from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionInstructPix2PixPipeline
from diffusers import EulerAncestralDiscreteScheduler
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from controlnet_aux import OpenposeDetector, MLSDdetector, HEDdetector

from transformers import AutoModelForCausalLM, AutoTokenizer, CLIPSegProcessor, CLIPSegForImageSegmentation
from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering
from transformers import AutoImageProcessor, UperNetForSemanticSegmentation

import os
import random
import torch
import cv2
import uuid
from PIL import Image, ImageOps
import numpy as np
import math

from langchain.llms.openai import OpenAI

# Grounding DINO
import groundingdino.datasets.transforms as T
from groundingdino.models import build_model
from groundingdino.util import box_ops
from groundingdino.util.slconfig import SLConfig
from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap

# segment anything
from segment_anything import build_sam, SamPredictor, SamAutomaticMaskGenerator
import matplotlib.pyplot as plt
import wget

def prompts(name, description):
    def decorator(func):
        func.name = name
        func.description = description
        return func

    return decorator

def blend_gt2pt(old_image, new_image, sigma=0.15, steps=100):
    new_size = new_image.size
    old_size = old_image.size
    easy_img = np.array(new_image)
    gt_img_array = np.array(old_image)
    pos_w = (new_size[0] - old_size[0]) // 2
    pos_h = (new_size[1] - old_size[1]) // 2

    kernel_h = cv2.getGaussianKernel(old_size[1], old_size[1] * sigma)
    kernel_w = cv2.getGaussianKernel(old_size[0], old_size[0] * sigma)
    kernel = np.multiply(kernel_h, np.transpose(kernel_w))

    kernel[steps:-steps, steps:-steps] = 1
    kernel[:steps, :steps] = kernel[:steps, :steps] / kernel[steps - 1, steps - 1]
    kernel[:steps, -steps:] = kernel[:steps, -steps:] / kernel[steps - 1, -(steps)]
    kernel[-steps:, :steps] = kernel[-steps:, :steps] / kernel[-steps, steps - 1]
    kernel[-steps:, -steps:] = kernel[-steps:, -steps:] / kernel[-steps, -steps]
    kernel = np.expand_dims(kernel, 2)
    kernel = np.repeat(kernel, 3, 2)

    weight = np.linspace(0, 1, steps)
    top = np.expand_dims(weight, 1)
    top = np.repeat(top, old_size[0] - 2 * steps, 1)
    top = np.expand_dims(top, 2)
    top = np.repeat(top, 3, 2)

    weight = np.linspace(1, 0, steps)
    down = np.expand_dims(weight, 1)
    down = np.repeat(down, old_size[0] - 2 * steps, 1)
    down = np.expand_dims(down, 2)
    down = np.repeat(down, 3, 2)

    weight = np.linspace(0, 1, steps)
    left = np.expand_dims(weight, 0)
    left = np.repeat(left, old_size[1] - 2 * steps, 0)
    left = np.expand_dims(left, 2)
    left = np.repeat(left, 3, 2)

    weight = np.linspace(1, 0, steps)
    right = np.expand_dims(weight, 0)
    right = np.repeat(right, old_size[1] - 2 * steps, 0)
    right = np.expand_dims(right, 2)
    right = np.repeat(right, 3, 2)

    kernel[:steps, steps:-steps] = top
    kernel[-steps:, steps:-steps] = down
    kernel[steps:-steps, :steps] = left
    kernel[steps:-steps, -steps:] = right

    pt_gt_img = easy_img[pos_h:pos_h + old_size[1], pos_w:pos_w + old_size[0]]
    gaussian_gt_img = kernel * gt_img_array + (1 - kernel) * pt_gt_img  # gt img with blur img
    gaussian_gt_img = gaussian_gt_img.astype(np.int64)
    easy_img[pos_h:pos_h + old_size[1], pos_w:pos_w + old_size[0]] = gaussian_gt_img
    gaussian_img = Image.fromarray(easy_img)
    return gaussian_img

def get_new_image_name(org_img_name, func_name="update"):
    head_tail = os.path.split(org_img_name)
    head = head_tail[0]
    tail = head_tail[1]
    name_split = tail.split('.')[0].split('_')
    this_new_uuid = str(uuid.uuid4())[0:4]
    if len(name_split) == 1:
        most_org_file_name = name_split[0]
        recent_prev_file_name = name_split[0]
        new_file_name = '{}_{}_{}_{}.png'.format(this_new_uuid, func_name, recent_prev_file_name, most_org_file_name)
    else:
        assert len(name_split) == 4
        most_org_file_name = name_split[3]
        recent_prev_file_name = name_split[0]
        new_file_name = '{}_{}_{}_{}.png'.format(this_new_uuid, func_name, recent_prev_file_name, most_org_file_name)
    return os.path.join(head, new_file_name)

def seed_everything(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    return seed

class InstructPix2Pix:
    def __init__(self, device):
        print(f"Initializing InstructPix2Pix to {device}")
        self.device = device
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("timbrooks/instruct-pix2pix",
                                                                           safety_checker=None,
                                                                           torch_dtype=self.torch_dtype).to(device)
        self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)

