********************************************************************************
Step 1
********************************************************************************

------Action------
THOUGHT:
None
ACTION TYPE:
None
ACTION CONTENT:
None

-----Response-----
RESPONSE (User Message):
USER QUERY: I need help with '09966.jpg'. I want to understand what the main object is in this photo. Based on this assessment, I would like to create some new visuals focusing on the identified subject. Lastly, I would like to divide these created images into distinguishable sections, for further analysis. Can you help me do this?
Any files mentioned in the query will be located at /root/input_data. E.g. if the query mentions example.png, it will be located at /root/input_data/example.png.

Use the code base located at `/root/mnm/mnm` to solve this query. Your current directory is `/root/playground`.

If the import path in retrieved code block says `testing/dir_name/file_name.py` and you want to import variable, function or class called `obj` from this file, then import using `from testing.dir_name.file_name import obj`.

The code base path has either been installed via pip or has been already been added to the system path via
```
import sys
sys.path.extend(['/root/mnm'])
```

********************************************************************************
Step 2
********************************************************************************

------Action------
THOUGHT:
None
ACTION TYPE:
None
ACTION CONTENT:
None

-----Response-----
RESPONSE (Invalid Action):
InvalidAction(reason='Action should always contain thought.\nAction type None is not supported.\n')

********************************************************************************
Step 3
********************************************************************************

------Action------
THOUGHT:
First, I need to identify the main object in the photo '09966.jpg'. I will search for an image understanding function that can detect objects and provide labels for them. This will likely be an object detection function that returns bounding boxes and labels for COCO categories. Once the main object is identified, I can proceed to create new visuals focusing on that subject and then divide those images into sections for further analysis.
ACTION TYPE:
search
ACTION CONTENT:
type: FUNCTION AND text: detect objects

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
type: FUNCTION AND text: detect objects

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[377, 411]
type=FUNCTION
content={
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']):
    """
    Performs object detection on an image using a DETR (DEtection TRansformer) model.
    
    Args:
        image: The image on which to perform object detection.
        ckpt: A string representing the model checkpoint to use for object detection. Defaults to a value from MODEL_SELECTION["object_detection"].
    
    Returns:
        A dictionary with two keys:
        - 'image': The processed image.
        - 'objects': A list of dictionaries, each containing a 'bbox' key with the bounding box coordinates and a 'label' key with the object's label.
    
    Raises:
        ImportError: If the required modules 'torch' or 'transformers' are not installed.
    
    Examples:
        >>> result = object_detection(my_image)
        >>> print(result['objects'])
        [{'bbox': [x1, y1, x2, y2], 'label': 'object_label'}, ...]
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[721, 729]
type=FUNCTION
content={
def count(objects, ckpt=None):
    """
    It takes a list of objects and returns the count of the objects.
    """
    objs = objects
    result_formatted = {"number": len(objs)}

    return result_formatted
}
---
file_path=mnm/tool_api.py
lines=[810, 838]
type=FUNCTION
content={
def select_object(objects, object_name, ckpt=None):
    """
    Selects an object from a list of objects based on the input object name.
    
    Args:
        objects (list): A list of dictionaries, where each dictionary represents an object with a "label" key.
        object_name (str): The name of the object to be selected.
        ckpt (optional): A checkpoint or state to resume from, not used in the current implementation.
    
    Returns:
        dict: A dictionary with a single key "object" that maps to the selected object dictionary.
    
    Examples:
        >>> objects_list = [{'label': 'apple'}, {'label': 'banana'}]
        >>> select_object(objects_list, 'apple')
        {'object': {'label': 'apple'}}
    """
    ...
}
---
(22 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 22 classes/functions found in unexpanded results (search again with the same query to see details):
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py
def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py
def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py
def object_processing(file_path): ... # mnm/tool_api.py
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py

********************************************************************************
Step 4
********************************************************************************

------Action------
THOUGHT:
The function `object_detection` from `tool_api.py` seems to be the right choice for detecting objects in the image '09966.jpg'. It returns a list of objects with their bounding boxes and labels. I will use this function to identify the main object in the photo. After identifying the main object, I can use other functions to create new visuals focusing on that subject and then divide those images into sections for further analysis. I will write the code to perform object detection first.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import object_detection

