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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I have an image '270650-input.png', and I want to add skis on the wheels in the photo. After that, can you divide the edited image into distinctive sections and tell me how many different parts there are?"
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:
First, I need to find a function in the codebase that can edit the image by adding skis to the wheels. After that, I will need to find a function that can segment the edited image into distinctive sections and count the different parts. I will start by searching for a function that can edit images based on a description.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: edit images)

-----Response-----
RESPONSE (Retrieval Result):
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=[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
}
---
(23 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 23 classes/functions found in unexpanded results (search again with the same query to see details):
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py
def image_crop_left(image, ckpt=None): ... # mnm/tool_api.py
def image_crop_top(image, ckpt=None): ... # mnm/tool_api.py
def image_crop_bottom(image, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The `image_editing` function from the `tool_api.py` file seems to be the right choice for editing the image based on a text description. I will use this function to add skis to the wheels in the photo. After editing the image, I will need to segment it into distinctive sections. The search results also mention an `image_segmentation` function, which I will search for next to get its details for segmenting the edited image.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: image_segmentation)

-----Response-----
RESPONSE (Retrieval Result):
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=[87, 97]
type=FUNCTION
content={
def image_processing(img):
    # different models have different requirements for image input, so we will do preprocessing in each function
    if isinstance(img, Image.Image):
        return img.convert("RGB")
    elif isinstance(img, str):
        if os.path.exists(img):
            return Image.open(img).convert("RGB")
        else:
            full_path = get_full_path_data(img)
            return Image.open(full_path).convert("RGB")
}
---
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
}
---
(28 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 28 classes/functions found in unexpanded results (search again with the same query to see details):
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
I have found the `image_segmentation` function which can be used to segment the edited image into distinctive sections. Now, I will write the code to first edit the image by adding skis to the wheels using the `image_editing` function and then segment the edited image using the `image_segmentation` function. After segmentation, I will count the number of different parts by counting the number of objects returned by the segmentation function.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_editing, image_segmentation

# Path to the input image
input_image_path = '/root/input_data/270650-input.png'

# Edit the image by adding skis to the wheels
edited_image_result = image_editing(image=input_image_path, prompt="Add skis on the wheels in the photo.")

# Check if the image was edited successfully
if 'image' in edited_image_result:
    edited_image = edited_image_result['image']
    
    # Segment the edited image into distinctive sections
    segmentation_result = image_segmentation(image=edited_image)
    
    # Count the number of different parts
    if 'objects' in segmentation_result:
        number_of_parts = len(segmentation_result['objects'])
    else:
        number_of_parts = 0
else:
    raise Exception("Image editing failed, 'image' key not found in the result.")

-----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 0x7f2c0f9630a0>, "image_segmentation": <function image_segmentation at 0x7f2c0f9632e0>, "input_image_path": /root/input_data/270650-input.png, "edited_image_result": {'image': <PIL.Image.Image image mode=RGB size=496x496 at 0x7F28CDEDC430>}, "edited_image": <PIL.Image.Image image mode=RGB size=496x496 at 0x7F28CDEDC430>, "segmentation_result": {'image': <PIL.Image.Image image mode=RGB size=496x496 at 0x7F28CD35D120>, 'objects': [{'mask': array([[0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       ...,
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.],
       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32), 'label': 'truck', 'bbox': [0, 7, 294, 232], 'inst_id': 1}, {'mask': array([[1., 1., 1., ..., 0., 0., 0.],
       [1., 1., 1., ..., 0., 0., 0.],
   ..., "number_of_parts": 4}

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

------Action------
THOUGHT:
The code has been executed without error, and the relevant variables indicate that the image was successfully edited and segmented into 4 different parts. I will now summarize the solution using the code_summary action.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import image_editing, image_segmentation
edited_image_result = image_editing(image='/root/input_data/270650-input.png', prompt="Add skis on the wheels in the photo.")
segmentation_result = image_segmentation(image=edited_image_result['image'])
number_of_parts = len(segmentation_result['objects'])

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

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

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

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