    @prompts(name="Instruct Image Using Text",
             description="useful when you want to the style of the image to be like the text. "
                         "like: make it look like a painting. or make it like a robot. "
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path and the text. ")
    def inference(self, inputs):
        """Change style of image."""
        print("===>Starting InstructPix2Pix Inference")
        image_path, text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        original_image = Image.open(image_path)
        image = self.pipe(text, image=original_image, num_inference_steps=40, image_guidance_scale=1.2).images[0]
        updated_image_path = get_new_image_name(image_path, func_name="pix2pix")
        image.save(updated_image_path)
        print(f"\nProcessed InstructPix2Pix, Input Image: {image_path}, Instruct Text: {text}, "
              f"Output Image: {updated_image_path}")
        return updated_image_path


class Text2Image:
    def __init__(self, device):
        print(f"Initializing Text2Image to {device}")
        self.device = device
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",
                                                            torch_dtype=self.torch_dtype)
        self.pipe.to(device)
        self.a_prompt = 'best quality, extremely detailed'
        self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
                        'fewer digits, cropped, worst quality, low quality'

    @prompts(name="Generate Image From User Input Text",
             description="useful when you want to generate an image from a user input text and save it to a file. "
                         "like: generate an image of an object or something, or generate an image that includes some objects. "
                         "The input to this tool should be a string, representing the text used to generate image. ")
    def inference(self, text):
        image_filename = os.path.join('image', f"{str(uuid.uuid4())[:8]}.png")
        prompt = text + ', ' + self.a_prompt
        image = self.pipe(prompt, negative_prompt=self.n_prompt).images[0]
        image.save(image_filename)
        print(
            f"\nProcessed Text2Image, Input Text: {text}, Output Image: {image_filename}")
        return image_filename


class ImageCaptioning:
    def __init__(self, device):
        print(f"Initializing ImageCaptioning to {device}")
        self.device = device
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
        self.model = BlipForConditionalGeneration.from_pretrained(
            "Salesforce/blip-image-captioning-base", torch_dtype=self.torch_dtype).to(self.device)

    @prompts(name="Get Photo Description",
             description="useful when you want to know what is inside the photo. receives image_path as input. "
                         "The input to this tool should be a string, representing the image_path. ")
    def inference(self, image_path):
        inputs = self.processor(Image.open(image_path), return_tensors="pt").to(self.device, self.torch_dtype)
        out = self.model.generate(**inputs)
        captions = self.processor.decode(out[0], skip_special_tokens=True)
        print(f"\nProcessed ImageCaptioning, Input Image: {image_path}, Output Text: {captions}")
        return captions


class Image2Canny:
    def __init__(self, device):
        print("Initializing Image2Canny")
        self.low_threshold = 100
        self.high_threshold = 200

    @prompts(name="Edge Detection On Image",
             description="useful when you want to detect the edge of the image. "
                         "like: detect the edges of this image, or canny detection on image, "
                         "or perform edge detection on this image, or detect the canny image of this image. "
                         "The input to this tool should be a string, representing the image_path")
    def inference(self, inputs):
        image = Image.open(inputs)
        image = np.array(image)
        canny = cv2.Canny(image, self.low_threshold, self.high_threshold)
        canny = canny[:, :, None]
        canny = np.concatenate([canny, canny, canny], axis=2)
        canny = Image.fromarray(canny)
        updated_image_path = get_new_image_name(inputs, func_name="edge")
        canny.save(updated_image_path)
        print(f"\nProcessed Image2Canny, Input Image: {inputs}, Output Text: {updated_image_path}")
        return updated_image_path


class CannyText2Image:
    def __init__(self, device):
        print(f"Initializing CannyText2Image to {device}")
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-canny",
                                                          torch_dtype=self.torch_dtype)
        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None,
            torch_dtype=self.torch_dtype)
        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
        self.pipe.to(device)
        self.seed = -1
        self.a_prompt = 'best quality, extremely detailed'
        self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
                        'fewer digits, cropped, worst quality, low quality'

    @prompts(name="Generate Image Condition On Canny Image",
             description="useful when you want to generate a new real image from both the user description and a canny image."
                         " like: generate a real image of a object or something from this canny image,"
                         " or generate a new real image of a object or something from this edge image. "
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path and the user description. ")
    def inference(self, inputs):
        image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        image = Image.open(image_path)
        self.seed = random.randint(0, 65535)
        seed_everything(self.seed)
        prompt = f'{instruct_text}, {self.a_prompt}'
        image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
                          guidance_scale=9.0).images[0]
        updated_image_path = get_new_image_name(image_path, func_name="canny2image")
        image.save(updated_image_path)
        print(f"\nProcessed CannyText2Image, Input Canny: {image_path}, Input Text: {instruct_text}, "
              f"Output Text: {updated_image_path}")
        return updated_image_path


class Image2Line:
    def __init__(self, device):
        print("Initializing Image2Line")
        self.detector = MLSDdetector.from_pretrained('lllyasviel/ControlNet')

    @prompts(name="Line Detection On Image",
             description="useful when you want to detect the straight line of the image. "
                         "like: detect the straight lines of this image, or straight line detection on image, "
                         "or perform straight line detection on this image, or detect the straight line image of this image. "
                         "The input to this tool should be a string, representing the image_path")
    def inference(self, inputs):
        image = Image.open(inputs)
        mlsd = self.detector(image)
        updated_image_path = get_new_image_name(inputs, func_name="line-of")
        mlsd.save(updated_image_path)
        print(f"\nProcessed Image2Line, Input Image: {inputs}, Output Line: {updated_image_path}")
        return updated_image_path


class LineText2Image:
    def __init__(self, device):
        print(f"Initializing LineText2Image to {device}")
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-mlsd",
                                                          torch_dtype=self.torch_dtype)
        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None,
            torch_dtype=self.torch_dtype
        )
        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
        self.pipe.to(device)
        self.seed = -1
        self.a_prompt = 'best quality, extremely detailed'
        self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
                        'fewer digits, cropped, worst quality, low quality'

