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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I'm working on a project about animals and their habitats. Using a visualization where we have two brown and black birds staring on a curved wire would be helpful. Once you have generated that image, I would like to isolate the birds for further study. Please identify and highlight the bird objects in the image for me."
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 generate an image with the description provided by the user. I will search for the function in the `mnm` codebase that can generate images from text descriptions. Once the image is generated, I will then search for functions that can detect and highlight objects in an image, which in this case are the birds.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: generate image)
(type: FUNCTION) AND (text: detect objects)
(type: FUNCTION) AND (text: highlight objects)

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

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=[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=[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."
    """
    ...
}
---
(27 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 27 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 tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py

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'}}
    """
    ...
}
---
(29 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:
(type: FUNCTION) AND (text: highlight objects)

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

Prototypes for the next 7 out of 26 classes/functions found in unexpanded results (search again with the same query to see details):
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
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 the `mnm` codebase can be used to generate an image based on a text description. After generating the image, I can use the `object_detection` function to detect objects in the image. Finally, the `tag` function can be used to highlight the detected objects, which in this case are the birds. I will now write the code to generate the image and then proceed with object detection and tagging.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation, object_detection, tag

# Generate an image with the description provided by the user
description = "two brown and black birds staring on a curved wire"
generated_image_result = image_generation(text=description)

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

# Highlight the bird objects in the image
highlighted_birds_result = tag(image=generated_image_result['image'], objects=detected_objects_result['objects'])

-----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 0x7f9d7660f400>, "object_detection": <function object_detection at 0x7f9d7660f6d0>, "tag": <function tag at 0x7f9d7660fd00>, "description": two brown and black birds staring on a curved wire, "generated_image_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F9D5DD75F30>}, "detected_objects_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F9E3643C8B0>, 'objects': [{'bbox': [646.18, 173.29, 899.75, 785.67], 'label': 'bird'}, {'bbox': [281.35, 143.34, 640.49, 894.5], 'label': 'bird'}, {'bbox': [641.68, 174.89, 901.38, 545.08], 'label': 'bird'}]}, "highlighted_birds_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F9EF8916320>}}

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

------Action------
THOUGHT:
The code has been executed without error, and the relevant variables indicate that the image has been generated, objects (birds) have been detected, and the birds have been highlighted in the image. The task is complete, and 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='two brown and black birds staring on a curved wire')
output1 = object_detection(image=output0['image'])
output2 = tag(image=output0['image'], objects=output1['objects'])

-----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