File: visual_chatgpt.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

# coding: utf-8
import os
import gradio as gr
import random
import torch
import cv2
import re
import uuid
from PIL import Image, ImageDraw, ImageOps, ImageFont
import math
import numpy as np
import argparse
import inspect
import tempfile
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering
from transformers import AutoImageProcessor, UperNetForSemanticSegmentation

from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionInstructPix2PixPipeline
from diffusers import EulerAncestralDiscreteScheduler
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker

from controlnet_aux import OpenposeDetector, MLSDdetector, HEDdetector

from langchain.agents.initialize import initialize_agent
from langchain.agents.tools import Tool
from langchain.chains.conversation.memory import ConversationBufferMemory
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 cv2
import numpy as np
import matplotlib.pyplot as plt
import wget

VISUAL_CHATGPT_PREFIX = """Visual ChatGPT is designed to be able to assist with a wide range of text and visual related tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. Visual ChatGPT is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.

Visual ChatGPT is able to process and understand large amounts of text and images. As a language model, Visual ChatGPT can not directly read images, but it has a list of tools to finish different visual tasks. Each image will have a file name formed as "image/xxx.png", and Visual ChatGPT can invoke different tools to indirectly understand pictures. When talking about images, Visual ChatGPT is very strict to the file name and will never fabricate nonexistent files. When using tools to generate new image files, Visual ChatGPT is also known that the image may not be the same as the user's demand, and will use other visual question answering tools or description tools to observe the real image. Visual ChatGPT is able to use tools in a sequence, and is loyal to the tool observation outputs rather than faking the image content and image file name. It will remember to provide the file name from the last tool observation, if a new image is generated.

Human may provide new figures to Visual ChatGPT with a description. The description helps Visual ChatGPT to understand this image, but Visual ChatGPT should use tools to finish following tasks, rather than directly imagine from the description.

Overall, Visual ChatGPT is a powerful visual dialogue assistant tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. 


TOOLS:
------

Visual ChatGPT  has access to the following tools:"""

VISUAL_CHATGPT_FORMAT_INSTRUCTIONS = """To use a tool, please use the following format:

```
Thought: Do I need to use a tool? Yes
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
```

When you have a response to say to the Human, or if you do not need to use a tool, you MUST use the format:

```
Thought: Do I need to use a tool? No
{ai_prefix}: [your response here]
```
"""

VISUAL_CHATGPT_SUFFIX = """You are very strict to the filename correctness and will never fake a file name if it does not exist.
You will remember to provide the image file name loyally if it's provided in the last tool observation.

Begin!

Previous conversation history:
{chat_history}

New input: {input}
Since Visual ChatGPT is a text language model, Visual ChatGPT must use tools to observe images rather than imagination.
The thoughts and observations are only visible for Visual ChatGPT, Visual ChatGPT should remember to repeat important information in the final response for Human. 
Thought: Do I need to use a tool? {agent_scratchpad} Let's think step by step.
"""

VISUAL_CHATGPT_PREFIX_CN = """Visual ChatGPT 旨在能够协助完成范围广泛的文本和视觉相关任务，从回答简单的问题到提供对广泛主题的深入解释和讨论。 Visual ChatGPT 能够根据收到的输入生成类似人类的文本，使其能够进行听起来自然的对话，并提供连贯且与手头主题相关的响应。

Visual ChatGPT 能够处理和理解大量文本和图像。作为一种语言模型，Visual ChatGPT 不能直接读取图像，但它有一系列工具来完成不同的视觉任务。每张图片都会有一个文件名，格式为“image/xxx.png”，Visual ChatGPT可以调用不同的工具来间接理解图片。在谈论图片时，Visual ChatGPT 对文件名的要求非常严格，绝不会伪造不存在的文件。在使用工具生成新的图像文件时，Visual ChatGPT也知道图像可能与用户需求不一样，会使用其他视觉问答工具或描述工具来观察真实图像。 Visual ChatGPT 能够按顺序使用工具，并且忠于工具观察输出，而不是伪造图像内容和图像文件名。如果生成新图像，它将记得提供上次工具观察的文件名。

Human 可能会向 Visual ChatGPT 提供带有描述的新图形。描述帮助 Visual ChatGPT 理解这个图像，但 Visual ChatGPT 应该使用工具来完成以下任务，而不是直接从描述中想象。有些工具将会返回英文描述，但你对用户的聊天应当采用中文。

总的来说，Visual ChatGPT 是一个强大的可视化对话辅助工具，可以帮助处理范围广泛的任务，并提供关于范围广泛的主题的有价值的见解和信息。

工具列表:
------

Visual ChatGPT 可以使用这些工具:"""

VISUAL_CHATGPT_FORMAT_INSTRUCTIONS_CN = """用户使用中文和你进行聊天，但是工具的参数应当使用英文。如果要调用工具，你必须遵循如下格式:

```
Thought: Do I need to use a tool? Yes
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
```

当你不再需要继续调用工具，而是对观察结果进行总结回复时，你必须使用如下格式：


```
Thought: Do I need to use a tool? No
{ai_prefix}: [your response here]
```
"""

VISUAL_CHATGPT_SUFFIX_CN = """你对文件名的正确性非常严格，而且永远不会伪造不存在的文件。

开始!