    @prompts(name="Generate Image Condition On Line Image",
             description="useful when you want to generate a new real image from both the user description "
                         "and a straight line image. "
                         "like: generate a real image of a object or something from this straight line image, "
                         "or generate a new real image of a object or something from this straight lines. "
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path and the user description. ")
    def inference(self, inputs):
        image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        image = Image.open(image_path)
        self.seed = random.randint(0, 65535)
        seed_everything(self.seed)
        prompt = f'{instruct_text}, {self.a_prompt}'
        image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
                          guidance_scale=9.0).images[0]
        updated_image_path = get_new_image_name(image_path, func_name="line2image")
        image.save(updated_image_path)
        print(f"\nProcessed LineText2Image, Input Line: {image_path}, Input Text: {instruct_text}, "
              f"Output Text: {updated_image_path}")
        return updated_image_path


class Image2Hed:
    def __init__(self, device):
        print("Initializing Image2Hed")
        self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet')

    @prompts(name="Hed Detection On Image",
             description="useful when you want to detect the soft hed boundary of the image. "
                         "like: detect the soft hed boundary of this image, or hed boundary detection on image, "
                         "or perform hed boundary detection on this image, or detect soft hed boundary image of this image. "
                         "The input to this tool should be a string, representing the image_path")
    def inference(self, inputs):
        image = Image.open(inputs)
        hed = self.detector(image)
        updated_image_path = get_new_image_name(inputs, func_name="hed-boundary")
        hed.save(updated_image_path)
        print(f"\nProcessed Image2Hed, Input Image: {inputs}, Output Hed: {updated_image_path}")
        return updated_image_path


class HedText2Image:
    def __init__(self, device):
        print(f"Initializing HedText2Image to {device}")
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-hed",
                                                          torch_dtype=self.torch_dtype)
        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None,
            torch_dtype=self.torch_dtype
        )
        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
        self.pipe.to(device)
        self.seed = -1
        self.a_prompt = 'best quality, extremely detailed'
        self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
                        'fewer digits, cropped, worst quality, low quality'

    @prompts(name="Generate Image Condition On Soft Hed Boundary Image",
             description="useful when you want to generate a new real image from both the user description "
                         "and a soft hed boundary image. "
                         "like: generate a real image of a object or something from this soft hed boundary image, "
                         "or generate a new real image of a object or something from this hed boundary. "
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path and the user description")
    def inference(self, inputs):
        image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        image = Image.open(image_path)
        self.seed = random.randint(0, 65535)
        seed_everything(self.seed)
        prompt = f'{instruct_text}, {self.a_prompt}'
        image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
                          guidance_scale=9.0).images[0]
        updated_image_path = get_new_image_name(image_path, func_name="hed2image")
        image.save(updated_image_path)
        print(f"\nProcessed HedText2Image, Input Hed: {image_path}, Input Text: {instruct_text}, "
              f"Output Image: {updated_image_path}")
        return updated_image_path


class Image2Scribble:
    def __init__(self, device):
        print("Initializing Image2Scribble")
        self.detector = HEDdetector.from_pretrained('lllyasviel/ControlNet')

    @prompts(name="Sketch Detection On Image",
             description="useful when you want to generate a scribble of the image. "
                         "like: generate a scribble of this image, or generate a sketch from this image, "
                         "detect the sketch from this image. "
                         "The input to this tool should be a string, representing the image_path")
    def inference(self, inputs):
        image = Image.open(inputs)
        scribble = self.detector(image, scribble=True)
        updated_image_path = get_new_image_name(inputs, func_name="scribble")
        scribble.save(updated_image_path)
        print(f"\nProcessed Image2Scribble, Input Image: {inputs}, Output Scribble: {updated_image_path}")
        return updated_image_path


class ScribbleText2Image:
    def __init__(self, device):
        print(f"Initializing ScribbleText2Image to {device}")
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-scribble",
                                                          torch_dtype=self.torch_dtype)
        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None,
            torch_dtype=self.torch_dtype
        )
        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
        self.pipe.to(device)
        self.seed = -1
        self.a_prompt = 'best quality, extremely detailed'
        self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
                        'fewer digits, cropped, worst quality, low quality'

    @prompts(name="Generate Image Condition On Sketch Image",
             description="useful when you want to generate a new real image from both the user description and "
                         "a scribble image or a sketch image. "
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path and the user description")
    def inference(self, inputs):
        image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        image = Image.open(image_path)
        self.seed = random.randint(0, 65535)
        seed_everything(self.seed)
        prompt = f'{instruct_text}, {self.a_prompt}'
        image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
                          guidance_scale=9.0).images[0]
        updated_image_path = get_new_image_name(image_path, func_name="scribble2image")
        image.save(updated_image_path)
        print(f"\nProcessed ScribbleText2Image, Input Scribble: {image_path}, Input Text: {instruct_text}, "
              f"Output Image: {updated_image_path}")
        return updated_image_path


class Image2Pose:
    def __init__(self, device):
        print("Initializing Image2Pose")
        self.detector = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')

