Build a UI for Stable Diffusion

# Import the modules
# With this you can get information from Anchorpoint e.g. which file is selected or in which folder the command is executed.
import anchorpoint as ap
# With this you can control the attribute system of Anchorpoint
import apsync as aps
# The replicate module to control machine learning models in the cloud
import replicate
# A module for downloading images from server and the os module for file operations.
import requests, os

# Get the context of the selected file e.g. the file path
ctx = ap.Context.instance()

# The image generation function. It also downloads the image from the replicate servers.
def generate(message):
    # Save the replicate credentials (the token) as environment variable
    os.environ["REPLICATE_API_TOKEN"] = "37db125a1e12a5ba99a8b95a9b9aac75409ed4c4"
    # The global progress indicator so that the user knows that Anchorpoint is something in the background.
    progress = ap.Progress("Running Stable Diffusion", show_loading_screen=True)
    # Select the model. Here you can find an overview of models: https://replicate.com/
    model = replicate.models.get("stability-ai/stable-diffusion")
    # The image is saved on the server. You need the URL to download it.
    output_url = model.predict(prompt=message,num_inference_steps=200)
    # We need a counter for the filename later. Otherwise you will always overwrite your last image.
    count = len(os.listdir(ctx.path))
    # The file path and filename where the image will be downloaded later on
    file_path = ctx.path+"/image_"+str(count)+".png"
    # Here we connect to the server
    http_request = requests.get(output_url[0], stream = True)
    
    # You can just google that part to understand it better. It's simply that the image is downloaded in chunks and written to the file.
    if http_request.status_code == 200:
        with open(file_path, 'wb') as file:
            for chunk in http_request:
                file.write(chunk)

    # Use attributes in Anchorpoint to simply append the prompt to the file
    aps.set_attribute_text(file_path,"Prompt",message)

# The function that happens after the user clicks "Generate Texture"
def click(dialog):
    # Get the value of the dialog into a variable
    input = dialog.get_value("prompt")
    # Start the generation with stable diffusion in another thread so that the application is not blocked
    ctx.run_async(generate,input)
    # Close the dialog
    dialog.close()

# Build the dialog
def create_dialog():
    # Initialize dialog
    dialog = ap.Dialog()
    dialog.title = "Texture from Prompt"
    # Add a label and a text field. The placeholder is the grayed out text that is useful as a tip for the user
    dialog.add_text("Prompt: ").add_input(placeholder="electric sheep, neon, synthwave", var="prompt", width=400)
    # Add a button that executes a function as a callback when clicked. 
    dialog.add_button("Generate Texture",callback = click)
    # Open the dialog
    dialog.show()

# Start the application with the dialog
create_dialog()