因为Visual ChatGPT是一个文本语言模型，必须使用工具去观察图片而不是依靠想象。
推理想法和观察结果只对Visual ChatGPT可见，需要记得在最终回复时把重要的信息重复给用户，你只能给用户返回中文句子。我们一步一步思考。在你使用工具时，工具的参数只能是英文。

聊天历史:
{chat_history}

新输入: {input}
Thought: Do I need to use a tool? {agent_scratchpad}
"""

os.makedirs('image', exist_ok=True)


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


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 cut_dialogue_history(history_memory, keep_last_n_words=500):
    if history_memory is None or len(history_memory) == 0:
        return history_memory
    tokens = history_memory.split()
    n_tokens = len(tokens)
    print(f"history_memory:{history_memory}, n_tokens: {n_tokens}")
    if n_tokens < keep_last_n_words:
        return history_memory
    paragraphs = history_memory.split('\n')
    last_n_tokens = n_tokens
    while last_n_tokens >= keep_last_n_words:
        last_n_tokens -= len(paragraphs[0].split(' '))
        paragraphs = paragraphs[1:]
    return '\n' + '\n'.join(paragraphs)


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())[:4]
    if len(name_split) == 1:
        most_org_file_name = name_split[0]
    else:
        assert len(name_split) == 4
        most_org_file_name = name_split[3]
    recent_prev_file_name = name_split[0]
    new_file_name = f'{this_new_uuid}_{func_name}_{recent_prev_file_name}_{most_org_file_name}.png'
    return os.path.join(head, new_file_name)

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=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
                                                                           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=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
            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=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
            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=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
            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=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
            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=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
            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=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
            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=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
            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=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker'),
            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)
        
        self.saved_points = []
        self.saved_labels = []

    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: np.ndarray,image: np.ndarray,
                random_color: bool = False, transparency=1) -> np.ndarray:
        
        """Visualize a mask on top of an image.
        Args:
            mask (np.ndarray): A 2D array of shape (H, W).
            image (np.ndarray): A 3D array of shape (H, W, 3).
            random_color (bool): Whether to use a random color for the mask.
        Outputs:
            np.ndarray: A 3D array of shape (H, W, 3) with the mask
            visualized on top of the image.
            transparenccy: the transparency of the segmentation mask
        """
        
        if random_color:
            color = np.concatenate([np.random.random(3)], axis=0)
        else:
            color = np.array([30 / 255, 144 / 255, 255 / 255])
        h, w = mask.shape[-2:]
        mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1) * 255

        image = cv2.addWeighted(image, 0.7, mask_image.astype('uint8'), transparency, 0)


        return 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

        for mask in masks:
            image = self.show_mask(mask[0].cpu().numpy(), image, random_color=True, transparency=0.3)

        updated_image_path = get_new_image_name(image_path, func_name="segmentation")
        
        new_image = Image.fromarray(image)
        new_image.save(updated_image_path)

        return updated_image_path

    def set_image(self, img) -> None:
        """Set the image for the predictor."""
        with torch.cuda.amp.autocast():
            self.sam_predictor.set_image(img)

    def show_points(self, coords: np.ndarray, labels: np.ndarray,
                image: np.ndarray) -> np.ndarray:
        """Visualize points on top of an image.

        Args:
            coords (np.ndarray): A 2D array of shape (N, 2).
            labels (np.ndarray): A 1D array of shape (N,).
            image (np.ndarray): A 3D array of shape (H, W, 3).
        Returns:
            np.ndarray: A 3D array of shape (H, W, 3) with the points
            visualized on top of the image.
        """
        pos_points = coords[labels == 1]
        neg_points = coords[labels == 0]
        for p in pos_points:
            image = cv2.circle(
                image, p.astype(int), radius=3, color=(0, 255, 0), thickness=-1)
        for p in neg_points:
            image = cv2.circle(
                image, p.astype(int), radius=3, color=(255, 0, 0), thickness=-1)
        return image


    def segment_image_with_click(self, img, is_positive: bool,
                            evt: gr.SelectData):
                            
        self.sam_predictor.set_image(img)
        self.saved_points.append([evt.index[0], evt.index[1]])
        self.saved_labels.append(1 if is_positive else 0)
        input_point = np.array(self.saved_points)
        input_label = np.array(self.saved_labels)
        
        # Predict the mask
        with torch.cuda.amp.autocast():
            masks, scores, logits = self.sam_predictor.predict(
                point_coords=input_point,
                point_labels=input_label,
                multimask_output=False,
            )

        img = self.show_mask(masks[0], img, random_color=False, transparency=0.3)

        img = self.show_points(input_point, input_label, img)

        return img

    def segment_image_with_coordinate(self, img, is_positive: bool,
                            coordinate: tuple):
        '''
            Args:
                img (numpy.ndarray): the given image, shape: H x W x 3.
                is_positive: whether the click is positive, if want to add mask use True else False.
                coordinate: the position of the click
                          If the position is (x,y), means click at the x-th column and y-th row of the pixel matrix.
                          So x correspond to W, and y correspond to H.
            Output:
                img (PLI.Image.Image): the result image
                result_mask (numpy.ndarray): the result mask, shape: H x W

            Other parameters:
                transparency (float): the transparenccy of the mask
                                      to control he degree of transparency after the mask is superimposed.
                                      if transparency=1, then the masked part will be completely replaced with other colors.
        '''
        self.sam_predictor.set_image(img)
        self.saved_points.append([coordinate[0], coordinate[1]])
        self.saved_labels.append(1 if is_positive else 0)
        input_point = np.array(self.saved_points)
        input_label = np.array(self.saved_labels)

