Sets up the Python gradio client

[abidlabs]

Creates a lightweight Python library called gradio_client

Documentation:

---

gradio_client: Use any Gradio app as an API -- in 3 lines of Python

Installation

The lightweight gradio_client package can be installed from pip (or pip3) and works with Python versions 3.7 or higher:

$ pip install gradio_client

Usage

Connecting to a Space or a Gradio app

Start by connecting instantiating a Client object and connecting it to a Gradio app
that is running on Spaces (or anywhere else)!

Connecting to a Space

import gradio_client as grc

client = grc.Client(space="abidlabs/en2fr")

Connecting a general Gradio app

If your app is running somewhere else, provide the full URL instead to the src argument. Here's an example of making predictions to a Gradio app that is running on a share URL:

import gradio_client as grc

client = grc.Client(src="btd372-js72hd.gradio.app")

Making a prediction

The simplest way to make a prediction is simply to call the .predict() function with the appropriate arguments and then immediately calling .result(), like this:

import gradio_client as grc

client = grc.Client(space="abidlabs/en2fr")

client.predict("Hello").result()

>> Bonjour

Running jobs asyncronously

Oe should note that .result() is a blocking operation as it waits for the operation to complete before returning the prediction.

In many cases, you may be better off letting the job run asynchronously and waiting to call .result() when you need the results of the prediction. For example:

import gradio_client as grc

client = grc.Client(space="abidlabs/en2fr")

job = client.predict("Hello")

# Do something else

job.result()

>> Bonjour

Adding callbacks

Alternatively, one can add callbacks to perform actions after the job has completed running, like this:

import gradio_client as grc

def print_result(x):
    print(x"The translated result is: {x}")

client = grc.Client(space="abidlabs/en2fr")

job = client.predict("Hello", result_callbacks=[print_result])

Closes: #2516

[gradio-pr-bot]

All the demos for this PR have been deployed at https://huggingface.co/spaces/gradio-pr-deploys/pr-3300-all-demos

[pngwn]

I've created #3310 and renamed #2516 so we can close them separately.

[abidlabs]

Made some changes to this PR:

• Reconciled with main
• Documented gr.load(). I kept the documentation for gr.Blocks.load() and gr.Interface.load() but marked them as methods that will be deprecated:

• Added telemetry using the huggingface_hub library

Put remaining TODOs here: #3565

[aliabid94]

lgtm!

[freddyaboulton]

Super awesome PR @abidlabs !

Left some comments that can be handled in future PRs

[abidlabs]

Thanks for the review @freddyaboulton and @aliabid94! Will address these comments

[abidlabs]

Ok I think that should be everything! Will merge in after CI passes