src/app.py

import dataclasses as dc
import io
from functools import cache
from typing import Any

import gradio as gr
import pillow_heif
from environs import Env
from finegrain import EditorAPIContext
from gradio_image_annotation import image_annotator
from gradio_imageslider import ImageSlider
from PIL import Image

pillow_heif.register_heif_opener()
pillow_heif.register_avif_opener()

env = Env()
env.read_env()

with env.prefixed("ERASER_"):
    API_USER: str | None = env.str("API_USER")
    API_PASSWORD: str | None = env.str("API_PASSWORD")
    API_URL: str | None = env.str("API_URL", None)
    CA_BUNDLE: str | None = env.str("CA_BUNDLE", None)


@cache
def _ctx() -> EditorAPIContext:
    assert API_USER is not None
    assert API_PASSWORD is not None
    ctx = EditorAPIContext(user=API_USER, password=API_PASSWORD, priority="low")
    if CA_BUNDLE:
        ctx.verify = CA_BUNDLE
    if API_URL:
        ctx.base_url = API_URL
    return ctx


def resize(image: Image.Image, shortest_side: int = 768) -> Image.Image:
    if image.width <= shortest_side and image.height <= shortest_side:
        return image
    if image.width < image.height:
        return image.resize(size=(shortest_side, int(shortest_side * image.height / image.width)))
    return image.resize(size=(int(shortest_side * image.width / image.height), shortest_side))


@dc.dataclass(kw_only=True)
class ProcessParams:
    image: Image.Image
    prompt: str | None = None
    bbox: tuple[int, int, int, int] | None = None


async def _process(ctx: EditorAPIContext, params: ProcessParams) -> Image.Image:
    with io.BytesIO() as f:
        params.image.save(f, format="JPEG")
        response = await ctx.request("POST", "state/upload", files={"file": f})
    st_input = response.json()["state"]

    if params.bbox:
        segment_input_st = st_input
        segment_params = {"bbox": list(params.bbox)}
    else:
        assert params.prompt
        segment_input_st = await ctx.ensure_skill(
            f"infer-bbox/{st_input}",
            {"product_name": params.prompt},
        )
        segment_params = {}

    st_mask = await ctx.ensure_skill(f"segment/{segment_input_st}", segment_params)
    st_erased = await ctx.ensure_skill(f"erase/{st_input}/{st_mask}", {"mode": "free"})

    response = await ctx.request(
        "GET",
        f"state/image/{st_erased}",
        params={"format": "JPEG", "resolution": "DISPLAY"},
    )
    f = io.BytesIO()
    f.write(response.content)
    f.seek(0)
    return Image.open(f)


def process_bbox(prompts: dict[str, Any]) -> tuple[Image.Image, Image.Image]:
    assert isinstance(img := prompts["image"], Image.Image)
    assert isinstance(boxes := prompts["boxes"], list)
    assert len(boxes) == 1
    assert isinstance(box := boxes[0], dict)

    resized_img = resize(img)
    bbox = [box[k] for k in ["xmin", "ymin", "xmax", "ymax"]]
    if resized_img.width != img.width:
        bbox = [int(v * resized_img.width / img.width) for v in bbox]

    output_image = _ctx().run_one_sync(
        _process,
        ProcessParams(
            image=resized_img,
            bbox=(bbox[0], bbox[1], bbox[2], bbox[3]),
        ),
    )
    return (img, output_image)


def on_change_bbox(prompts: dict[str, Any] | None):
    return gr.update(interactive=prompts is not None and len(prompts["boxes"]) > 0)


def process_prompt(img: Image.Image, prompt: str) -> tuple[Image.Image, Image.Image]:
    resized_img = resize(img)
    output_image = _ctx().run_one_sync(
        _process,
        ProcessParams(image=resized_img, prompt=prompt),
    )
    return (img, output_image)


def on_change_prompt(img: Image.Image | None, prompt: str | None):
    return gr.update(interactive=bool(img and prompt))


TITLE = """
<div style="
  background-color: #f08c00;
  color: #212529;
  padding: 0.5rem 1rem;
  font-size: 1.25rem;
" align="center">
  🥳 We're launching our API! It's way faster and more accurate than this space, so check it out!
  <a href="https://finegrain.ai/?utm_source=hf&utm_campaign=object-eraser" target="_blank">Try it now for free</a>
  using our Editor app! 🚀
</div>
<h1>Finegrain Object Eraser</h1>
<p>
  Erase any object from your image just by naming it — no manual work required!
  Not only will the object disappear, but so will its effects on the scene, like shadows or reflections.
</p>
<p>
  <a href="https://discord.gg/zFKg5TjXub" target="_blank">[Discord]</a>
  <a href="https://github.com/finegrain-ai" target="_blank">[GitHub]</a>
</p>
"""