        # Predict the mask
        with torch.cuda.amp.autocast():
            masks, scores, logits = self.sam_predictor.predict(
                point_coords=input_point,
                point_labels=input_label,
                multimask_output=False,
            )


        img = self.show_mask(masks[0], img, random_color=False, transparency=0.3)

        img = self.show_points(input_point, input_label, img)

        img = Image.fromarray(img)
        
        result_mask = masks[0]

        return img, result_mask

    @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,safety_checker=StableDiffusionSafetyChecker.from_pretrained('CompVis/stable-diffusion-safety-checker')).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.llm = OpenAI(temperature=0)
        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 check_prompt(self, prompt):
        check = f"Here is a paragraph with adjectives. " \
                f"{prompt} " \
                f"Please change all plural forms in the adjectives to singular forms. "
        return self.llm(check)

    def get_imagine_caption(self, image, imagine):
        BLIP_caption = self.get_BLIP_caption(image)
        background_color = self.get_BLIP_vqa(image, 'what is the background color of this image')
        style = self.get_BLIP_vqa(image, 'what is the style of this image')
        imagine_prompt = f"let's pretend you are an excellent painter and now " \
                         f"there is an incomplete painting with {BLIP_caption} in the center, " \
                         f"please imagine the complete painting and describe it" \
                         f"you should consider the background color is {background_color}, the style is {style}" \
                         f"You should make the painting as vivid and realistic as possible" \
                         f"You can not use words like painting or picture" \
                         f"and you should use no more than 50 words to describe it"
        caption = self.llm(imagine_prompt) if imagine else BLIP_caption
        caption = self.check_prompt(caption)
        print(f'BLIP observation: {BLIP_caption}, ChatGPT imagine to {caption}') if imagine else 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

    def merge_masks(self, masks):
        '''
            Args:
                mask (numpy.ndarray): shape N x 1 x H x W
            Outputs:
                new_mask (numpy.ndarray): shape H x W       
        '''
        if type(masks) == torch.Tensor:
            x = masks
        elif type(masks) == np.ndarray:
            x = torch.tensor(masks,dtype=int)
        else:   
            raise TypeError("the type of the input masks must be numpy.ndarray or torch.tensor")
        x = x.squeeze(dim=1)
        value, _ = x.max(dim=0)
        new_mask = value.cpu().numpy()
        new_mask.astype(np.uint8)
        return new_mask
    
    def get_mask(self, image_path, text_prompt):

        print(f"image_path={image_path}, text_prompt={text_prompt}")
        # image_pil (PIL.Image.Image) -> size: W x H
        # image (numpy.ndarray) -> H x W x 3
        image_pil, image = self.grounding.load_image(image_path)

        boxes_filt, pred_phrases = self.grounding.get_grounding_boxes(image, text_prompt)
        image = cv2.imread(image_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        self.sam.sam_predictor.set_image(image)
        
        # masks (torch.tensor) -> N x 1 x H x W 
        masks = self.sam.get_mask_with_boxes(image_pil, image, boxes_filt)

        # merged_mask -> H x W
        merged_mask = self.merge_masks(masks)
        # draw output image

        for mask in masks:
            image = self.sam.show_mask(mask[0].cpu().numpy(), image, random_color=True, transparency=0.3)


        merged_mask_image = Image.fromarray(merged_mask)

        return merged_mask


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

class BackgroundRemoving:
    '''
        using to remove the background of the given picture
    '''
    template_model = True
    def __init__(self,VisualQuestionAnswering:VisualQuestionAnswering, Text2Box:Text2Box, Segmenting:Segmenting):
        self.vqa = VisualQuestionAnswering
        self.obj_segmenting = ObjectSegmenting(Text2Box,Segmenting)

    @prompts(name="Remove the background",
             description="useful when you want to extract the object or remove the background,"
                         "the input should be a string image_path"
                                )
    def inference(self, image_path):
        '''
            given a image, return the picture only contains the extracted main object
        '''
        updated_image_path = None

        mask = self.get_mask(image_path)

        image = Image.open(image_path)
        mask = Image.fromarray(mask)
        image.putalpha(mask)

        updated_image_path = get_new_image_name(image_path, func_name="detect-something")
        image.save(updated_image_path)

        return updated_image_path

    def get_mask(self, image_path):
        '''
            Description:
                given an image path, return the mask of the main object.
            Args:
                image_path (string): the file path of the image
            Outputs:
                mask (numpy.ndarray): H x W
        '''
        vqa_input = f"{image_path}, what is the main object in the image?"
        text_prompt = self.vqa.inference(vqa_input)

        mask = self.obj_segmenting.get_mask(image_path,text_prompt)

        return mask


class ConversationBot:
    def __init__(self, load_dict):
        # load_dict = {'VisualQuestionAnswering':'cuda:0', 'ImageCaptioning':'cuda:1',...}
        print(f"Initializing VisualChatGPT, load_dict={load_dict}")
        if 'ImageCaptioning' not in load_dict:
            raise ValueError("You have to load ImageCaptioning as a basic function for VisualChatGPT")

        self.models = {}
        # Load Basic Foundation Models
        for class_name, device in load_dict.items():
            self.models[class_name] = globals()[class_name](device=device)