    @prompts(name="Pose Detection On Image",
             description="useful when you want to detect the human pose of the image. "
                         "like: generate human poses of this image, or generate a pose image from this image. "
                         "The input to this tool should be a string, representing the image_path")
    def inference(self, inputs):
        image = Image.open(inputs)
        pose = self.detector(image)
        updated_image_path = get_new_image_name(inputs, func_name="human-pose")
        pose.save(updated_image_path)
        print(f"\nProcessed Image2Pose, Input Image: {inputs}, Output Pose: {updated_image_path}")
        return updated_image_path


class PoseText2Image:
    def __init__(self, device):
        print(f"Initializing PoseText2Image to {device}")
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-openpose",
                                                          torch_dtype=self.torch_dtype)
        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None,
            torch_dtype=self.torch_dtype)
        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
        self.pipe.to(device)
        self.num_inference_steps = 20
        self.seed = -1
        self.unconditional_guidance_scale = 9.0
        self.a_prompt = 'best quality, extremely detailed'
        self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \
                        ' fewer digits, cropped, worst quality, low quality'

    @prompts(name="Generate Image Condition On Pose Image",
             description="useful when you want to generate a new real image from both the user description "
                         "and a human pose image. "
                         "like: generate a real image of a human from this human pose image, "
                         "or generate a new real image of a human from this pose. "
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path and the user description")
    def inference(self, inputs):
        image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        image = Image.open(image_path)
        self.seed = random.randint(0, 65535)
        seed_everything(self.seed)
        prompt = f'{instruct_text}, {self.a_prompt}'
        image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
                          guidance_scale=9.0).images[0]
        updated_image_path = get_new_image_name(image_path, func_name="pose2image")
        image.save(updated_image_path)
        print(f"\nProcessed PoseText2Image, Input Pose: {image_path}, Input Text: {instruct_text}, "
              f"Output Image: {updated_image_path}")
        return updated_image_path


class SegText2Image:
    def __init__(self, device):
        print(f"Initializing SegText2Image to {device}")
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.controlnet = ControlNetModel.from_pretrained("fusing/stable-diffusion-v1-5-controlnet-seg",
                                                          torch_dtype=self.torch_dtype)
        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None,
            torch_dtype=self.torch_dtype)
        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
        self.pipe.to(device)
        self.seed = -1
        self.a_prompt = 'best quality, extremely detailed'
        self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \
                        ' fewer digits, cropped, worst quality, low quality'

    @prompts(name="Generate Image Condition On Segmentations",
             description="useful when you want to generate a new real image from both the user description and segmentations. "
                         "like: generate a real image of a object or something from this segmentation image, "
                         "or generate a new real image of a object or something from these segmentations. "
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path and the user description")
    def inference(self, inputs):
        image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        image = Image.open(image_path)
        self.seed = random.randint(0, 65535)
        seed_everything(self.seed)
        prompt = f'{instruct_text}, {self.a_prompt}'
        image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
                          guidance_scale=9.0).images[0]
        updated_image_path = get_new_image_name(image_path, func_name="segment2image")
        image.save(updated_image_path)
        print(f"\nProcessed SegText2Image, Input Seg: {image_path}, Input Text: {instruct_text}, "
              f"Output Image: {updated_image_path}")
        return updated_image_path


class Image2Depth:
    def __init__(self, device):
        print("Initializing Image2Depth")
        self.depth_estimator = pipeline('depth-estimation')

    @prompts(name="Predict Depth On Image",
             description="useful when you want to detect depth of the image. like: generate the depth from this image, "
                         "or detect the depth map on this image, or predict the depth for this image. "
                         "The input to this tool should be a string, representing the image_path")
    def inference(self, inputs):
        image = Image.open(inputs)
        depth = self.depth_estimator(image)['depth']
        depth = np.array(depth)
        depth = depth[:, :, None]
        depth = np.concatenate([depth, depth, depth], axis=2)
        depth = Image.fromarray(depth)
        updated_image_path = get_new_image_name(inputs, func_name="depth")
        depth.save(updated_image_path)
        print(f"\nProcessed Image2Depth, Input Image: {inputs}, Output Depth: {updated_image_path}")
        return updated_image_path


class DepthText2Image:
    def __init__(self, device):
        print(f"Initializing DepthText2Image to {device}")
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.controlnet = ControlNetModel.from_pretrained(
            "fusing/stable-diffusion-v1-5-controlnet-depth", torch_dtype=self.torch_dtype)
        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None,
            torch_dtype=self.torch_dtype)
        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
        self.pipe.to(device)
        self.seed = -1
        self.a_prompt = 'best quality, extremely detailed'
        self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \
                        ' fewer digits, cropped, worst quality, low quality'

    @prompts(name="Generate Image Condition On Depth",
             description="useful when you want to generate a new real image from both the user description and depth image. "
                         "like: generate a real image of a object or something from this depth image, "
                         "or generate a new real image of a object or something from the depth map. "
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path and the user description")
    def inference(self, inputs):
        image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        image = Image.open(image_path)
        self.seed = random.randint(0, 65535)
        seed_everything(self.seed)
        prompt = f'{instruct_text}, {self.a_prompt}'
        image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
                          guidance_scale=9.0).images[0]
        updated_image_path = get_new_image_name(image_path, func_name="depth2image")
        image.save(updated_image_path)
        print(f"\nProcessed DepthText2Image, Input Depth: {image_path}, Input Text: {instruct_text}, "
              f"Output Image: {updated_image_path}")
        return updated_image_path


class Image2Normal:
    def __init__(self, device):
        print("Initializing Image2Normal")
        self.depth_estimator = pipeline("depth-estimation", model="Intel/dpt-hybrid-midas")
        self.bg_threhold = 0.4