with gr.Blocks() as demo:
    gr.HTML(TITLE)
    with gr.Tab("By prompt", id="tab_prompt"):
        with gr.Row():
            with gr.Column():
                iimg = gr.Image(type="pil", label="Input")
                prompt = gr.Textbox(label="What should we erase?")
            with gr.Column():
                oimg = ImageSlider(label="Output")
        with gr.Row():
            btn = gr.ClearButton(components=[oimg], value="Erase Object", interactive=False)

        for inp in [iimg, prompt]:
            inp.change(
                fn=on_change_prompt,
                inputs=[iimg, prompt],
                outputs=[btn],
            )
        btn.click(
            fn=process_prompt,
            inputs=[iimg, prompt],
            outputs=[oimg],
            api_name=False,
        )

        examples = [
            [
                "examples/white-towels-rattan-basket-white-table-with-bright-room-background.jpg",
                "soap",
            ],
            [
                "examples/interior-decor-with-mirror-potted-plant.jpg",
                "potted plant",
            ],
            [
                "examples/detail-ball-basketball-court-sunset.jpg",
                "basketball",
            ],
            [
                "examples/still-life-device-table_23-2150994394.jpg",
                "glass of water",
            ],
            [
                "examples/knife-fork-green-checkered-napkin_140725-63576.jpg",
                "knife and fork",
            ],
            [
                "examples/city-night-with-architecture-vibrant-lights_23-2149836930.jpg",
                "frontmost black car on right lane",
            ],
            [
                "examples/close-up-coffee-latte-wooden-table_23-2147893063.jpg",
                "coffee cup on plate",
            ],
            [
                "examples/empty-chair-with-vase-plant_74190-2078.jpg",
                "chair",
            ],
        ]

        ex = gr.Examples(
            examples=examples,
            inputs=[iimg, prompt],
            outputs=[oimg],
            fn=process_prompt,
            cache_examples=True,
        )
    with gr.Tab("By bounding box", id="tab_bb"):
        with gr.Row():
            with gr.Column():
                annotator = image_annotator(
                    image_type="pil",
                    disable_edit_boxes=True,
                    show_download_button=False,
                    show_share_button=False,
                    single_box=True,
                    label="Input",
                )
            with gr.Column():
                oimg = ImageSlider(label="Output")
        with gr.Row():
            btn = gr.ClearButton(components=[oimg], value="Erase Object", interactive=False)

        annotator.change(
            fn=on_change_bbox,
            inputs=[annotator],
            outputs=[btn],
        )
        btn.click(
            fn=process_bbox,
            inputs=[annotator],
            outputs=[oimg],
            api_name=False,
        )

        examples = [
            {
                "image": "examples/white-towels-rattan-basket-white-table-with-bright-room-background.jpg",
                "boxes": [{"xmin": 836, "ymin": 475, "xmax": 1125, "ymax": 1013}],
            },
            {
                "image": "examples/interior-decor-with-mirror-potted-plant.jpg",
                "boxes": [{"xmin": 47, "ymin": 907, "xmax": 397, "ymax": 1633}],
            },
            {
                "image": "examples/detail-ball-basketball-court-sunset.jpg",
                "boxes": [{"xmin": 673, "ymin": 954, "xmax": 911, "ymax": 1186}],
            },
            {
                "image": "examples/still-life-device-table_23-2150994394.jpg",
                "boxes": [{"xmin": 429, "ymin": 586, "xmax": 571, "ymax": 834}],
            },
            {
                "image": "examples/knife-fork-green-checkered-napkin_140725-63576.jpg",
                "boxes": [{"xmin": 972, "ymin": 226, "xmax": 1092, "ymax": 1023}],
            },
            {
                "image": "examples/city-night-with-architecture-vibrant-lights_23-2149836930.jpg",
                "boxes": [{"xmin": 215, "ymin": 637, "xmax": 411, "ymax": 855}],
            },
            {
                "image": "examples/close-up-coffee-latte-wooden-table_23-2147893063.jpg",
                "boxes": [{"xmin": 255, "ymin": 456, "xmax": 1080, "ymax": 1064}],
            },
            {
                "image": "examples/empty-chair-with-vase-plant_74190-2078.jpg",
                "boxes": [{"xmin": 35, "ymin": 320, "xmax": 383, "ymax": 983}],
            },
        ]

        ex = gr.Examples(
            examples=examples,
            inputs=[annotator],
            outputs=[oimg],
            fn=process_bbox,
            cache_examples=True,
        )

demo.queue(max_size=30, api_open=False)
demo.launch(show_api=False)