        # Load Template Foundation Models
        for class_name, module in globals().items():
            if getattr(module, 'template_model', False):
                template_required_names = {k for k in inspect.signature(module.__init__).parameters.keys() if k!='self'}
                loaded_names = set([type(e).__name__ for e in self.models.values()])
                if template_required_names.issubset(loaded_names):
                    self.models[class_name] = globals()[class_name](
                        **{name: self.models[name] for name in template_required_names})
        
        print(f"All the Available Functions: {self.models}")

        self.tools = []
        for instance in self.models.values():
            for e in dir(instance):
                if e.startswith('inference'):
                    func = getattr(instance, e)
                    self.tools.append(Tool(name=func.name, description=func.description, func=func))
        self.llm = OpenAI(temperature=0)
        self.memory = ConversationBufferMemory(memory_key="chat_history", output_key='output')

    def init_agent(self, lang):
        self.memory.clear() #clear previous history
        if lang=='English':
            PREFIX, FORMAT_INSTRUCTIONS, SUFFIX = VISUAL_CHATGPT_PREFIX, VISUAL_CHATGPT_FORMAT_INSTRUCTIONS, VISUAL_CHATGPT_SUFFIX
            place = "Enter text and press enter, or upload an image"
            label_clear = "Clear"
        else:
            PREFIX, FORMAT_INSTRUCTIONS, SUFFIX = VISUAL_CHATGPT_PREFIX_CN, VISUAL_CHATGPT_FORMAT_INSTRUCTIONS_CN, VISUAL_CHATGPT_SUFFIX_CN
            place = "输入文字并回车，或者上传图片"
            label_clear = "清除"
        self.agent = initialize_agent(
            self.tools,
            self.llm,
            agent="conversational-react-description",
            verbose=True,
            memory=self.memory,
            return_intermediate_steps=True,
            agent_kwargs={'prefix': PREFIX, 'format_instructions': FORMAT_INSTRUCTIONS,
                          'suffix': SUFFIX}, )
        return gr.update(visible = True), gr.update(visible = False), gr.update(placeholder=place), gr.update(value=label_clear)

    def run_text(self, text, state):
        self.agent.memory.buffer = cut_dialogue_history(self.agent.memory.buffer, keep_last_n_words=500)
        res = self.agent({"input": text.strip()})
        res['output'] = res['output'].replace("\\", "/")
        response = re.sub('(image/[-\w]*.png)', lambda m: f'![](file={m.group(0)})*{m.group(0)}*', res['output'])
        state = state + [(text, response)]
        print(f"\nProcessed run_text, Input text: {text}\nCurrent state: {state}\n"
              f"Current Memory: {self.agent.memory.buffer}")
        return state, state

    def run_image(self, image, state, txt, lang):
        image_filename = os.path.join('image', f"{str(uuid.uuid4())[:8]}.png")
        print("======>Auto Resize Image...")
        img = Image.open(image.name)
        width, height = img.size
        ratio = min(512 / width, 512 / height)
        width_new, height_new = (round(width * ratio), round(height * ratio))
        width_new = int(np.round(width_new / 64.0)) * 64
        height_new = int(np.round(height_new / 64.0)) * 64
        img = img.resize((width_new, height_new))
        img = img.convert('RGB')
        img.save(image_filename, "PNG")
        print(f"Resize image form {width}x{height} to {width_new}x{height_new}")
        description = self.models['ImageCaptioning'].inference(image_filename)
        if lang == 'Chinese':
            Human_prompt = f'\nHuman: 提供一张名为 {image_filename}的图片。它的描述是: {description}。 这些信息帮助你理解这个图像，但是你应该使用工具来完成下面的任务，而不是直接从我的描述中想象。 如果你明白了, 说 \"收到\". \n'
            AI_prompt = "收到。  "
        else:
            Human_prompt = f'\nHuman: provide a figure named {image_filename}. The description is: {description}. This information helps you to understand this image, but you should use tools to finish following tasks, rather than directly imagine from my description. If you understand, say \"Received\". \n'
            AI_prompt = "Received.  "
        self.agent.memory.buffer = self.agent.memory.buffer + Human_prompt + 'AI: ' + AI_prompt
        state = state + [(f"![](file={image_filename})*{image_filename}*", AI_prompt)]
        print(f"\nProcessed run_image, Input image: {image_filename}\nCurrent state: {state}\n"
              f"Current Memory: {self.agent.memory.buffer}")
        return state, state, f'{txt} {image_filename} '


if __name__ == '__main__':
    if not os.path.exists("checkpoints"):
        os.mkdir("checkpoints")
    parser = argparse.ArgumentParser()
    parser.add_argument('--load', type=str, default="ImageCaptioning_cuda:0,Text2Image_cuda:0")
    args = parser.parse_args()
    load_dict = {e.split('_')[0].strip(): e.split('_')[1].strip() for e in args.load.split(',')}
    bot = ConversationBot(load_dict=load_dict)
    with gr.Blocks(css="#chatbot .overflow-y-auto{height:500px}") as demo:
        lang = gr.Radio(choices = ['Chinese','English'], value=None, label='Language')
        chatbot = gr.Chatbot(elem_id="chatbot", label="Visual ChatGPT")
        state = gr.State([])
        with gr.Row(visible=False) as input_raws:
            with gr.Column(scale=0.7):
                txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter, or upload an image").style(
                    container=False)
            with gr.Column(scale=0.15, min_width=0):
                clear = gr.Button("Clear")
            with gr.Column(scale=0.15, min_width=0):
                btn = gr.UploadButton(label="🖼️",file_types=["image"])

        lang.change(bot.init_agent, [lang], [input_raws, lang, txt, clear])
        txt.submit(bot.run_text, [txt, state], [chatbot, state])
        txt.submit(lambda: "", None, txt)
        btn.upload(bot.run_image, [btn, state, txt, lang], [chatbot, state, txt])
        clear.click(bot.memory.clear)
        clear.click(lambda: [], None, chatbot)
        clear.click(lambda: [], None, state)
    demo.launch(server_name="0.0.0.0", server_port=7861)