    @prompts(name="Predict Normal Map On Image",
             description="useful when you want to detect norm map of the image. "
                         "like: generate normal map from this image, or predict normal map of this image. "
                         "The input to this tool should be a string, representing the image_path")
    def inference(self, inputs):
        image = Image.open(inputs)
        original_size = image.size
        image = self.depth_estimator(image)['predicted_depth'][0]
        image = image.numpy()
        image_depth = image.copy()
        image_depth -= np.min(image_depth)
        image_depth /= np.max(image_depth)
        x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)
        x[image_depth < self.bg_threhold] = 0
        y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3)
        y[image_depth < self.bg_threhold] = 0
        z = np.ones_like(x) * np.pi * 2.0
        image = np.stack([x, y, z], axis=2)
        image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5
        image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
        image = Image.fromarray(image)
        image = image.resize(original_size)
        updated_image_path = get_new_image_name(inputs, func_name="normal-map")
        image.save(updated_image_path)
        print(f"\nProcessed Image2Normal, Input Image: {inputs}, Output Depth: {updated_image_path}")
        return updated_image_path


class NormalText2Image:
    def __init__(self, device):
        print(f"Initializing NormalText2Image to {device}")
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.controlnet = ControlNetModel.from_pretrained(
            "fusing/stable-diffusion-v1-5-controlnet-normal", torch_dtype=self.torch_dtype)
        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, safety_checker=None,
            torch_dtype=self.torch_dtype)
        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
        self.pipe.to(device)
        self.seed = -1
        self.a_prompt = 'best quality, extremely detailed'
        self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit,' \
                        ' fewer digits, cropped, worst quality, low quality'

    @prompts(name="Generate Image Condition On Normal Map",
             description="useful when you want to generate a new real image from both the user description and normal map. "
                         "like: generate a real image of a object or something from this normal map, "
                         "or generate a new real image of a object or something from the normal map. "
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path and the user description")
    def inference(self, inputs):
        image_path, instruct_text = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        image = Image.open(image_path)
        self.seed = random.randint(0, 65535)
        seed_everything(self.seed)
        prompt = f'{instruct_text}, {self.a_prompt}'
        image = self.pipe(prompt, image, num_inference_steps=20, eta=0.0, negative_prompt=self.n_prompt,
                          guidance_scale=9.0).images[0]
        updated_image_path = get_new_image_name(image_path, func_name="normal2image")
        image.save(updated_image_path)
        print(f"\nProcessed NormalText2Image, Input Normal: {image_path}, Input Text: {instruct_text}, "
              f"Output Image: {updated_image_path}")
        return updated_image_path


class VisualQuestionAnswering:
    def __init__(self, device):
        print(f"Initializing VisualQuestionAnswering to {device}")
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.device = device
        self.processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
        self.model = BlipForQuestionAnswering.from_pretrained(
            "Salesforce/blip-vqa-base", torch_dtype=self.torch_dtype).to(self.device)

    @prompts(name="Answer Question About The Image",
             description="useful when you need an answer for a question based on an image. "
                         "like: what is the background color of the last image, how many cats in this figure, what is in this figure. "
                         "The input to this tool should be a comma separated string of two, representing the image_path and the question")
    def inference(self, inputs):
        image_path, question = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        raw_image = Image.open(image_path).convert('RGB')
        inputs = self.processor(raw_image, question, return_tensors="pt").to(self.device, self.torch_dtype)
        out = self.model.generate(**inputs)
        answer = self.processor.decode(out[0], skip_special_tokens=True)
        print(f"\nProcessed VisualQuestionAnswering, Input Image: {image_path}, Input Question: {question}, "
              f"Output Answer: {answer}")
        return answer


class Segmenting:
    def __init__(self, device):
        print(f"Inintializing Segmentation to {device}")
        self.device = device
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.model_checkpoint_path = os.path.join("checkpoints", "sam")

        self.download_parameters()
        self.sam = build_sam(checkpoint=self.model_checkpoint_path).to(device)
        self.sam_predictor = SamPredictor(self.sam)
        self.mask_generator = SamAutomaticMaskGenerator(self.sam)

    def download_parameters(self):
        url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
        if not os.path.exists(self.model_checkpoint_path):
            wget.download(url, out=self.model_checkpoint_path)

    def show_mask(self, mask, ax, random_color=False):
        if random_color:
            color = np.concatenate([np.random.random(3), np.array([1])], axis=0)
        else:
            color = np.array([30 / 255, 144 / 255, 255 / 255, 1])
        h, w = mask.shape[-2:]
        mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
        ax.imshow(mask_image)

    def show_box(self, box, ax, label):
        x0, y0 = box[0], box[1]
        w, h = box[2] - box[0], box[3] - box[1]
        ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0, 0, 0, 0), lw=2))
        ax.text(x0, y0, label)

    def get_mask_with_boxes(self, image_pil, image, boxes_filt):

        size = image_pil.size
        H, W = size[1], size[0]
        for i in range(boxes_filt.size(0)):
            boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])
            boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
            boxes_filt[i][2:] += boxes_filt[i][:2]

        boxes_filt = boxes_filt.cpu()
        transformed_boxes = self.sam_predictor.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(self.device)

        masks, _, _ = self.sam_predictor.predict_torch(
            point_coords=None,
            point_labels=None,
            boxes=transformed_boxes.to(self.device),
            multimask_output=False,
        )
        return masks

    def segment_image_with_boxes(self, image_pil, image_path, boxes_filt, pred_phrases):

        image = cv2.imread(image_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        self.sam_predictor.set_image(image)

        masks = self.get_mask_with_boxes(image_pil, image, boxes_filt)