# Load the image
image_path = '/root/input_data/09966.jpg'
with open(image_path, 'rb') as file:
    image_data = file.read()

# Perform object detection
detection_result = object_detection(image_data)

-----Response-----
RESPONSE (Execution Result):
EXECUTION ERROR:
Traceback (most recent call last):
  File "/root/codenav/codenav/environments/code_env.py", line 88, in exec_with_informative_errors
    exec(code_str, global_vars)
  File "<string>", line 9, in <module>
  File "/root/mnm/mnm/tool_api.py", line 392, in object_detection
    inputs = processor(images=image, return_tensors="pt")
  File "/opt/conda/lib/python3.10/site-packages/transformers/image_processing_utils.py", line 551, in __call__
    return self.preprocess(images, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/transformers/models/detr/image_processing_detr.py", line 1286, in preprocess
    images = make_list_of_images(images)
  File "/opt/conda/lib/python3.10/site-packages/transformers/image_utils.py", line 162, in make_list_of_images
    raise ValueError(
ValueError: Invalid image type. Expected either PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray, but got <class 'NoneType'>.

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"object_detection": <function object_detection at 0x7f31150cfc70>, "image_path": /root/input_data/09966.jpg, "file": <_io.BufferedReader name='/root/input_data/09966.jpg'>, "image_data": b'\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x00\x00\x01\x00\x01\x00\x00\xff\xdb\x00C\x00\x08\x06\x06\x07\x06\x05\x08\x07\x07\x07\t\t\x08\n\x0c\x14\r\x0c\x0b\x0b\x0c\x19\x12\x13\x0f\x14\x1d\x1a\x1f\x1e\x1d\x1a\x1c\x1c $.\' ",#\x1c\x1c(7),01444\x1f\'9=82<.342\xff\xdb\x00C\x01\t\t\t\x0c\x0b\x0c\x18\r\r\x182!\x1c!22222222222222222222222222222222222222222222222222\xff\xc0\x00\x11\x08\x01\x90\x01\xf4\x03\x01"\x00\x02\x11\x01\x03\x11\x01\xff\xc4\x00\x1f\x00\x00\x01\x05\x01\x01\x01\x01\x01\x01\x00\x00\x0...}

********************************************************************************
Step 5
********************************************************************************

------Action------
THOUGHT:
The error indicates that the `object_detection` function expects an image in a specific format, but it received `None`. This is likely because I read the image as binary data, but the function expects an image object. I need to use an appropriate image library to open the image and then pass it to the `object_detection` function. I will use the PIL library to open the image correctly and retry the object detection.
ACTION TYPE:
code
ACTION CONTENT:
from PIL import Image
from mnm.tool_api import object_detection

# Open the image using PIL
image_path = '/root/input_data/09966.jpg'
image = Image.open(image_path)

# Perform object detection
detection_result = object_detection(image)

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"Image": <module 'PIL.Image' from '/opt/conda/lib/python3.10/site-packages/PIL/Image.py'>, "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x400 at 0x7F2E884F5ED0>, "detection_result": {'image': <PIL.Image.Image image mode=RGB size=500x400 at 0x7F2E884F5F00>, 'objects': [{'bbox': [-0.18, -0.24, 499.82, 399.75], 'label': 'bed'}, {'bbox': [46.87, 71.25, 341.91, 364.66], 'label': 'cake'}]}}

********************************************************************************
Step 6
********************************************************************************

------Action------
THOUGHT:
Object detection has been performed successfully, and the results contain two objects: 'bed' and 'cake'. Since the user wants to focus on the main object, I will assume the object with the larger bounding box is the main subject. I will calculate the area of the bounding boxes to determine which object is larger. Then, I will proceed to create new visuals focusing on the identified main subject. For this, I will search for functions related to image editing that can generate images based on a description or edit images given the original image and a description of how to modify it.
ACTION TYPE:
search
ACTION CONTENT:
type: FUNCTION AND text: generate images
type: FUNCTION AND text: edit images

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
type: FUNCTION AND text: generate images

