app.py

from os import getenv
from pathlib import Path

import gradio as gr
from PIL import Image
from rich.traceback import install as traceback_install

from tagger.common import Heatmap, ImageLabels, LabelData, load_labels_hf, preprocess_image
from tagger.model import load_model_and_transform, process_heatmap

TITLE = "WD Tagger Heatmap For More Models"
DESCRIPTION = """WD Tagger v3 Heatmap Generator."""
# get HF token
HF_TOKEN = getenv("HF_TOKEN", None)

# model repo and cache
AVAILABLE_MODEL_REPOS = [
    'SmilingWolf/wd-convnext-tagger-v3',
    'SmilingWolf/wd-swinv2-tagger-v3',
    'SmilingWolf/wd-vit-tagger-v3',
    'SmilingWolf/wd-vit-large-tagger-v3',
    "SmilingWolf/wd-eva02-large-tagger-v3",
]
MODEL_REPO = "SmilingWolf/wd-vit-tagger-v3"
# get the repo root (or the current working directory if running in ipython)
WORK_DIR = Path(__file__).parent.resolve() if "__file__" in globals() else Path().resolve()
# allowed extensions
IMAGE_EXTENSIONS = [".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp", ".tiff", ".tif"]

_ = traceback_install(show_locals=True, locals_max_length=0)

# get the example images
example_images = sorted(
    [
        str(x.relative_to(WORK_DIR))
        for x in WORK_DIR.joinpath("examples").iterdir()
        if x.is_file() and x.suffix.lower() in IMAGE_EXTENSIONS
    ]
)


def predict(
    image: Image.Image,
    model_repo: str,
    threshold: float = 0.5,
):
    # join variant for cache key
    model, transform = load_model_and_transform(model_repo)
    # load labels
    labels: LabelData = load_labels_hf(model_repo)
    # preprocess image
    image = preprocess_image(image, (448, 448))
    image = transform(image).unsqueeze(0)

    # get the model output
    heatmaps: list[Heatmap]
    image_labels: ImageLabels
    heatmaps, heatmap_grid, image_labels = process_heatmap(model, image, labels, threshold)

    heatmap_images = [(x.image, x.label) for x in heatmaps]

    return (
        heatmap_images,
        heatmap_grid,
        image_labels.caption,
        image_labels.booru,
        image_labels.rating,
        image_labels.character,
        image_labels.general,
    )


css = """
#use_mcut, #char_mcut {
    padding-top: var(--scale-3);
}
#threshold.dimmed {
    filter: brightness(75%);
}
"""

with gr.Blocks(theme="NoCrypt/miku", analytics_enabled=False, title=TITLE, css=css) as demo:
    with gr.Row(equal_height=False):
        with gr.Column(min_width=720):
            with gr.Group():
                img_input = gr.Image(
                    label="Input",
                    type="pil",
                    image_mode="RGB",
                    sources=["upload", "clipboard"],
                )
            with gr.Group():
                with gr.Row():
                    threshold = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=0.35,
                        step=0.01,
                        label="Tag Threshold",
                        scale=5,
                        elem_id="threshold",
                    )
                    model_to_use = gr.Dropdown(
                        choices=AVAILABLE_MODEL_REPOS,
                        value=MODEL_REPO,
                    )
            with gr.Row():
                clear = gr.ClearButton(
                    components=[],
                    variant="secondary",
                    size="lg",
                )
                submit = gr.Button(value="Submit", variant="primary", size="lg")

        with gr.Column(min_width=720):
            with gr.Tab(label="Heatmaps"):
                heatmap_gallery = gr.Gallery(columns=3, show_label=False)
            with gr.Tab(label="Grid"):
                heatmap_grid = gr.Image(show_label=False)
            with gr.Tab(label="Tags"):
                with gr.Group():
                    caption = gr.Textbox(label="Caption", show_copy_button=True)
                    tags = gr.Textbox(label="Tags", show_copy_button=True)
                with gr.Group():
                    rating = gr.Label(label="Rating")
                with gr.Group():
                    character = gr.Label(label="Character")
                with gr.Group():
                    general = gr.Label(label="General")

    with gr.Row():
        examples = [[imgpath, MODEL_REPO, 0.35] for imgpath in example_images]
        examples = gr.Examples(
            examples=examples,
            inputs=[img_input, model_to_use, threshold],
        )

    # tell clear button which components to clear
    clear.add([img_input, heatmap_gallery, heatmap_grid, caption, tags, rating, character, general])

    submit.click(
        predict,
        inputs=[img_input, model_to_use, threshold],
        outputs=[heatmap_gallery, heatmap_grid, caption, tags, rating, character, general],
        api_name="predict",
    )

if __name__ == "__main__":
    demo.queue(max_size=10)
    if getenv("SPACE_ID", None) is not None:
        demo.launch()
    else:
        demo.launch(
            server_name="0.0.0.0",
            server_port=7871,
            debug=True,
        )