File: LowCodeLLM/src/executingLLM.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

from openAIWrapper import OpenAIWrapper

EXECUTING_LLM_PREFIX = """Executing LLM is designed to provide outstanding responses.
Executing LLM will be given a overall task as the background of the conversation between the Executing LLM and human.
When providing response, Executing LLM MUST STICTLY follow the provided standard operating procedure (SOP).
the SOP is formatted as:
'''
STEP 1: [step name][step descriptions][[[if 'condition1'][Jump to STEP]], [[if 'condition2'][Jump to STEP]], ...]
'''
here "[[[if 'condition1'][Jump to STEP n]]]" is judgmental logic. It means when you're performing this step, and if 'condition1' is satisfied, you will perform STEP n next.

Remember: 
Executing LLM is facing a real human, who does not know what SOP is. 
So, Do not show him/her the SOP steps you are following, or it will make him/her confused. Just response the answer.
"""

EXECUTING_LLM_SUFFIX = """
Remember: 
Executing LLM is facing a real human, who does not know what SOP is. 
So, Do not show him/her the SOP steps you are following, or it will make him/her confused. Just response the answer.
"""

class executingLLM:
    def __init__(self, temperature) -> None:
        self.prefix = EXECUTING_LLM_PREFIX
        self.suffix = EXECUTING_LLM_SUFFIX
        self.LLM = OpenAIWrapper(temperature)
        self.messages = [{"role": "system", "content": "You are a helpful assistant."},
                         {"role": "system", "content": self.prefix}]

    def execute(self, current_prompt, history):
        ''' provide LLM the dialogue history and the current prompt to get response '''
        messages = self.messages + history
        messages.append({'role': 'user', "content": current_prompt + self.suffix})
        response, status = self.LLM.run(messages)
        if status:
            return response
        else:
            return "OpenAI API error."


File: LowCodeLLM/src/openAIWrapper.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

import os
import openai

class OpenAIWrapper:
    def __init__(self, temperature):
        self.key = os.environ.get("OPENAIKEY")
        openai.api_key = self.key

        # Access the USE_AZURE environment variable
        self.use_azure = os.environ.get('USE_AZURE')

        # Check if USE_AZURE is defined
        if self.use_azure is not None:
            # Convert the USE_AZURE value to boolean
            self.use_azure = self.use_azure.lower() == 'true'
        else:
            self.use_azure = False

        if self.use_azure:
            openai.api_type = "azure"
            self.api_base = os.environ.get('API_BASE')
            openai.api_base = self.api_base
            self.api_version = os.environ.get('API_VERSION')
            openai.api_version = self.api_version
            self.engine = os.environ.get('MODEL')
        else:
            self.chat_model_id = "gpt-3.5-turbo"
            
        self.temperature = temperature
        self.max_tokens = 2048
        self.top_p = 1
        self.time_out = 7
    
    def run(self, prompt):
        return self._post_request_chat(prompt)

    def _post_request_chat(self, messages):
        try:
            if self.use_azure:
                response = openai.ChatCompletion.create(
                    engine=self.engine,
                    messages=messages,
                    temperature=self.temperature,
                    max_tokens=self.max_tokens,
                    frequency_penalty=0,
                    presence_penalty=0
                )
            else:
                response = openai.ChatCompletion.create(
                    model=self.chat_model_id,
                    messages=messages,
                    temperature=self.temperature,
                    max_tokens=self.max_tokens,
                    frequency_penalty=0,
                    presence_penalty=0
                )
            res = response['choices'][0]['message']['content']
            return res, True
        except Exception as e:
            return "", False



File: LowCodeLLM/src/planningLLM.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

import re
import json
from openAIWrapper import OpenAIWrapper

PLANNING_LLM_PREFIX = """Planning LLM is designed to provide a standard operating procedure so that an difficult task will be broken down into several steps, and the task will be easily solved by following these steps.
Planning LLM is a powerful problem-solving assistant, and it only needs to analyze the task and provide standard operating procedure as guidance, but does not need actually to solve the problem.
Sometimes there exists some unknown or undetermined situation, thus judgmental logic is needed: some "conditions" are listed, and the next step that should be carried out if a "condition" is satisfied is also listed. The judgmental logics are not necessary.
Planning LLM MUST only provide standard operating procedure in the following format without any other words:
'''
STEP 1: [step name][step descriptions][[[if 'condition1'][Jump to STEP]], [[[if 'condition1'][Jump to STEP]], [[if 'condition2'][Jump to STEP]], ...]
STEP 2: [step name][step descriptions][[[if 'condition1'][Jump to STEP]], [[[if 'condition1'][Jump to STEP]], [[if 'condition2'][Jump to STEP]], ...]
...
'''

For example:
'''
STEP 1: [Brainstorming][Choose a topic or prompt, and generate ideas and organize them into an outline][]
STEP 2: [Research][Gather information, take notes and organize them into the outline][[[lack of ideas][Jump to STEP 1]]]
...
'''
"""

EXTEND_PREFIX = """
\nsome steps of the SOP provided by Planning LLM are too rough, so Planning LLM can also provide a detailed sub-SOP for the given step.
Remember, Planning LLM take the overall SOP into consideration, and the sub-SOP MUST be consistent with the rest of the steps, and there MUST be no duplication in content between the extension and the original SOP.

