Sets up the Python gradio client

client/python/README.md

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+# `gradio_client`: Use any Gradio app as an API -- in 3 lines of Python
+
+This directory contains the source code for `gradio_client`, a lightweight Python library that makes it very easy to use any Gradio app as an API. This library is CURRENTLY IN ALPHA. APIS MAY CHANGE. WE DO NOT RECOMMEND USING IT EXCEPT IN EXPERIMENTAL PROJECTS. 
+
+As an example, consider the Stable Diffusion Gradio app, which is hosted on Hugging Face Spaces, and which generates images given a text prompt. Using the `gradio_client` library, we can easily use the Gradio as an API to generates images programmatically.
+
+Here's the entire code to do it:
+
+```python
+import gradio_client as grc
+
+client = grc.Client(space="stability-ai/stable-diffusion")
+job = client.predict("a hyperrealistic portrait of a cat wearing cyberpunk armor")
+job.result()
+
+>> URL
+
+```
+
+## Installation
+
+If you already have a recent version of `gradio`, then the `gradio_client` is included as a dependency. 
+
+Otherwise, the lightweight `gradio_client` package can be installed from pip (or pip3) and works with Python versions 3.7 or higher:
+
+```bash
+$ 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**
+
+```python
+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:
+
+```python
+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:
+
+
+```python
+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:
+
+
+```python
+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:
+
+
+```python
+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", callbacks=[print_result])
+```

client/python/gradio_client/__init__.py

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+from gradio_client.client import Client
+from gradio_client.utils import __version__