import gradio as gr
import spaces
import torch
from loadimg import load_img
from torchvision import transforms
from transformers import AutoModelForImageSegmentation
from diffusers import FluxFillPipeline
from PIL import Image, ImageOps
from sam2.sam2_image_predictor import SAM2ImagePredictor

torch.set_float32_matmul_precision(["high", "highest"][0])

birefnet = AutoModelForImageSegmentation.from_pretrained(
    "ZhengPeng7/BiRefNet", trust_remote_code=True
)
birefnet.to("cuda")

predictor = SAM2ImagePredictor.from_pretrained("facebook/sam2-hiera-tiny", device="cpu")

transform_image = transforms.Compose(
    [
        transforms.Resize((1024, 1024)),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    ]
)

pipe = FluxFillPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16
).to("cuda")


def prepare_image_and_mask(
    image,
    padding_top=0,
    padding_bottom=0,
    padding_left=0,
    padding_right=0,
):
    image = load_img(image).convert("RGB")
    # expand image (left,top,right,bottom)
    background = ImageOps.expand(
        image,
        border=(padding_left, padding_top, padding_right, padding_bottom),
        fill="white",
    )
    mask = Image.new("RGB", image.size, "black")
    mask = ImageOps.expand(
        mask,
        border=(padding_left, padding_top, padding_right, padding_bottom),
        fill="white",
    )
    return background, mask


def outpaint(
    image,
    padding_top=0,
    padding_bottom=0,
    padding_left=0,
    padding_right=0,
    prompt="",
    num_inference_steps=28,
    guidance_scale=50,
):
    background, mask = prepare_image_and_mask(
        image, padding_top, padding_bottom, padding_left, padding_right
    )

    result = pipe(
        prompt=prompt,
        height=background.height,
        width=background.width,
        image=background,
        mask_image=mask,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
    ).images[0]

    result = result.convert("RGBA")

    return result


def inpaint(
    image,
    mask,
    prompt="",
    num_inference_steps=28,
    guidance_scale=50,
):
    background = image.convert("RGB")
    mask = mask.convert("L")

    result = pipe(
        prompt=prompt,
        height=background.height,
        width=background.width,
        image=background,
        mask_image=mask,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
    ).images[0]

    result = result.convert("RGBA")

    return result


def rmbg(image=None, url=None):
    if image is None:
        image = url
    image = load_img(image).convert("RGB")
    image_size = image.size
    input_images = transform_image(image).unsqueeze(0).to("cuda")
    # Prediction
    with torch.no_grad():
        preds = birefnet(input_images)[-1].sigmoid().cpu()
    pred = preds[0].squeeze()
    pred_pil = transforms.ToPILImage()(pred)
    mask = pred_pil.resize(image_size)
    image.putalpha(mask)
    return image


def mask_generation(image=None, json=None):
    return None


@spaces.GPU
def main(*args):
    api_num = args[0]
    args = args[1:]
    if api_num == 1:
        return rmbg(*args)
    elif api_num == 2:
        return outpaint(*args)
    elif api_num == 3:
        return inpaint(*args)
    elif api_num == 4:
        return mask_generation(*args)


rmbg_tab = gr.Interface(
    fn=main,
    inputs=[
        gr.Number(1, interactive=False),
        "image",
        gr.Text("", label="url"),
    ],
    outputs=["image"],
    api_name="rmbg",
    examples=[[1, "./assets/Inpainting mask.png", ""]],
    cache_examples=False,
    description="pass an image or a url of an image",
)

outpaint_tab = gr.Interface(
    fn=main,
    inputs=[
        gr.Number(2, interactive=False),
        gr.Image(label="image", type="pil"),
        gr.Number(label="padding top"),
        gr.Number(label="padding bottom"),
        gr.Number(label="padding left"),
        gr.Number(label="padding right"),
        gr.Text(label="prompt"),
        gr.Number(value=50, label="num_inference_steps"),
        gr.Number(value=28, label="guidance_scale"),
    ],
    outputs=["image"],
    api_name="outpainting",
    examples=[[2, "./assets/rocket.png", 100, 0, 0, 0, "", 50, 28]],
    cache_examples=False,
)


inpaint_tab = gr.Interface(
    fn=main,
    inputs=[
        gr.Number(3, interactive=False),
        gr.Image(label="image", type="pil"),
        gr.Image(label="mask", type="pil"),
        gr.Text(label="prompt"),
        gr.Number(value=50, label="num_inference_steps"),
        gr.Number(value=28, label="guidance_scale"),
    ],
    outputs=["image"],
    api_name="inpaint",
    examples=[[3, "./assets/rocket.png", "./assets/Inpainting mask.png"]],
    cache_examples=False,
    description="it is recommended that you use https://github.com/la-voliere/react-mask-editor when creating an image mask in JS and then inverse it before sending it to this space",
)


sam2_tab = gr.Interface(
    main,
    inputs=[
        gr.Number(4, interactive=False),
        gr.Image("image", type="pil"),
        gr.JSON(),
    ],
    outputs=["image"],
)

demo = gr.TabbedInterface(
    [rmbg_tab, outpaint_tab, inpaint_tab, sam2_tab],
    ["remove background", "outpainting", "inpainting", "sam2"],
    title="Utilities that require GPU",
)


demo.launch()