import gradio as gr
import numpy as np
import torch
from diffusers import StableDiffusionInpaintPipeline
from PIL import Image
from segment_anything import SamPredictor, sam_model_registry, SamAutomaticMaskGenerator
from diffusers import ControlNetModel
from diffusers import UniPCMultistepScheduler
from controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
import colorsys

sam_checkpoint = "sam_vit_h_4b8939.pth"
model_type = "vit_h"
device = "cuda"


sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)
predictor = SamPredictor(sam)
mask_generator = SamAutomaticMaskGenerator(sam)

# pipe = StableDiffusionInpaintPipeline.from_pretrained(
#     "stabilityai/stable-diffusion-2-inpainting",
#     torch_dtype=torch.float16,
# )
# pipe = pipe.to("cuda")

controlnet = ControlNetModel.from_pretrained(
    "lllyasviel/sd-controlnet-seg",
    torch_dtype=torch.float16,
)
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    controlnet=controlnet,
    torch_dtype=torch.float16,
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.enable_xformers_memory_efficient_attention()


with gr.Blocks() as demo:
    gr.Markdown("# StableSAM: Stable Diffusion + Segment Anything Model")
    gr.Markdown(
        """
    To try the demo, upload an image and select object(s) you want to inpaint.
    Write a prompt & a negative prompt to control the inpainting.
    Click on the "Submit" button to inpaint the selected object(s).
    Check "Background" to inpaint the background instead of the selected object(s).

    If the demo is slow, clone the space to your own HF account and run on a GPU.
    """
    )
    selected_pixels = gr.State([])
    with gr.Row():
        input_img = gr.Image(label="Input")
        mask_img = gr.Image(label="Mask", interactive=False)
        seg_img = gr.Image(label="Segmentation", interactive=False)
        output_img = gr.Image(label="Output", interactive=False)

    with gr.Row():
        prompt_text = gr.Textbox(lines=1, label="Prompt")
        negative_prompt_text = gr.Textbox(lines=1, label="Negative Prompt")
        is_background = gr.Checkbox(label="Background")

    with gr.Row():
        submit = gr.Button("Submit")
        clear = gr.Button("Clear")

    def generate_mask(image, bg, sel_pix, evt: gr.SelectData):
        sel_pix.append(evt.index)
        predictor.set_image(image)
        input_point = np.array(sel_pix)
        input_label = np.ones(input_point.shape[0])
        mask, _, _ = predictor.predict(
            point_coords=input_point,
            point_labels=input_label,
            multimask_output=False,
        )
        # clear torch cache
        torch.cuda.empty_cache()
        if bg:
            mask = np.logical_not(mask)
        mask = Image.fromarray(mask[0, :, :])
        segs = mask_generator.generate(image)
        boolean_masks = [s["segmentation"] for s in segs]
        finseg = np.zeros((boolean_masks[0].shape[0], boolean_masks[0].shape[1], 3), dtype=np.uint8)
        # Loop over the boolean masks and assign a unique color to each class
        for class_id, boolean_mask in enumerate(boolean_masks):
            hue = class_id * 1.0 / len(boolean_masks)
            rgb = tuple(int(i * 255) for i in colorsys.hsv_to_rgb(hue, 1, 1))
            rgb_mask = np.zeros((boolean_mask.shape[0], boolean_mask.shape[1], 3), dtype=np.uint8)
            rgb_mask[:, :, 0] = boolean_mask * rgb[0]
            rgb_mask[:, :, 1] = boolean_mask * rgb[1]
            rgb_mask[:, :, 2] = boolean_mask * rgb[2]
            finseg += rgb_mask

        torch.cuda.empty_cache()

        return mask, finseg

    def inpaint(image, mask, seg_img, prompt, negative_prompt):
        image = Image.fromarray(image)
        mask = Image.fromarray(mask)
        seg_img = Image.fromarray(seg_img)

        image = image.resize((512, 512))
        mask = mask.resize((512, 512))
        seg_img = seg_img.resize((512, 512))

        output = pipe(
            prompt,
            image,
            mask,
            seg_img,
            negative_prompt=negative_prompt,
            num_inference_steps=20,
        ).images[0]
        torch.cuda.empty_cache()
        return output

    def _clear(sel_pix, img, mask, seg, out, prompt, neg_prompt, bg):
        sel_pix = []
        img = None
        mask = None
        seg = None
        out = None
        prompt = ""
        neg_prompt = ""
        bg = False
        return img, mask, seg, out, prompt, neg_prompt, bg

    input_img.select(
        generate_mask,
        [input_img, is_background, selected_pixels],
        [mask_img, seg_img],
    )
    submit.click(
        inpaint,
        inputs=[input_img, mask_img, seg_img, prompt_text, negative_prompt_text],
        outputs=[output_img],
    )
    clear.click(
        _clear,
        inputs=[
            selected_pixels,
            input_img,
            mask_img,
            seg_img,
            output_img,
            prompt_text,
            negative_prompt_text,
            is_background,
        ],
        outputs=[
            input_img,
            mask_img,
            seg_img,
            output_img,
            prompt_text,
            negative_prompt_text,
            is_background,
        ],
    )

if __name__ == "__main__":
    demo.launch()