #|export comment that you have seen in cells above do! You can export code from this notebook like so:

from nbdev.export import nb_export
nb_export('app.ipynb', lib_path='.', name='app')

Understanding what is generated

Notice how the contents of app.py only contains the exported cells from this notebook:

%pycat app.py

# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.

# %% auto 0
__all__ = ['iface', 'size']

# %% app.ipynb 6
import gradio as gr
from fastcore.net import urljson, HTTPError

# %% app.ipynb 7
def size(repo:str):
    "Returns the size in GB of a HuggingFace Dataset."
    url = f'https://huggingface.co/api/datasets/{repo}'
    try: resp = urljson(f'{url}/treesize/main')
    except HTTPError: return f'Did not find repo: {url}'
    gb = resp['size'] / 1e9
    return f'{gb:.2f} GB'

# %% app.ipynb 11
iface = gr.Interface(fn=size, inputs=gr.Text(value="tglcourse/CelebA-faces-cropped-128"), outputs="text")
iface.launch(width=500)

Fill out requirements.txt

You must supply a requirements.txt file so the Gradio app knows how to build your dependencies. In this example, the only dependency other than Gradio is fastcore. You don't need to specify Gradio itself as a dependency in requirements.txt, so our requirements.txt file has only one dependency:

!cat requirements.txt

fastcore

Note: you may want to add operating system dependencies in addition to python dependencies. You can do this via a packages.txt file as documented here.

4. Launch Your Gradio App

To launch your gradio app, you need to commit the changes to the Hugging Face repo:

git add -A; git commit -m "Add application files"; git push

5. Voilà! Enjoy your Gradio App

After a couple of minutes, you will see your app published! This app is published at https://huggingface.co/spaces/hamel/hfspace_demo.

Shameless Plug: nbdev

Hopefully you felt something magical while doing this example. Even though the target application required you to write a python script (app.py), you didn't have to refactor your script from a notebook! We believe that you shouldn't have to refactor your code and switch contexts even when creating python packages! If this intrigues you, check out nbdev