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[228, 249]
type=FUNCTION
content={
def image_generation(text: str, ckpt=MODEL_SELECTION["image_generation"]):
    """
    It takes a text prompt and generates an image that matches the text description.
    """
    if ckpt == "stabilityai/stable-diffusion-xl-base-1.0":

        text = text_processing(text)

        from diffusers import DiffusionPipeline

        generator = DiffusionPipeline.from_pretrained(
            pretrained_model_name_or_path=ckpt,
            torch_dtype=torch.float16,
            use_safetensors=True,
            variant="fp16",
        ).to(DEVICE)

        result = generator(prompt=text).images[0]
        result_formatted = {"image": result}

        return result_formatted
}
---
file_path=mnm/tool_api.py
lines=[251, 267]
type=FUNCTION
content={
def image_captioning(
    image, ckpt=MODEL_SELECTION["image_captioning"]
):  # alternative: nlpconnect/vit-gpt2-image-captioning (testing, blip is better than vit-gpt2)z
    """
    It takes an image and generates a text caption of the image.
    """
    image = image_processing(image)

    pipe = pipeline("image-to-text", model=ckpt)

    result = pipe(
        image
    )  # [{'generated_text': 'there is a small white dog sitting next to a cell phone'}]
    result_formatted = {"text": result[0]["generated_text"]}

    return result_formatted
}
---
file_path=mnm/tool_api.py
lines=[348, 375]
type=FUNCTION
content={
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']):
    """
    Processes an image and a question about the image, then generates an answer using a pre-trained model.
    
    Args:
        image: The image to be processed.
        question: The question related to the image.
        ckpt: The checkpoint or model identifier for the pre-trained model. Defaults to MODEL_SELECTION["visual_question_answering"].
    
    Returns:
        A dictionary with a key 'text' containing the generated answer to the question.
    
    Examples:
        >>> answer = visual_question_answering(my_image, "What is in this image?")
        >>> print(answer['text'])
        "A cat sitting on a windowsill."
    """
    ...
}
---
(16 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 16 classes/functions found in unexpanded results (search again with the same query to see details):
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
def get_full_path_data(filename): ... # mnm/tool_api.py
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py

QUERY:
type: FUNCTION AND text: edit images

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[269, 323]
type=FUNCTION
content={
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']):
    """
    Edits an image based on a given text prompt using a specified model checkpoint.
    
    Args:
        image: The input image to be edited.
        prompt (str): The text prompt describing the desired edits.
        ckpt: The model checkpoint to use for image editing. Defaults to MODEL_SELECTION["image_editing"].
    
    Returns:
        A dictionary with the key 'image' containing the edited image.
    
    Raises:
        ImportError: If the necessary libraries for the chosen model are not installed.
    
    Examples:
        # Using the default checkpoint
        edited_image = image_editing(input_image, "A cat with a party hat.")
        
        # Using a specific checkpoint
        edited_image = image_editing(input_image, "A dog on a skateboard.", ckpt="timbrooks/instruct-pix2pix")
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[325, 346]
type=FUNCTION
content={
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']):
    """
    Classifies an image into one of the predefined categories using a Vision Transformer (ViT) model.
    
    Args:
        image: The image to be classified, which will be processed by `image_processing` function.
        ckpt (str, optional): The checkpoint or model identifier to use for loading the pre-trained model. Defaults to `MODEL_SELECTION["image_classification"]`.
    
    Returns:
        dict: A dictionary with a key "text" that maps to the predicted category of the image.
    
    Examples:
        >>> result = image_classification(my_image)
        >>> print(result)
        {'text': 'cat'}
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[985, 1011]
type=FUNCTION
content={
def wikipedia_simple_search(text: str, ckpt=None):
    """
    Performs a basic search on Wikipedia to retrieve a summary of the most relevant page.
    
    Args:
        text (str): The search query text.
        ckpt: Unused parameter, can be anything (default is None).
    
    Returns:
        dict: A dictionary containing the formatted text summary of the most relevant Wikipedia page.
    
    Raises:
        This function may raise exceptions related to network issues or JSON decoding if the Wikipedia API changes or is unreachable.
    
    Examples:
        result = wikipedia_simple_search("Python programming language")
        # result might be something like:
        # {'text': 'Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with its use of significant indentation.'}
    """
    ...
}
---
(All code blocks matching the query were returned.)