For example:
If the overall SOP is:
'''
STEP 1: [Brainstorming][Choose a topic or prompt, and generate ideas and organize them into an outline][]
STEP 2: [Write][write the text][]
'''
If the STEP 2: "write the text" is too rough and needs to be extended, then the response could be:
'''
STEP 2.1: [Write the title][write the title of the essay][]
STEP 2.2: [Write the body][write the body of the essay][[[if lack of materials][Jump to STEP 1]]]
'''
"""

PLANNING_LLM_SUFFIX = """\nRemember: Planning LLM is very strict to the format and NEVER reply any word other than the standard operating procedure.
"""

class planningLLM:
    def __init__(self, temperature) -> None:
        self.prefix = PLANNING_LLM_PREFIX
        self.suffix = PLANNING_LLM_SUFFIX
        self.LLM = OpenAIWrapper(temperature)
        self.messages = [{"role": "system", "content": "You are a helpful assistant."}]

    def get_workflow(self, task_prompt):
        '''
        - input: task_prompt
        - output: workflow (json)
        '''
        messages = self.messages + [{'role': 'user', "content": PLANNING_LLM_PREFIX+'\nThe task is:\n'+task_prompt+PLANNING_LLM_SUFFIX}]
        response, status = self.LLM.run(messages)
        if status:
            return self._txt2json(response)
        else:
            return "OpenAI API error."

    def extend_workflow(self, task_prompt, current_workflow, step):
        messages = self.messages + [{'role': 'user', "content": PLANNING_LLM_PREFIX+'\nThe task is:\n'+task_prompt+PLANNING_LLM_SUFFIX}]
        messages.append({'role': 'user', "content": EXTEND_PREFIX+
                         'The current SOP is:\n'+current_workflow+
                         '\nThe step needs to be extended is:\n'+step+
                         PLANNING_LLM_SUFFIX})
        response, status = self.LLM.run(messages)
        if status:
            return self._txt2json(response)
        else:
            return "OpenAI API error."

    def _txt2json(self, workflow_txt):
        ''' convert the workflow in natural language to json format '''
        workflow = []
        try:
            steps = workflow_txt.split('\n')
            for step in steps:
                if step[0:4] != "STEP":
                    continue
                left_indices = [_.start() for _ in re.finditer("\[", step)]
                right_indices = [_.start() for _ in re.finditer("\]", step)]
                step_id = step[: left_indices[0]-2]
                step_name = step[left_indices[0]+1: right_indices[0]]
                step_description = step[left_indices[1]+1: right_indices[1]]
                jump_str = step[left_indices[2]+1: right_indices[-1]]
                if re.findall(re.compile(r'[A-Za-z]',re.S), jump_str) == []:
                    workflow.append({"stepId": step_id, "stepName": step_name, "stepDescription": step_description, "jumpLogic": [], "extension": []})
                    continue
                jump_logic = []
                left_indices = [_.start() for _ in re.finditer('\[', jump_str)]
                right_indices = [_.start() for _ in re.finditer('\]', jump_str)]
                i = 1
                while i < len(left_indices):
                    jump = {"Condition": jump_str[left_indices[i]+1: right_indices[i-1]], "Target": re.search(r'STEP\s\d', jump_str[left_indices[i+1]+1: right_indices[i]]).group(0)}
                    jump_logic.append(jump)
                    i += 3
                workflow.append({"stepId": step_id, "stepName": step_name, "stepDescription": step_description, "jumpLogic": jump_logic, "extension": []})
            return json.dumps(workflow)
        except:
            print("Format error, please try again.")


File: LowCodeLLM/src/app.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

import os
from flask import Flask, request, send_from_directory
from flask_cors import CORS, cross_origin
from lowCodeLLM import lowCodeLLM
from flask.logging import default_handler
import logging

app = Flask('lowcode-llm', static_folder='', template_folder='')
app.debug = True
llm = lowCodeLLM()
gunicorn_logger = logging.getLogger('gunicorn.error')
app.logger = gunicorn_logger
logging_format = logging.Formatter(
    '%(asctime)s - %(levelname)s - %(filename)s - %(funcName)s - %(lineno)s - %(message)s')
default_handler.setFormatter(logging_format)

@app.route("/")
def index():
    return send_from_directory(".", "index.html")

@app.route('/api/get_workflow', methods=['POST'])
@cross_origin()
def get_workflow():
    try:
        request_content = request.get_json()
        task_prompt = request_content['task_prompt']
        workflow = llm.get_workflow(task_prompt)
        return workflow, 200
    except Exception as e:
        app.logger.error(
            'failed to get_workflow, msg:%s, request data:%s' % (str(e), request.json))
        return {'errmsg': 'internal errors'}, 500

