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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: I just ordered a painting based on the description {'text': 'this woman is sitting on a wood bench'}. Once it's generated, help me identify and highlight the wood bench in the artwork.
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 `mnm` codebase that can generate an image based on a text description. Once the image is generated, I will then need to search for functions that can detect objects in the image and highlight a specific object, in this case, a wood bench.
ACTION TYPE:
search
ACTION CONTENT:
text: generate image from text description
text: detect objects
text: highlight object

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: generate image from text description

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=[195, 226]
type=FUNCTION
content={
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']):
    """
    Performs automatic speech recognition on an audio file using a pre-trained model.
    
    Args:
        audio (str): The file path to the audio file to be transcribed.
        ckpt (str, optional): The checkpoint identifier of the pre-trained model to use. Defaults to the model specified in MODEL_SELECTION["automatic_speech_recognition"].
    
    Returns:
        dict: A dictionary containing the transcription of the audio file with the key 'text'.
    
    Raises:
        ImportError: If the required libraries (librosa, transformers) are not installed.
    
    Examples:
        >>> transcription = automatic_speech_recognition("path/to/audio/file.wav")
        >>> print(transcription)
        {'text': 'The transcribed text of the audio.'}
    """
    ...
}
---
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
}
---
(42 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 35 classes/functions found in unexpanded results (search again with the same query to see details):
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def get_full_path_data(filename): ... # 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

QUERY:
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'}}
    """
    ...
}
---
(32 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 29 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

QUERY:
text: highlight object

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[731, 765]
type=FUNCTION
content={
def tag(image, objects, ckpt=None):
    """
    Tags objects in an image with bounding boxes and class labels.
    
    Args:
        image: The image to be processed and tagged.
        objects: A list of dictionaries, each containing 'bbox' (bounding box) and 'label' (class) for an object.
        ckpt (optional): A checkpoint or model state to use for tagging, not used in the current implementation.
    
    Returns:
        A dictionary with a key 'image' containing the tagged image.
    
    Examples:
        # Assuming 'image' is an image object and 'objects' is a list of object dictionaries with 'bbox' and 'label'
        result = tag(image, objects)
        tagged_image = result['image']
    """
    ...
}
---
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']
    """
    ...
}
---
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()
    """
    ...
}
---
(24 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 21 classes/functions found in unexpanded results (search again with the same query to see details):
def object_processing(file_path): ... # 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 image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_generation` from `mnm/tool_api.py` can be used to generate an image based on a text description. After generating the image, the function `object_detection` can be used to detect objects in the image. Once the wood bench is detected, the function `tag` can be used to highlight it in the image. I will now write the code to generate the image and then detect and highlight the wood bench.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation, object_detection, tag

# Generate an image based on the text description
description = {'text': 'this woman is sitting on a wood bench'}
generated_image_result = image_generation(description['text'])

# Detect objects in the generated image
detected_objects_result = object_detection(generated_image_result['image'])

# Find the wood bench among the detected objects
wood_bench = next((obj for obj in detected_objects_result['objects'] if 'bench' in obj['label'].lower()), None)

# If a wood bench is detected, highlight it in the image
if wood_bench:
    highlighted_image_result = tag(generated_image_result['image'], [wood_bench])
else:
    highlighted_image_result = None

highlighted_image_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_generation": <function image_generation at 0x7f38ccbb30a0>, "object_detection": <function object_detection at 0x7f38ccbb3370>, "tag": <function tag at 0x7f38ccbb39a0>, "description": {'text': 'this woman is sitting on a wood bench'}, "generated_image_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F34EAE4C0A0>}, "detected_objects_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F353F9B8220>, 'objects': [{'bbox': [140.05, 122.51, 892.81, 930.98], 'label': 'person'}, {'bbox': [-0.27, 528.47, 1023.62, 1014.59], 'label': 'bench'}]}, "wood_bench": {'bbox': [-0.27, 528.47, 1023.62, 1014.59], 'label': 'bench'}, "highlighted_image_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F34EB1CF370>}}

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

------Action------
THOUGHT:
The code executed successfully, and the relevant variables indicate that the wood bench was detected and highlighted in the generated image. The `highlighted_image_result` contains the final image with the wood bench highlighted. I will now provide a summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import image_generation, object_detection, tag
output0 = image_generation(text='this woman is sitting on a wood bench')
output1 = object_detection(image=output0['image'])
wood_bench = next((obj for obj in output1['objects'] if 'bench' in obj['label'].lower()), None)
output2 = tag(image=output0['image'], objects=[wood_bench]) if wood_bench else None

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

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

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

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