        # draw output image
        plt.figure(figsize=(10, 10))
        plt.imshow(image)
        for mask in masks:
            self.show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)

        updated_image_path = get_new_image_name(image_path, func_name="segmentation")
        plt.axis('off')
        plt.savefig(
            updated_image_path,
            bbox_inches="tight", dpi=300, pad_inches=0.0
        )
        return updated_image_path

    @prompts(name="Segment the Image",
             description="useful when you want to segment all the part of the image, but not segment a certain object."
                         "like: segment all the object in this image, or generate segmentations on this image, "
                         "or segment the image,"
                         "or perform segmentation on this image, "
                         "or segment all the object in this image."
                         "The input to this tool should be a string, representing the image_path")
    def inference_all(self, image_path):
        image = cv2.imread(image_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        masks = self.mask_generator.generate(image)
        plt.figure(figsize=(20, 20))
        plt.imshow(image)
        if len(masks) == 0:
            return
        sorted_anns = sorted(masks, key=(lambda x: x['area']), reverse=True)
        ax = plt.gca()
        ax.set_autoscale_on(False)
        polygons = []
        color = []
        for ann in sorted_anns:
            m = ann['segmentation']
            img = np.ones((m.shape[0], m.shape[1], 3))
            color_mask = np.random.random((1, 3)).tolist()[0]
            for i in range(3):
                img[:, :, i] = color_mask[i]
            ax.imshow(np.dstack((img, m)))

        updated_image_path = get_new_image_name(image_path, func_name="segment-image")
        plt.axis('off')
        plt.savefig(
            updated_image_path,
            bbox_inches="tight", dpi=300, pad_inches=0.0
        )
        return updated_image_path


class Text2Box:
    def __init__(self, device):
        print(f"Initializing ObjectDetection to {device}")
        self.device = device
        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
        self.model_checkpoint_path = os.path.join("checkpoints", "groundingdino")
        self.model_config_path = os.path.join("checkpoints", "grounding_config.py")
        self.download_parameters()
        self.box_threshold = 0.3
        self.text_threshold = 0.25
        self.grounding = (self.load_model()).to(self.device)

    def download_parameters(self):
        url = "https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth"
        if not os.path.exists(self.model_checkpoint_path):
            wget.download(url, out=self.model_checkpoint_path)
        config_url = "https://raw.githubusercontent.com/IDEA-Research/GroundingDINO/main/groundingdino/config/GroundingDINO_SwinT_OGC.py"
        if not os.path.exists(self.model_config_path):
            wget.download(config_url, out=self.model_config_path)

    def load_image(self, image_path):
        # load image
        image_pil = Image.open(image_path).convert("RGB")  # load image

        transform = T.Compose(
            [
                T.RandomResize([512], max_size=1333),
                T.ToTensor(),
                T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
            ]
        )
        image, _ = transform(image_pil, None)  # 3, h, w
        return image_pil, image

    def load_model(self):
        args = SLConfig.fromfile(self.model_config_path)
        args.device = self.device
        model = build_model(args)
        checkpoint = torch.load(self.model_checkpoint_path, map_location="cpu")
        load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
        print(load_res)
        _ = model.eval()
        return model

    def get_grounding_boxes(self, image, caption, with_logits=True):
        caption = caption.lower()
        caption = caption.strip()
        if not caption.endswith("."):
            caption = caption + "."
        image = image.to(self.device)
        with torch.no_grad():
            outputs = self.grounding(image[None], captions=[caption])
        logits = outputs["pred_logits"].cpu().sigmoid()[0]  # (nq, 256)
        boxes = outputs["pred_boxes"].cpu()[0]  # (nq, 4)
        logits.shape[0]

        # filter output
        logits_filt = logits.clone()
        boxes_filt = boxes.clone()
        filt_mask = logits_filt.max(dim=1)[0] > self.box_threshold
        logits_filt = logits_filt[filt_mask]  # num_filt, 256
        boxes_filt = boxes_filt[filt_mask]  # num_filt, 4
        logits_filt.shape[0]

        # get phrase
        tokenlizer = self.grounding.tokenizer
        tokenized = tokenlizer(caption)
        # build pred
        pred_phrases = []
        for logit, box in zip(logits_filt, boxes_filt):
            pred_phrase = get_phrases_from_posmap(logit > self.text_threshold, tokenized, tokenlizer)
            if with_logits:
                pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
            else:
                pred_phrases.append(pred_phrase)

        return boxes_filt, pred_phrases

    def plot_boxes_to_image(self, image_pil, tgt):
        H, W = tgt["size"]
        boxes = tgt["boxes"]
        labels = tgt["labels"]
        assert len(boxes) == len(labels), "boxes and labels must have same length"

        draw = ImageDraw.Draw(image_pil)
        mask = Image.new("L", image_pil.size, 0)
        mask_draw = ImageDraw.Draw(mask)