********************************************************************************
Step 7
********************************************************************************

------Action------
THOUGHT:
The function `image_editing` from `tool_api.py` is suitable for creating new visuals based on the identified main subject. I will use this function to focus on the 'cake' object, as it has a smaller bounding box and is likely the main subject of interest in the context of the image. After creating a new visual focusing on the 'cake', I will search for a function that can divide images into sections for further analysis.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_editing

# Since the 'cake' has a smaller bounding box, we will assume it's the main subject.
# Create a new visual focusing on the 'cake' object.
prompt = "Focus on the cake"
edited_image_result = image_editing(detection_result['image'], prompt)

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_editing": <function image_editing at 0x7f31150cfac0>, "prompt": Focus on the cake, "edited_image_result": {'image': <PIL.Image.Image image mode=RGB size=496x400 at 0x7F2D6CAA8910>}}

********************************************************************************
Step 8
********************************************************************************

------Action------
THOUGHT:
The image has been successfully edited to focus on the 'cake'. Now, I need to search for a function that can divide the edited image into sections for further analysis. I will look for a function related to image segmentation or any function that can help in dividing the image into parts.
ACTION TYPE:
search
ACTION CONTENT:
type: FUNCTION AND text: divide image
type: FUNCTION AND text: image segmentation

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
type: FUNCTION AND text: divide image

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[678, 719]
type=FUNCTION
content={
def color_pop(image, object, ckpt=None):
    """
    Applies a "color pop" effect to an image, where the specified object is in color and the rest of the image is converted to black and white.
    
    Args:
        image: The input image to be processed.
        object: A dictionary containing the object and its mask. The mask should be a binary mask where the object is white (1) and the rest is black (0).
        ckpt: Optional; a checkpoint for the model, not used in the current implementation.
    
    Returns:
        A dictionary with the key 'image' containing the PIL Image with the color pop effect applied.
    
    Raises:
        ImportError: If the required modules 'cv2' (OpenCV) or 'numpy' are not installed.
    
    Examples:
        # Assuming 'image' is a PIL Image and 'object_mask' is a binary mask of the object
        object_info = {'mask': object_mask}
        result = color_pop(image, object_info)
        result_image = result['image']
        result_image.show()
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[627, 676]
type=FUNCTION
content={
def background_blur(image, object, ckpt=None):
    """
    Applies a background blur effect to an image, keeping specified objects in the foreground sharp.
    
    Args:
        image: A PIL Image object representing the input image.
        object: A dictionary containing the object(s) and their associated mask(s) to keep in focus.
        ckpt: Optional; a checkpoint or model state to use for advanced processing (not used in the current implementation).
    
    Returns:
        A dictionary with a key 'image' containing the resulting PIL Image object with the background blurred.
    
    Raises:
        ImportError: If the required modules (cv2, numpy, PIL) are not available.
    
    Examples:
        # Assuming 'input_image' is a PIL Image and 'foreground_object' is a dictionary with a mask.
        result = background_blur(input_image, foreground_object)
        blurred_image = result['image']
        blurred_image.show()
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[895, 909]
type=FUNCTION
content={
def get_trivia_fact(number: str, ckpt=None):
    """
    It provides interesting trivia facts about number.
    """
    url = f"https://numbersapi.p.rapidapi.com/{number}/trivia"
    params = {"fragment": "true", "json": "true"}
    headers = {
        "X-RapidAPI-Key": RAPID_API_KEY,
        "X-RapidAPI-Host": "numbersapi.p.rapidapi.com",
    }
    response = requests.get(url, headers=headers, params=params)
    result_formatted = response.json()

    return result_formatted
}
---
(5 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 5 out of 5 classes/functions found in unexpanded results (search again with the same query to see details):
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def get_math_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py
def get_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py

QUERY:
type: FUNCTION AND text: image segmentation

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[413, 461]
type=FUNCTION
content={
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']):
    """
    Segments an image into different parts using a pre-trained MaskFormer model and outputs segmentation masks along with additional information for each segment.
    
