interface/app.py

import gradio as gr
from .model_loader import Model
from PIL import Image
import cv2
import io
from huggingface_hub import snapshot_download

models_path = snapshot_download(repo_id="radames/UserControllableLT", repo_type="model")


# models fron pretrained/latent_transformer folder
models_files = {
    "anime": "anime.pt",
    "car": "car.pt",
    "cat": "cat.pt",
    "church": "church.pt",
    "ffhq": "ffhq.pt",
}

models = {name: Model(models_path + "/" + path) for name, path in models_files.items()}


def cv_to_pil(img):
    return Image.fromarray(cv2.cvtColor(img.astype("uint8"), cv2.COLOR_BGR2RGB))


def random_sample(model_name: str):
    model = models[model_name]
    img, latents = model.random_sample()
    pil_img = cv_to_pil(img)
    return pil_img, model_name, latents


def zoom(model_state, latents_state, dx=0, dy=0, dz=0, sxsy=[128, 128]):
    model = models[model_state]
    dx = dx
    dy = dy
    dz = dz
    sx = sxsy[0]
    sy = sxsy[1]
    stop_points = []
    img, latents_state = model.zoom(
        latents_state, dz, sxsy=[sx, sy], stop_points=stop_points
    )  # dz, sxsy=[sx, sy], stop_points=stop_points)
    pil_img = cv_to_pil(img)
    return pil_img, latents_state


def translate(model_state, latents_state, dx=0, dy=0, dz=0, sxsy=[128, 128]):
    model = models[model_state]

    dx = dx
    dy = dy
    dz = dz
    sx = sxsy[0]
    sy = sxsy[1]
    stop_points = []
    zi = False
    zo = False

    img, latents_state = model.translate(
        latents_state,
        [dx, dy],
        sxsy=[sx, sy],
        stop_points=stop_points,
        zoom_in=zi,
        zoom_out=zo,
    )

    pil_img = cv_to_pil(img)
    return pil_img, latents_state


def change_style(image: Image.Image, model_state, latents_state):
    model = models[model_state]
    img, latents_state = model.change_style(latents_state)
    pil_img = cv_to_pil(img)
    return pil_img, latents_state


def reset(model_state, latents_state):
    model = models[model_state]
    img, latents_state = model.reset(latents_state)
    pil_img = cv_to_pil(img)
    return pil_img, latents_state


def image_click(evt: gr.SelectData):
    click_pos = evt.index
    return click_pos


with gr.Blocks() as block:
    model_state = gr.State(value="cat")
    latents_state = gr.State({})
    sxsy = gr.State([128, 128])
    gr.Markdown("# UserControllableLT: User controllable latent transformer")
    gr.Markdown("## Select model")
    with gr.Row():
        with gr.Column():
            model_name = gr.Dropdown(
                choices=list(models_files.keys()),
                label="Select Pretrained Model",
                value="cat",
            )
            with gr.Row():
                button = gr.Button("Random sample")
                reset_btn = gr.Button("Reset")

            dx = gr.Slider(
                minimum=-256, maximum=256, step_size=0.1, label="dx", value=0.0
            )
            dy = gr.Slider(
                minimum=-256, maximum=256, step_size=0.1, label="dy", value=0.0
            )
            dz = gr.Slider(
                minimum=-256, maximum=256, step_size=0.1, label="dz", value=0.0
            )

            with gr.Row():
                change_style_bt = gr.Button("Change style")

        with gr.Column():
            image = gr.Image(type="pil", label="")
    image.select(image_click, inputs=None, outputs=sxsy)
    button.click(
        random_sample, inputs=[model_name], outputs=[image, model_state, latents_state]
    )
    reset_btn.click(
        reset,
        inputs=[model_state, latents_state],
        outputs=[image, latents_state],
    )

    change_style_bt.click(
        change_style,
        inputs=[image, model_state, latents_state],
        outputs=[image, latents_state],
    )
    dx.change(
        translate,
        inputs=[model_state, latents_state, dx, dy, dz, sxsy],
        outputs=[image, latents_state],
        show_progress=False,
    )
    dy.change(
        translate,
        inputs=[model_state, latents_state, dx, dy, dz, sxsy],
        outputs=[image, latents_state],
        show_progress=False,
    )
    dz.change(
        zoom,
        inputs=[model_state, latents_state, dx, dy, dz, sxsy],
        outputs=[image, latents_state],
        show_progress=False,
    )

block.queue()
block.launch()