toolhost 0.2.1

Web apps in pure Python with all the flexibility and speed of nextjs.

⚙️ Installation

Open a terminal and run (Requires Python 3.7+):

pip install nextpy

🥳 Create your first app

Installing nextpy also installs the nextpy command line tool.

Test that the install was successful by creating a new project. (Replace my_app_name with your project name):

mkdir my_app_name
cd my_app_name
nextpy init

This command initializes a boilerplate app in your new directory.

You can run this app in development mode:

nextpy run

You should see your app running at http://localhost:3000.

Now you can modify the source code in my_app_name/my_app_name.py. Nextpy has fast refreshes so you can see your changes instantly when you save your code.

🫧 Example App

Let's go over an example: creating an image generation UI around DALL·E. For simplicity, we just call the OpenAI API, but you could replace this with an ML model run locally.

 

 

Here is the complete code to create this. This is all done in one Python file!

import nextpy as xt
from openai import OpenAI
client = OpenAI()


class State(xt.State):
    """The app state."""
    prompt = ""
    image_url = ""
    processing = False
    complete = False

    def get_image(self):
        """Get the image from the prompt."""
        if self.prompt == "":
            return xt.window_alert("Prompt Empty")

        self.processing, self.complete = True, False
        yield
        response = client.images.generate(
            model="dall-e-3",
            prompt="a white siamese cat",
            size="1024x1024",
            quality="standard",
            n=1,
            )
        self.image_url = response.data[0].url
        self.processing, self.complete = False, True
        

def index():
    return xt.center(
        xt.vstack(
            xt.heading("DALL·E"),
            xt.input(placeholder="Enter a prompt", on_blur=State.set_prompt),
            xt.button(
                "Generate Image",
                on_click=State.get_image,
                is_loading=State.processing,
                width="100%",
            ),
            xt.cond(
                State.complete,
                     xt.image(
                         src=State.image_url,
                         height="25em",
                         width="25em",
                    )
            ),
            padding="2em",
            shadow="lg",
            border_radius="lg",
        ),
        width="100%",
        height="100vh",
    )

# Add state and page to the app.
app = xt.App()
app.add_page(index, title="nextpy:DALL·E")
app.compile()

Let's break this down.

Nextpy UI

Now let's explore the main components of the DALL·E image generation app.

The index Function

def index():
    return xt.center(
        ...
    )

This index function serves as the main entry point for defining the structure and content of the app's front-end user interface. This function utilizes Nextpy's declarative UI components to specify what the app should look like. When the app runs, whatever is returned by the index function is rendered on the screen.

We use different components such as center, vstack, input, and button to build the frontend. Components can be nested within each other to create complex layouts. And you can use keyword args to style them with the full power of CSS.

State

Nextpy represents your UI as a function of your state.

class State(xt.State):
    """The app state."""
    prompt = ""
    image_url = ""
    processing = False
    complete = False

The State class defines the reactive variables, or "vars," that store the application's data and state. In this example, we have:

• prompt: A string to capture user input for image generation.
• image_url: A string to store the URL of the generated image.
• processing: A boolean indicating whether the image generation is in progress.
• complete: A boolean that turns true when image processing is finished.

Here the state is comprised of a prompt and image_url. There are also the booleans processing and complete to indicate when to show the circular progress and image.

State

class State(xt.State):
    prompt = ""
    image_url = ""
    processing = False
    complete = False

The State class defines the reactive variables, or "vars," that store the application's data and state. In this example, we have:

• prompt: A string to capture user input for image generation.
• image_url: A string to store the URL of the generated image.
• processing: A boolean indicating whether the image generation is in progress.
• complete: A boolean that turns true when image processing is finished.

Furthermore, the State class also contains event handlers that react to user-expressed actions, altering the state as required. The get_image event handler initiates image generation when a user submits a prompt, using the yield statement to refresh the UI whenever needed.

def get_image(self):
    if self.prompt == "":
        return xt.window_alert("Prompt Empty")
    self.processing, self.complete = True, False
    yield
    response = client.images.generate(...)
    self.image_url = ...  # Extract image URL from response
    self.processing, self.complete = False, True

App Configuration and Routing

app = xt.App()
app.add_page(index, title="nextpy:DALL·E")
app.compile()

The app's configuration is established with an instance of xt.App which encapsulates the entire Nextpy application. The index function is added as the root page, and the app.compile() call prepares the app for execution.

This breakdown demonstrates how to structure a Nextpy application, implement state management, define event handlers, and construct a responsive UI, culminating in a deployable web application.