        # draw boxes and masks
        for box, label in zip(boxes, labels):
            # from 0..1 to 0..W, 0..H
            box = box * torch.Tensor([W, H, W, H])
            # from xywh to xyxy
            box[:2] -= box[2:] / 2
            box[2:] += box[:2]
            # random color
            color = tuple(np.random.randint(0, 255, size=3).tolist())
            # draw
            x0, y0, x1, y1 = box
            x0, y0, x1, y1 = int(x0), int(y0), int(x1), int(y1)

            draw.rectangle([x0, y0, x1, y1], outline=color, width=6)
            # draw.text((x0, y0), str(label), fill=color)

            font = ImageFont.load_default()
            if hasattr(font, "getbbox"):
                bbox = draw.textbbox((x0, y0), str(label), font)
            else:
                w, h = draw.textsize(str(label), font)
                bbox = (x0, y0, w + x0, y0 + h)
            # bbox = draw.textbbox((x0, y0), str(label))
            draw.rectangle(bbox, fill=color)
            draw.text((x0, y0), str(label), fill="white")

            mask_draw.rectangle([x0, y0, x1, y1], fill=255, width=2)

        return image_pil, mask

    @prompts(name="Detect the Give Object",
             description="useful when you only want to detect or find out given objects in the picture"
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path, the text description of the object to be found")
    def inference(self, inputs):
        image_path, det_prompt = inputs.split(",")
        print(f"image_path={image_path}, text_prompt={det_prompt}")
        image_pil, image = self.load_image(image_path)

        boxes_filt, pred_phrases = self.get_grounding_boxes(image, det_prompt)

        size = image_pil.size
        pred_dict = {
            "boxes": boxes_filt,
            "size": [size[1], size[0]],  # H,W
            "labels": pred_phrases, }

        image_with_box = self.plot_boxes_to_image(image_pil, pred_dict)[0]

        updated_image_path = get_new_image_name(image_path, func_name="detect-something")
        updated_image = image_with_box.resize(size)
        updated_image.save(updated_image_path)
        print(
            f"\nProcessed ObejectDetecting, Input Image: {image_path}, Object to be Detect {det_prompt}, "
            f"Output Image: {updated_image_path}")
        return updated_image_path


class Inpainting:
    def __init__(self, device):
        self.device = device
        self.revision = 'fp16' if 'cuda' in self.device else None
        self.torch_dtype = torch.float16 if 'cuda' in self.device else torch.float32

        self.inpaint = StableDiffusionInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting", revision=self.revision, torch_dtype=self.torch_dtype).to(device)

    def __call__(self, prompt, image, mask_image, height=512, width=512, num_inference_steps=50):
        update_image = self.inpaint(prompt=prompt, image=image.resize((width, height)),
                                    mask_image=mask_image.resize((width, height)), height=height, width=width,
                                    num_inference_steps=num_inference_steps).images[0]
        return update_image


class InfinityOutPainting:
    template_model = True # Add this line to show this is a template model.
    def __init__(self, ImageCaptioning, Inpainting, VisualQuestionAnswering):
        self.ImageCaption = ImageCaptioning
        self.inpaint = Inpainting
        self.ImageVQA = VisualQuestionAnswering
        self.a_prompt = 'best quality, extremely detailed'
        self.n_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, ' \
                        'fewer digits, cropped, worst quality, low quality'

    def get_BLIP_vqa(self, image, question):
        inputs = self.ImageVQA.processor(image, question, return_tensors="pt").to(self.ImageVQA.device,
                                                                                  self.ImageVQA.torch_dtype)
        out = self.ImageVQA.model.generate(**inputs)
        answer = self.ImageVQA.processor.decode(out[0], skip_special_tokens=True)
        print(f"\nProcessed VisualQuestionAnswering, Input Question: {question}, Output Answer: {answer}")
        return answer

    def get_BLIP_caption(self, image):
        inputs = self.ImageCaption.processor(image, return_tensors="pt").to(self.ImageCaption.device,
                                                                                self.ImageCaption.torch_dtype)
        out = self.ImageCaption.model.generate(**inputs)
        BLIP_caption = self.ImageCaption.processor.decode(out[0], skip_special_tokens=True)
        return BLIP_caption

    def get_imagine_caption(self, image, imagine):
        BLIP_caption = self.get_BLIP_caption(image)
        caption = BLIP_caption
        print(f'Prompt: {caption}')
        return caption

    def resize_image(self, image, max_size=1000000, multiple=8):
        aspect_ratio = image.size[0] / image.size[1]
        new_width = int(math.sqrt(max_size * aspect_ratio))
        new_height = int(new_width / aspect_ratio)
        new_width, new_height = new_width - (new_width % multiple), new_height - (new_height % multiple)
        return image.resize((new_width, new_height))

    def dowhile(self, original_img, tosize, expand_ratio, imagine, usr_prompt):
        old_img = original_img
        while (old_img.size != tosize):
            prompt = self.check_prompt(usr_prompt) if usr_prompt else self.get_imagine_caption(old_img, imagine)
            crop_w = 15 if old_img.size[0] != tosize[0] else 0
            crop_h = 15 if old_img.size[1] != tosize[1] else 0
            old_img = ImageOps.crop(old_img, (crop_w, crop_h, crop_w, crop_h))
            temp_canvas_size = (expand_ratio * old_img.width if expand_ratio * old_img.width < tosize[0] else tosize[0],
                                expand_ratio * old_img.height if expand_ratio * old_img.height < tosize[1] else tosize[
                                    1])
            temp_canvas, temp_mask = Image.new("RGB", temp_canvas_size, color="white"), Image.new("L", temp_canvas_size,
                                                                                                  color="white")
            x, y = (temp_canvas.width - old_img.width) // 2, (temp_canvas.height - old_img.height) // 2
            temp_canvas.paste(old_img, (x, y))
            temp_mask.paste(0, (x, y, x + old_img.width, y + old_img.height))
            resized_temp_canvas, resized_temp_mask = self.resize_image(temp_canvas), self.resize_image(temp_mask)
            image = self.inpaint(prompt=prompt, image=resized_temp_canvas, mask_image=resized_temp_mask,
                                              height=resized_temp_canvas.height, width=resized_temp_canvas.width,
                                              num_inference_steps=50).resize(
                (temp_canvas.width, temp_canvas.height), Image.ANTIALIAS)
            image = blend_gt2pt(old_img, image)
            old_img = image
        return old_img

