src/app.py

import dataclasses as dc
import io
from typing import Any

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

from fg import EditorAPIContext

pillow_heif.register_heif_opener()
pillow_heif.register_avif_opener()

env = Env()
env.read_env()

with env.prefixed("ERASER_"):
    API_URL: str = str(env.str("API_URL", "https://api.finegrain.ai/editor"))
    API_USER: str | None = env.str("API_USER")
    API_PASSWORD: str | None = env.str("API_PASSWORD")
    CA_BUNDLE: str | None = env.str("CA_BUNDLE", None)

assert API_USER is not None
assert API_PASSWORD is not None
CTX = EditorAPIContext(uri=API_URL, user=API_USER, password=API_PASSWORD)
if CA_BUNDLE:
    CTX.verify = CA_BUNDLE


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")
        async with ctx as client:
            response = await client.post(
                f"{ctx.uri}/state/upload",
                files={"file": f},
                headers=ctx.auth_headers,
            )
    response.raise_for_status()
    st_input = response.json()["state"]

    if params.bbox:
        segment_input_st = st_input
        segment_params = {"bbox": list(params.bbox)}
    else:
        assert params.prompt
        async with ctx as client:
            response = await client.post(
                f"{ctx.uri}/skills/infer-bbox/{st_input}",
                json={"product_name": params.prompt},
                headers=ctx.auth_headers,
            )
        response.raise_for_status()
        st_bbox = response.json()["state"]
        await ctx.sse_await(st_bbox)
        segment_input_st = st_bbox
        segment_params = {}

    async with ctx as client:
        response = await client.post(
            f"{ctx.uri}/skills/segment/{segment_input_st}",
            json=segment_params,
            headers=ctx.auth_headers,
        )
    response.raise_for_status()
    st_mask = response.json()["state"]
    await ctx.sse_await(st_mask)

    erase_params: dict[str, str | bool] = {
        "mode": "free",  # new API
        "restore_original_resolution": False,  # legacy API
    }
    async with ctx as client:
        response = await client.post(
            f"{ctx.uri}/skills/erase/{st_input}/{st_mask}",
            json=erase_params,
            headers=ctx.auth_headers,
        )
    response.raise_for_status()
    st_erased = response.json()["state"]
    await ctx.sse_await(st_erased)

    async with ctx as client:
        response = await client.get(
            f"{ctx.uri}/state/image/{st_erased}",
            params={"format": "JPEG", "resolution": "DISPLAY"},
            headers=ctx.auth_headers,
        )
    response.raise_for_status()
    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 = """
<center>

  <div style="
    background-color: #ff9100;
    color: #1f2937;
    padding: 0.5rem 1rem;
    font-size: 1.25rem;
  ">
    🚀 For an optimized version of this space, try out the
    <a href="https://finegrain.ai/editor?utm_source=hf&utm_campaign=object-eraser" target="_blank">Finegrain Editor</a>!
    You'll find there all our AI tools made available in a nice UI. 🚀
  </div>

  <h1 style="font-size: 1.5rem; margin-bottom: 0.5rem;">
    Object Eraser Powered By Refiners
  </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>
    This space is powered by Refiners, our open source micro-framework for simple foundation model adaptation.
    If you enjoyed it, please consider starring Refiners on GitHub!
  </p>

  <a href="https://github.com/finegrain-ai/refiners" target="_blank">
    <img src="https://img.shields.io/github/stars/finegrain-ai/refiners?style=social" />
  </a>

</center>
"""

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)