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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I have a picture identified as '270650-input.png'. I wish to add skis to the wheel of the vehicle in the photo. After modifying the image, I would like to obtain the lower half for use in a promotional material. Please also provide a suitable caption that can give context to the modified image."
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 that can edit the image by adding skis to the wheel of the vehicle. After that, I will need to find a function to crop the image to obtain the lower half. Finally, I will search for a function that can generate a suitable caption for the modified image. I will start by searching for the image editing function that can modify the image as per the user's request.
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 function `image_editing` from `tool_api.py` seems suitable for editing the image by adding skis to the wheel of the vehicle based on a text prompt. Next, I will search for the `image_crop_bottom` function to obtain the lower half of the image for promotional material. Lastly, I will search for the `image_captioning` function to generate a suitable caption for the modified image.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: image_crop_bottom)
(type: FUNCTION) AND (text: image_captioning)

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

CODE BLOCKS:
---
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']
    """
    ...
}
---
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=[581, 602]
type=FUNCTION
content={
def image_crop_top(image, ckpt=None):
    """
    Crops the top 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:
        >>> from PIL import Image
        >>> original_image = Image.open('path/to/image.jpg')
        >>> cropped_image_info = image_crop_top(original_image)
        >>> cropped_image = cropped_image_info['image']
    """
    ...
}
---
(18 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 18 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(image, object, ckpt=None): ... # mnm/tool_api.py
def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py

QUERY:
(type: FUNCTION) AND (text: image_captioning)

CODE BLOCKS:
---
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=[463, 489]
type=FUNCTION
content={
def optical_character_recognition(
    image, ckpt=MODEL_SELECTION["optical_character_recognition"]
):
    """
    It takes an image and outputs recognized texts in the image.
    """
    import easyocr

    if isinstance(image, str):
        image_path = image if os.path.exists(image) else get_full_path_data(image)
    else:
        image = image_processing(image)
        image_path = os.path.join(MNM_BASE_PATH_DATA, "ocr_temp.jpg")
        image.save(image_path)

    assert ckpt == "easyOCR"  # only support easyocr for now
    reader = easyocr.Reader(
        ["en"]
    )  # this needs to run only once to load the model into memory
    result = reader.readtext(image_path)
    result_text = [
        text for _, text, _ in result
    ]  # Extract only the text from the result
    result_formatted = {"text": ", ".join(result_text)}

    return result_formatted
}
---
file_path=mnm/tool_api.py
lines=[558, 579]
type=FUNCTION
content={
def image_crop_right(image, ckpt=None):
    """
    Crops the right half of the provided image.
    
    Args:
        image: The image to be cropped.
        ckpt: Optional; a checkpoint or reference point for the cropping process, not used in the current implementation.
    
    Returns:
        A dictionary containing the cropped image under the key 'image'.
    
    Examples:
        Assuming `img` is a PIL Image object:
        >>> cropped_result = image_crop_right(img)
        >>> cropped_image = cropped_result['image']
    """
    ...
}
---
(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 image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
I have found the necessary functions to perform the tasks. The `image_editing` function will be used to add skis to the wheel of the vehicle in the image. The `image_crop_bottom` function will crop the lower half of the edited image. Finally, the `image_captioning` function will generate a caption for the modified image. I will now write the code to execute these functions step by step.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_editing, image_crop_bottom, image_captioning

# Load the image
image_path = '/root/input_data/270650-input.png'

# Edit the image to add skis to the wheel of the vehicle
edit_prompt = 'Add skis to the wheel of the vehicle in the photo.'
edited_image_result = image_editing(image_path, edit_prompt)
edited_image = edited_image_result['image']

# Crop the bottom half of the edited image
cropped_image_result = image_crop_bottom(edited_image)
cropped_image = cropped_image_result['image']

# Generate a caption for the cropped image
caption_result = image_captioning(cropped_image)
caption = caption_result['text']

# Prepare the results
results = {
    'edited_image': edited_image,
    'cropped_image': cropped_image,
    'caption': caption
}

-----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 0x7f751332f250>, "image_crop_bottom": <function image_crop_bottom at 0x7f751332f7f0>, "image_captioning": <function image_captioning at 0x7f751332f1c0>, "image_path": /root/input_data/270650-input.png, "edit_prompt": Add skis to the wheel of the vehicle in the photo., "edited_image_result": {'image': <PIL.Image.Image image mode=RGB size=496x496 at 0x7F74D198DA80>}, "edited_image": <PIL.Image.Image image mode=RGB size=496x496 at 0x7F74D198DA80>, "cropped_image_result": {'image': <PIL.Image.Image image mode=RGB size=495x247 at 0x7F74D1AC9DB0>}, "cropped_image": <PIL.Image.Image image mode=RGB size=495x247 at 0x7F74D1AC9DB0>, "caption_result": {'text': 'a close up of a yellow car parked on a street'}, "caption": a close up of a yellow car parked on a street, "results": {'edited_image': <PIL.Image.Image image mode=RGB size=496x496 at 0x7F74D198DA80>, 'cropped_image': <PIL.Image.Image image mode=RGB size=495x247 at 0x7F74D1AC9DB0>, 'caption': 'a close up of a yellow car parked on a street'}}

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

------Action------
THOUGHT:
The code has been executed without errors, and the relevant variables indicate that the image has been edited, cropped, and a caption has been generated. The task is now complete.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import image_editing, image_crop_bottom, image_captioning
edited_image_result = image_editing(image='/root/input_data/270650-input.png', prompt='Add skis to the wheel of the vehicle in the photo.')
cropped_image_result = image_crop_bottom(image=edited_image_result['image'])
caption_result = image_captioning(image=cropped_image_result['image'])

-----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 now complete.
ACTION TYPE:
done
ACTION CONTENT:
True

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