app.py

import time
from typing import cast
from comfydeploy import ComfyDeploy
import os
import gradio as gr
from gradio.components.image_editor import EditorValue
from PIL import Image
import requests
import dotenv
from gradio_imageslider import ImageSlider
from io import BytesIO
import base64
import numpy as np
from loguru import logger


dotenv.load_dotenv()


API_KEY = os.environ.get("API_KEY")
CLEANER_DEPLOYMENT_ID = os.environ.get(
    "CLEANER_DEPLOYMENT_ID", "CLEANER_DEPLOYMENT_ID_NOT_SET"
)
MASKER_DEPLOYMENT_ID = os.environ.get(
    "MASKER_DEPLOYMENT_ID", "MASKER_DEPLOYMENT_ID_NOT_SET"
)

if not API_KEY:
    raise ValueError("Please set API_KEY in your environment variables")
if (
    not CLEANER_DEPLOYMENT_ID
    or CLEANER_DEPLOYMENT_ID == "CLEANER_DEPLOYMENT_ID_NOT_SET"
):
    raise ValueError("Please set CLEANER_DEPLOYMENT_ID in your environment variables")


client = ComfyDeploy(bearer_auth=API_KEY)


def get_base64_from_image(image: Image.Image) -> str:
    buffered: BytesIO = BytesIO()
    image.save(buffered, format="PNG")
    return base64.b64encode(buffered.getvalue()).decode("utf-8")


def compute_mask(
    image: Image.Image | str | None, progress: gr.Progress = gr.Progress()
) -> Image.Image | None:
    progress(0, desc="Preparing inputs...")
    if image is None:
        return None

    image = resize_image(image)

    image_base64 = get_base64_from_image(image)

    # Prepare inputs
    inputs: dict = {
        "input_image": f"data:image/png;base64,{image_base64}",
        "dilation_1_iterations": 10,
        "dilation_2_iterations": 15,
        "mask_blur_amount": 0,
    }

    # Call ComfyDeploy API
    try:
        result = client.run.create(
            request={"deployment_id": MASKER_DEPLOYMENT_ID, "inputs": inputs}
        )

        if result and result.object:
            run_id: str = result.object.run_id
            progress(0, desc="Starting processing...")
            # Wait for the result
            while True:
                run_result = client.run.get(run_id=run_id)
                if not run_result.object:
                    continue

                progress_value = run_result.object.progress or 0
                status = run_result.object.live_status or "Cold starting..."
                progress(progress_value, desc=f"Status: {status}")

                if run_result.object.status == "success":
                    for output in run_result.object.outputs or []:
                        if output.data and output.data.images:
                            image_url: str = output.data.images[0].url
                            # Download and return the mask image
                            response: requests.Response = requests.get(image_url)
                            mask_image: Image.Image = Image.open(
                                BytesIO(response.content)
                            )
                            return mask_image
                    return None
                elif run_result.object.status == "failed":
                    logger.debug("Processing failed")
                    return None

                time.sleep(1)  # Wait for 1 second before checking the status again
    except Exception as e:
        logger.debug(f"Error: {e}")
        return None


def create_editor_value(image: Image.Image, mask: Image.Image) -> EditorValue:
    # Convert image to numpy array
    image_np = np.array(image)

    # Resize mask to match image dimensions
    mask_resized = mask.resize((image_np.shape[1], image_np.shape[0]), Image.NEAREST)
    mask_np = np.array(mask_resized)

    # Ensure mask is grayscale
    if len(mask_np.shape) == 3:
        mask_np = mask_np[:, :, -1]

    # Create the layers array
    layers = np.zeros((image_np.shape[0], image_np.shape[1], 4), dtype=np.uint8)
    layers[:, :, 3] = mask_np

    # Create the composite image
    composite = np.zeros((image_np.shape[0], image_np.shape[1], 4), dtype=np.uint8)
    composite[:, :, :3] = image_np
    composite[:, :, 3] = np.where(mask_np == 255, 0, 255)

    return {
        "background": image_np,
        "layers": [layers],
        "composite": composite,
    }


def run_masking(
    image: np.ndarray | Image.Image | str | None,
    progress: gr.Progress = gr.Progress(),
    profile: gr.OAuthProfile | None = None,
) -> EditorValue | None:
    if image is None:
        return None

    if profile is None:
        gr.Info("Please log in to process the image.")
        return None