    @prompts(name="Extend An Image",
             description="useful when you need to extend an image into a larger image."
                         "like: extend the image into a resolution of 2048x1024, extend the image into 2048x1024. "
                         "The input to this tool should be a comma separated string of two, representing the image_path and the resolution of widthxheight")
    def inference(self, inputs):
        image_path, resolution = inputs.split(',')
        width, height = resolution.split('x')
        tosize = (int(width), int(height))
        image = Image.open(image_path)
        image = ImageOps.crop(image, (10, 10, 10, 10))
        out_painted_image = self.dowhile(image, tosize, 4, True, False)
        updated_image_path = get_new_image_name(image_path, func_name="outpainting")
        out_painted_image.save(updated_image_path)
        print(f"\nProcessed InfinityOutPainting, Input Image: {image_path}, Input Resolution: {resolution}, "
              f"Output Image: {updated_image_path}")
        return updated_image_path


class ObjectSegmenting:
    template_model = True  # Add this line to show this is a template model.

    def __init__(self, Text2Box: Text2Box, Segmenting: Segmenting):
        # self.llm = OpenAI(temperature=0)
        self.grounding = Text2Box
        self.sam = Segmenting

    @prompts(name="Segment the given object",
             description="useful when you only want to segment the certain objects in the picture"
                         "according to the given text"
                         "like: segment the cat,"
                         "or can you segment an obeject for me"
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path, the text description of the object to be found")
    def inference(self, inputs):
        image_path, det_prompt = inputs.split(",")
        print(f"image_path={image_path}, text_prompt={det_prompt}")
        image_pil, image = self.grounding.load_image(image_path)
        boxes_filt, pred_phrases = self.grounding.get_grounding_boxes(image, det_prompt)
        updated_image_path = self.sam.segment_image_with_boxes(image_pil, image_path, boxes_filt, pred_phrases)
        print(
            f"\nProcessed ObejectSegmenting, Input Image: {image_path}, Object to be Segment {det_prompt}, "
            f"Output Image: {updated_image_path}")
        return updated_image_path


class ImageEditing:
    template_model = True

    def __init__(self, Text2Box: Text2Box, Segmenting: Segmenting, Inpainting: Inpainting):
        print(f"Initializing ImageEditing")
        self.sam = Segmenting
        self.grounding = Text2Box
        self.inpaint = Inpainting

    def pad_edge(self, mask, padding):
        # mask Tensor [H,W]
        mask = mask.numpy()
        true_indices = np.argwhere(mask)
        mask_array = np.zeros_like(mask, dtype=bool)
        for idx in true_indices:
            padded_slice = tuple(slice(max(0, i - padding), i + padding + 1) for i in idx)
            mask_array[padded_slice] = True
        new_mask = (mask_array * 255).astype(np.uint8)
        # new_mask
        return new_mask

    @prompts(name="Remove Something From The Photo",
             description="useful when you want to remove and object or something from the photo "
                         "from its description or location. "
                         "The input to this tool should be a comma separated string of two, "
                         "representing the image_path and the object need to be removed. ")
    def inference_remove(self, inputs):
        image_path, to_be_removed_txt = inputs.split(",")[0], ','.join(inputs.split(',')[1:])
        return self.inference_replace_sam(f"{image_path},{to_be_removed_txt},background")

    @prompts(name="Replace Something From The Photo",
             description="useful when you want to replace an object from the object description or "
                         "location with another object from its description. "
                         "The input to this tool should be a comma separated string of three, "
                         "representing the image_path, the object to be replaced, the object to be replaced with ")
    def inference_replace_sam(self, inputs):
        image_path, to_be_replaced_txt, replace_with_txt = inputs.split(",")

        print(f"image_path={image_path}, to_be_replaced_txt={to_be_replaced_txt}")
        image_pil, image = self.grounding.load_image(image_path)
        boxes_filt, pred_phrases = self.grounding.get_grounding_boxes(image, to_be_replaced_txt)
        image = cv2.imread(image_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        self.sam.sam_predictor.set_image(image)
        masks = self.sam.get_mask_with_boxes(image_pil, image, boxes_filt)
        mask = torch.sum(masks, dim=0).unsqueeze(0)
        mask = torch.where(mask > 0, True, False)
        mask = mask.squeeze(0).squeeze(0).cpu()  # tensor

        mask = self.pad_edge(mask, padding=20)  # numpy
        mask_image = Image.fromarray(mask)

        updated_image = self.inpaint(prompt=replace_with_txt, image=image_pil,
                                     mask_image=mask_image)
        updated_image_path = get_new_image_name(image_path, func_name="replace-something")
        updated_image = updated_image.resize(image_pil.size)
        updated_image.save(updated_image_path)
        print(
            f"\nProcessed ImageEditing, Input Image: {image_path}, Replace {to_be_replaced_txt} to {replace_with_txt}, "
            f"Output Image: {updated_image_path}")
        return updated_image_path