@app.route('/api/extend_workflow', methods=['POST'])
@cross_origin()
def extend_workflow():
    try:
        request_content = request.get_json()
        task_prompt = request_content['task_prompt']
        current_workflow = request_content['current_workflow']
        step = request_content['step']
        sub_workflow = llm.extend_workflow(task_prompt, current_workflow, step)
        return sub_workflow, 200
    except Exception as e:
        app.logger.error(
            'failed to extend_workflow, msg:%s, request data:%s' % (str(e), request.json))
        return {'errmsg': 'internal errors'}, 500

@app.route('/api/execute', methods=['POST'])
@cross_origin()
def execute():
    try:
        request_content = request.get_json()
        task_prompt = request_content['task_prompt']
        confirmed_workflow = request_content['confirmed_workflow']
        curr_input = request_content['curr_input']
        history = request_content['history']
        response = llm.execute(task_prompt,confirmed_workflow, history, curr_input)
        return response, 200
    except Exception as e:
        app.logger.error(
            'failed to execute, msg:%s, request data:%s' % (str(e), request.json))
        return {'errmsg': 'internal errors'}, 500



File: LowCodeLLM/src/lowCodeLLM.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

from planningLLM import planningLLM
from executingLLM import executingLLM
import json

class lowCodeLLM:
    def __init__(self, PLLM_temperature=0.4, ELLM_temperature=0):
        self.PLLM = planningLLM(PLLM_temperature)
        self.ELLM = executingLLM(ELLM_temperature)

    def get_workflow(self, task_prompt):
        return self.PLLM.get_workflow(task_prompt)

    def extend_workflow(self, task_prompt, current_workflow, step=''):
        ''' generate a sub-workflow for one of steps 
            - input: the current workflow, the step needs to extend
            - output: sub-workflow '''
        workflow = self._json2txt(current_workflow)
        return self.PLLM.extend_workflow(task_prompt, workflow, step)

    def execute(self, task_prompt,confirmed_workflow, history, curr_input):
        ''' chat with the workflow-equipped low-code LLM '''
        prompt = [{'role': 'system', "content": 'The overall task you are facing is: '+task_prompt+
                '\nThe standard operating procedure(SOP) is:\n'+self._json2txt(confirmed_workflow)}]
        history = prompt + history
        response = self.ELLM.execute(curr_input, history)
        return response
    
    def _json2txt(self, workflow_json):
        ''' convert the json workflow to text'''
        def json2text_step(step):
            step_res = ""
            step_res += step["stepId"] + ": [" + step["stepName"] + "]"
            step_res += "[" + step["stepDescription"] + "]["
            for jump in step["jumpLogic"]:
                step_res += "[[" + jump["Condition"] + "][" + jump["Target"] + "]],"
            step_res += "]\n"
            return step_res

        workflow_txt = ""
        for step in json.loads(workflow_json):
            workflow_txt += json2text_step(step)
            for substep in step['extension']:
                workflow_txt += json2text_step(substep)
        return workflow_txt


# TaskMatrix

**TaskMatrix** connects ChatGPT and a series of Visual Foundation Models to enable **sending** and **receiving** images during chatting.

See our paper: [<font size=5>Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models</font>](https://arxiv.org/abs/2303.04671)

<a src="https://img.shields.io/badge/%F0%9F%A4%97-Open%20in%20Spaces-blue" href="https://huggingface.co/spaces/microsoft/visual_chatgpt">
    <img src="https://img.shields.io/badge/%F0%9F%A4%97-Open%20in%20Spaces-blue" alt="Open in Spaces">
</a>

<a src="https://colab.research.google.com/assets/colab-badge.svg" href="https://colab.research.google.com/drive/1P3jJqKEWEaeNcZg8fODbbWeQ3gxOHk2-?usp=sharing">
    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab">
</a>

## Updates:
- Now TaskMatrix supports [GroundingDINO](https://github.com/IDEA-Research/GroundingDINO) and [segment-anything](https://github.com/facebookresearch/segment-anything)! Thanks **@jordddan** for his efforts. For the image editing case, `GroundingDINO` is first used to locate bounding boxes guided by given text, then `segment-anything` is used to generate the related mask, and finally stable diffusion inpainting is used to edit image based on the mask. 
    - Firstly, run `python visual_chatgpt.py --load "Text2Box_cuda:0,Segmenting_cuda:0,Inpainting_cuda:0,ImageCaptioning_cuda:0"`
    - Then, say `find xxx in the image` or `segment xxx in the image`. `xxx` is an object. TaskMatrix will return the detection or segmentation result!


- Now TaskMatrix can support Chinese! Thanks to **@Wang-Xiaodong1899** for his efforts.
- We propose the **template** idea in TaskMatrix!
    - A template is a **pre-defined execution flow** that assists ChatGPT in assembling complex tasks involving multiple foundation models. 
    - A template contains the **experiential solution** to complex tasks as determined by humans. 
    - A template can **invoke multiple foundation models** or even **establish a new ChatGPT session**
    - To define a **template**, simply adding a class with attributes `template_model = True`
- Thanks to **@ShengmingYin** and **@thebestannie** for providing a template example in `InfinityOutPainting` class (see the following gif)
    - Firstly, run `python visual_chatgpt.py --load "Inpainting_cuda:0,ImageCaptioning_cuda:0,VisualQuestionAnswering_cuda:0"`
    - Secondly, say `extend the image to 2048x1024` to TaskMatrix!
    - By simply creating an `InfinityOutPainting` template, TaskMatrix can seamlessly extend images to any size through collaboration with existing `ImageCaptioning`, `Inpainting`, and `VisualQuestionAnswering` foundation models, **without the need for additional training**.
- **TaskMatrix needs the effort of the community! We crave your contribution to add new and interesting features!**
<img src="./assets/demo_inf.gif" width="750">


## Insight & Goal:
On the one hand, **ChatGPT (or LLMs)** serves as a **general interface** that provides a broad and diverse understanding of a
wide range of topics. On the other hand, **Foundation Models** serve as **domain experts** by providing deep knowledge in specific domains.
By leveraging **both general and deep knowledge**, we aim at building an AI that is capable of handling various tasks.