    # Convert np.ndarray to Image.Image
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image)
    elif isinstance(image, str):
        image = Image.open(image)

    mask = compute_mask(image, progress)
    if mask is None:
        return None

    # Use the new create_editor_value function
    return create_editor_value(image, mask)


def remove_objects(
    image: Image.Image | str | None,
    mask: Image.Image | str | None,
    user_data: dict,
    progress: gr.Progress = gr.Progress(),
) -> Image.Image | None:
    progress(0, desc="Preparing inputs...")
    if image is None or mask is None:
        return None

    if isinstance(mask, str):
        mask = Image.open(mask)
    if isinstance(image, str):
        image = Image.open(image)

    image_base64 = get_base64_from_image(image)
    mask_base64 = get_base64_from_image(mask)

    # Prepare inputs
    inputs: dict = {
        "image": f"data:image/png;base64,{image_base64}",
        "mask": f"data:image/png;base64,{mask_base64}",
        # "run_metatada": str(
        #     {
        #         "source": "HF",
        #         "user": user_data,
        #     }
        # ),
    }

    # Call ComfyDeploy API
    try:
        result = client.run.create(
            request={"deployment_id": CLEANER_DEPLOYMENT_ID, "inputs": inputs}
        )

        if result and result.object:
            run_id: str = result.object.run_id
            progress(0, desc="Starting processing...")
            # Wait for the result
            while True:
                run_result = client.run.get(run_id=run_id)
                if not run_result.object:
                    continue

                progress_value = (
                    run_result.object.progress
                    if run_result.object.progress is not None
                    else 0
                )
                status = (
                    run_result.object.live_status
                    if run_result.object.live_status is not None
                    else "Cold starting..."
                )
                progress(progress_value, desc=f"Status: {status}")

                if run_result.object.status == "success":
                    for output in run_result.object.outputs or []:
                        if output.data and output.data.images:
                            image_url: str = output.data.images[0].url
                            # Download and return both the original and processed images
                            response: requests.Response = requests.get(image_url)
                            processed_image: Image.Image = Image.open(
                                BytesIO(response.content)
                            )
                            return processed_image
                    return None
                elif run_result.object.status == "failed":
                    logger.debug("Processing failed")
                    return None

                time.sleep(1)  # Wait for 1 second before checking the status again
    except Exception as e:
        logger.debug(f"Error: {e}")
        return None


def resize_image(img: Image.Image, min_side_length: int = 768) -> Image.Image:
    if img.width <= min_side_length and img.height <= min_side_length:
        return img

    aspect_ratio = img.width / img.height
    if img.width < img.height:
        new_height = int(min_side_length / aspect_ratio)
        return img.resize((min_side_length, new_height))

    new_width = int(min_side_length * aspect_ratio)
    return img.resize((new_width, min_side_length))


def get_profile(profile) -> dict:
    return {
        "username": profile.username,
        "profile": profile.profile,
        "name": profile.name,
    }


async def run_removal(
    image_and_mask: EditorValue | None,
    progress: gr.Progress = gr.Progress(),
    profile: gr.OAuthProfile | None = None,
) -> tuple[Image.Image, Image.Image] | None:
    if not image_and_mask:
        gr.Info("Please upload an image and draw a mask")
        return None

    if profile is None:
        gr.Info("Please log in to process the image.")
        return None

    user_data = get_profile(profile)
    logger.debug("--------- RUN ----------")
    logger.debug(user_data)
    logger.debug("--------- RUN ----------")

    image_np = image_and_mask["background"]
    image_np = cast(np.ndarray, image_np)

    # If the image is empty, return None
    if np.sum(image_np) == 0:
        gr.Info("Please upload an image")
        return None

    alpha_channel = image_and_mask["layers"][0]
    alpha_channel = cast(np.ndarray, alpha_channel)
    mask_np = np.where(alpha_channel[:, :, 3] == 0, 0, 255).astype(np.uint8)

