Clients 1.0 Launch!

We’re excited to unveil the first major release of the Gradio clients. We’ve made it even easier to turn any Gradio application into a production endpoint thanks to the clients’ ergonomic, transparent, and portable design.

Ergonomic API 💆

Stream From a Gradio app in 5 lines

Use the submit method to get a job you can iterate over.

In python:

from gradio_client import Client

client = Client("gradio/llm_stream")

for result in client.submit("What's the best UI framework in Python?"):
    print(result)

In typescript:

import { Client } from "@gradio/client";

const client = await Client.connect("gradio/llm_stream")
const job = client.submit("/predict", {"text": "What's the best UI framework in Python?"})

for await (const msg of job) console.log(msg.data)

Use the same keyword arguments as the app

In the examples below, the upstream app has a function with parameters called `message`, `system_prompt`, and `tokens`. We can see that the client `predict` call uses the same arguments.

In python:

from gradio_client import Client

client = Client("http://127.0.0.1:7860/")
result = client.predict(
		message="Hello!!",
		system_prompt="You are helpful AI.",
		tokens=10,
		api_name="/chat"
)
print(result)

In typescript:

import { Client } from "@gradio/client";

const client = await Client.connect("http://127.0.0.1:7860/");
const result = await client.predict("/chat", { 		
		message: "Hello!!", 		
		system_prompt: "Hello!!", 		
		tokens: 10, 
});

console.log(result.data);

Better Error Messages

If something goes wrong in the upstream app, the client will raise the same exception as the app provided that `show_error=True` in the original app's `launch()` function, or it's a `gr.Error` exception.

Transparent Design 🪟

Anything you can do in the UI, you can do with the client:

🔐Authentication
🛑 Job Cancelling
ℹ️ Access Queue Position and API
📕 View the API information

Here's an example showing how to display the queue position of a pending job:

from gradio_client import Client

client = Client("gradio/diffusion_model")

job = client.submit("A cute cat")
while not job.done():
    status = job.status()
    print(f"Current in position {status.rank} out of {status.queue_size}")

Portable Design ⛺️

The client can run from pretty much any python and javascript environment (node, deno, the browser, Service Workers).
Here's an example using the client from a Flask server using gevent:

from gevent import monkey
monkey.patch_all()

from gradio_client import Client
from flask import Flask, send_file
import time

app = Flask(__name__)

imageclient = Client("gradio/diffusion_model")

@app.route("/gen")
def gen():
      result = imageclient.predict(
                "A cute cat",
                api_name="/predict"
              )
      return send_file(result)

if __name__ == "__main__":
      app.run(host="0.0.0.0", port=5000)

v1.0 Migration Guide and Breaking Changes

Python

The `serialize` argument of the `Client` class was removed and has no effect.
The `upload_files` argument of the `Client` was removed.
All filepaths must be wrapped in the `handle_file` method. For example, `caption = client.predict(handle_file('./dog.jpg'))`.
The `output_dir` argument was removed. It is not specified in the `download_files` argument.

Javascript

The client has been redesigned entirely. It was refactored from a function into a class. An instance can now be constructed by awaiting the `connect` method.

const app = await Client.connect("gradio/whisper")

The app variable has the same methods as the python class (submit, predict, view_api, duplicate).

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