    Args:
        image: The input image to be segmented.
        ckpt: A string or a dictionary key that specifies the pre-trained model checkpoint to be used for segmentation. Defaults to a pre-selected model checkpoint from a MODEL_SELECTION dictionary.
    
    Returns:
        A dictionary containing the original image and a list of objects. Each object in the list is a dictionary with keys 'mask', 'label', 'bbox', and 'inst_id', representing the segmentation mask, label, bounding box, and instance ID, respectively.
    
    Raises:
        ImportError: If required modules (numpy, torch, transformers, PIL) are not installed.
    
    Examples:
        # Assuming `image` is a PIL image and `MODEL_SELECTION` is defined
        segmentation_result = image_segmentation(image)
        for obj in segmentation_result['objects']:
            print(obj['label'], obj['bbox'])
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[536, 556]
type=FUNCTION
content={
def image_crop_left(image, ckpt=None):
    """
    Crops the left half of the provided image and returns it in a formatted dictionary.
    
    Args:
        image: The image to be cropped.
        ckpt: Optional checkpoint data (unused in the current implementation).
    
    Returns:
        A dictionary containing the cropped image with the key 'image'.
    
    Examples:
        Assuming `image` is a PIL Image object:
        >>> result = image_crop_left(image)
        >>> result['image']  # This will contain the left half of the original image.
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[604, 625]
type=FUNCTION
content={
def image_crop_bottom(image, ckpt=None):
    """
    Crops the bottom half of an image and returns it in a dictionary.
    
    Args:
        image: The image to be cropped.
        ckpt: Optional checkpoint data (unused in the current implementation).
    
    Returns:
        A dictionary containing the cropped image with the key 'image'.
    
    Examples:
        Assuming `image` is a PIL Image object:
        >>> cropped_image_result = image_crop_bottom(image)
        >>> cropped_image = cropped_image_result['image']
    """
    ...
}
---
(12 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 12 classes/functions found in unexpanded results (search again with the same query to see details):
def image_crop_right(image, ckpt=None): ... # mnm/tool_api.py
def image_crop_top(image, ckpt=None): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py
def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py

********************************************************************************
Step 9
********************************************************************************

------Action------
THOUGHT:
The function `image_segmentation` from `tool_api.py` is suitable for dividing the edited image into sections. It uses a pre-trained MaskFormer model to output segmentation masks along with additional information for each segment. I will use this function to segment the edited image that focuses on the 'cake'. After segmentation, I can provide the user with the segmented parts for further analysis.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_segmentation

# Perform image segmentation on the edited image to divide it into sections
segmentation_result = image_segmentation(edited_image_result['image'])

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_segmentation": <function image_segmentation at 0x7f31150cfd00>, "segmentation_result": {'image': <PIL.Image.Image image mode=RGB size=496x400 at 0x7F2EC1D971C0>, 'objects': [{'mask': array([[1., 1., 1., ..., 0., 0., 0.],
       [1., 1., 1., ..., 0., 0., 0.],
       [1., 1., 1., ..., 0., 0., 0.],
       ...,
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.],
       [1., 1., 1., ..., 1., 1., 1.]], dtype=float32), 'label': 'flower', 'bbox': [0, 0, 495, 399], 'inst_id': 1}, {'mask': array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
  ...}

********************************************************************************
Step 10
********************************************************************************

------Action------
THOUGHT:
The image segmentation has been completed successfully, and the segmentation result contains the segmented objects with their masks, labels, and bounding boxes. The user can now use these segmented parts for further analysis. I will summarize the steps taken to solve the user's query.
ACTION TYPE:
code_summary
ACTION CONTENT:
from PIL import Image
from mnm.tool_api import object_detection, image_editing, image_segmentation
image_path = '/root/input_data/09966.jpg'
image = Image.open(image_path)
detection_result = object_detection(image)
prompt = "Focus on the cake"
edited_image_result = image_editing(detection_result['image'], prompt)
segmentation_result = image_segmentation(edited_image_result['image'])

-----Response-----
RESPONSE (User Message):
Summary received and stored.

********************************************************************************
Step 11
********************************************************************************

------Action------
THOUGHT:
The user has acknowledged the receipt of the summary. The task is complete.
ACTION TYPE:
done
ACTION CONTENT:
True

-----Response-----
None