    # if mask_np is empty, return None
    if np.sum(mask_np) == 0:
        gr.Info("Please mark the areas you want to remove")
        return None

    mask = Image.fromarray(mask_np)
    mask = resize_image(mask)

    image = Image.fromarray(image_np)
    image = resize_image(image)

    output = remove_objects(
        image,  # type: ignore
        mask,  # type: ignore
        user_data,
        progress,
    )

    if output is None:
        gr.Info("Processing failed")
        return None
    progress(100, desc="Processing completed")
    return image, output


with gr.Blocks() as demo:
    gr.HTML("""
        <div style="display: flex; justify-content: center; text-align:center; flex-direction: column;">
            <h1 style="color: #333;">🧹 Room Cleaner</h1>
            <div style="max-width: 800px; margin: 0 auto;">
                <p style="font-size: 16px;">Upload an image and use the pencil tool (✏️ icon at the bottom) to <b>mark the areas you want to remove</b>.</p>
                <p style="font-size: 16px;">
                    For best results, include the shadows and reflections of the objects you want to remove.
                    You can remove multiple objects at once.
                    If you forget to mask some parts of your object, it's likely that the model will reconstruct them.
                </p>
                <br>
                <video width="640" height="360" controls style="margin: 0 auto; border-radius: 10px;">
                    <source src="https://dropshare.blanchon.xyz/public/dropshare/room_cleaner_demo.mp4" type="video/mp4">
                </video>
                <br>
                <p style="font-size: 16px;">Finally, click on the <b>"Run"</b> button to process the image.</p>
                <p style="font-size: 16px;">Wait for the processing to complete and compare the original and processed images using the slider.</p>
            
                <p style="font-size: 16px;">⚠️ Note that the images are compressed to reduce the workloads of the demo. </p>
            </div>
            <div style="margin-top: 20px; display: flex; justify-content: center; gap: 10px;">
                <a href="https://x.com/JulienBlanchon">
                    <img src="https://img.shields.io/badge/X-%23000000.svg?style=for-the-badge&logo=X&logoColor=white" alt="X Badge" style="border-radius: 3px;"/>
                </a>
            </div>
        </div>
    """)

    login_button = gr.LoginButton(scale=8)

    # ------ MASKING

    with gr.Column():
        with gr.Row(equal_height=False):
            # The image overflow, fix
            input_image = gr.Image(
                label="Input Image",
                height="full",
                width="full",
            )

            gr.HTML("""
                <h3 style="text-align: center;">Step 1: input image</h3>
                <p style="text-align: center;">Upload an image of the room you want to clean.</p>
            """)

        with gr.Row(equal_height=False):
            image_and_mask_auto = gr.ImageMask(
                label="Image and Mask",
                layers=False,
                show_fullscreen_button=False,
                sources=["upload"],
                show_download_button=False,
                interactive=True,
                height="full",
                width="full",
                brush=gr.Brush(default_size=75, colors=["#000000"], color_mode="fixed"),
                transforms=[],
            )

            with gr.Column():
                gr.HTML("""
                    <h3 style="text-align: center;">Step 2: Run masking</h3>
                    <p style="text-align: center;">Click get mask to get automatic masking and edit it after manually if needed.</p>
                """)
                compute_mask_btn = gr.ClearButton(
                    value="Get mask",
                    variant="primary",
                    size="lg",
                    components=[image_and_mask_auto],
                )

                compute_mask_btn.click(
                    fn=lambda _: gr.update(interactive=False, value="Processing..."),
                    inputs=[],
                    outputs=[compute_mask_btn],
                    api_name=False,
                ).then(
                    fn=run_masking,
                    inputs=[
                        input_image,
                    ],
                    outputs=[image_and_mask_auto],
                    api_name=False,
                ).then(
                    fn=lambda _: gr.update(interactive=True, value="Get mask"),
                    inputs=[],
                    outputs=[compute_mask_btn],
                    api_name=False,
                )

        # ------ REMOVAL

        with gr.Row(equal_height=False):
            image_slider = ImageSlider(
                label="Result",
                interactive=False,
            )

            with gr.Column():
                gr.HTML("""
                    <h3 style="text-align: center;">Step 3: Run removal</h3>
                    <p style="text-align: center;">Click run to remove the objects from the image.</p>
                """)

                process_btn = gr.ClearButton(
                    value="Run",
                    variant="primary",
                    size="lg",
                    components=[image_slider],
                )

                process_btn.click(
                    fn=lambda _: gr.update(interactive=False, value="Processing..."),
                    inputs=[],
                    outputs=[process_btn],
                    api_name=False,
                ).then(
                    fn=run_removal,
                    inputs=[
                        image_and_mask_auto,
                    ],
                    outputs=[image_slider],
                    api_name=False,
                ).then(
                    fn=lambda _: gr.update(interactive=True, value="Run"),
                    inputs=[],
                    outputs=[process_btn],
                    api_name=False,
                )


if __name__ == "__main__":
    demo.launch(
        debug=False,
        share=False,
        show_api=False,
    )