## Demo 
<img src="./assets/demo_short.gif" width="750">

##  System Architecture 

 
<p align="center"><img src="./assets/figure.jpg" alt="Logo"></p>


## Quick Start

```
# clone the repo
git clone https://github.com/microsoft/TaskMatrix.git

# Go to directory
cd visual-chatgpt

# create a new environment
conda create -n visgpt python=3.8

# activate the new environment
conda activate visgpt

#  prepare the basic environments
pip install -r requirements.txt
pip install  git+https://github.com/IDEA-Research/GroundingDINO.git
pip install  git+https://github.com/facebookresearch/segment-anything.git

# prepare your private OpenAI key (for Linux)
export OPENAI_API_KEY={Your_Private_Openai_Key}

# prepare your private OpenAI key (for Windows)
set OPENAI_API_KEY={Your_Private_Openai_Key}

# Start TaskMatrix !
# You can specify the GPU/CPU assignment by "--load", the parameter indicates which 
# Visual Foundation Model to use and where it will be loaded to
# The model and device are separated by underline '_', the different models are separated by comma ','
# The available Visual Foundation Models can be found in the following table
# For example, if you want to load ImageCaptioning to cpu and Text2Image to cuda:0
# You can use: "ImageCaptioning_cpu,Text2Image_cuda:0"

# Advice for CPU Users
python visual_chatgpt.py --load ImageCaptioning_cpu,Text2Image_cpu

# Advice for 1 Tesla T4 15GB  (Google Colab)                       
python visual_chatgpt.py --load "ImageCaptioning_cuda:0,Text2Image_cuda:0"
                                
# Advice for 4 Tesla V100 32GB                            
python visual_chatgpt.py --load "Text2Box_cuda:0,Segmenting_cuda:0,
    Inpainting_cuda:0,ImageCaptioning_cuda:0,
    Text2Image_cuda:1,Image2Canny_cpu,CannyText2Image_cuda:1,
    Image2Depth_cpu,DepthText2Image_cuda:1,VisualQuestionAnswering_cuda:2,
    InstructPix2Pix_cuda:2,Image2Scribble_cpu,ScribbleText2Image_cuda:2,
    SegText2Image_cuda:2,Image2Pose_cpu,PoseText2Image_cuda:2,
    Image2Hed_cpu,HedText2Image_cuda:3,Image2Normal_cpu,
    NormalText2Image_cuda:3,Image2Line_cpu,LineText2Image_cuda:3"

```

## GPU memory usage
Here we list the GPU memory usage of each visual foundation model, you can specify which one you like:

| Foundation Model        | GPU Memory (MB) |
|------------------------|-----------------|
| ImageEditing           | 3981            |
| InstructPix2Pix        | 2827            |
| Text2Image             | 3385            |
| ImageCaptioning        | 1209            |
| Image2Canny            | 0               |
| CannyText2Image        | 3531            |
| Image2Line             | 0               |
| LineText2Image         | 3529            |
| Image2Hed              | 0               |
| HedText2Image          | 3529            |
| Image2Scribble         | 0               |
| ScribbleText2Image     | 3531            |
| Image2Pose             | 0               |
| PoseText2Image         | 3529            |
| Image2Seg              | 919             |
| SegText2Image          | 3529            |
| Image2Depth            | 0               |
| DepthText2Image        | 3531            |
| Image2Normal           | 0               |
| NormalText2Image       | 3529            |
| VisualQuestionAnswering| 1495            |

## Acknowledgement
We appreciate the open source of the following projects:

[Hugging Face](https://github.com/huggingface) &#8194;
[LangChain](https://github.com/hwchase17/langchain) &#8194;
[Stable Diffusion](https://github.com/CompVis/stable-diffusion) &#8194; 
[ControlNet](https://github.com/lllyasviel/ControlNet) &#8194; 
[InstructPix2Pix](https://github.com/timothybrooks/instruct-pix2pix) &#8194; 
[CLIPSeg](https://github.com/timojl/clipseg) &#8194;
[BLIP](https://github.com/salesforce/BLIP) &#8194;

## Contact Information
For help or issues using the TaskMatrix, please submit a GitHub issue.

For other communications, please contact Chenfei WU (chewu@microsoft.com) or Nan DUAN (nanduan@microsoft.com).

## Trademark Notice

Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

## Disclaimer
The recommended models in this Repo are just examples, used for scientific research exploring the concept of task automation and benchmarking with the paper published at [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://arxiv.org/abs/2303.04671). Users can replace the models in this Repo according to their research needs. When using the recommended models in this Repo, you need to comply with the licenses of these models respectively. Microsoft shall not be held liable for any infringement of third-party rights resulting from your usage of this repo. Users agree to defend, indemnify and hold Microsoft harmless from and against all damages, costs, and attorneys' fees in connection with any claims arising from this Repo. If anyone believes that this Repo infringes on your rights, please notify the project owner [email](chewu@microsoft.com).
