********************************************************************************
Step 1
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------Action------
THOUGHT:
None
ACTION TYPE:
None
ACTION CONTENT:
None

-----Response-----
RESPONSE (User Message):
USER QUERY: I have a quirky image 2382602.jpg. I want to make it a bit funnier. Could you please find the motorcycle in the image and replace it with a goblin emoji? 
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 detect objects in an image and return their bounding boxes and labels. Once I have the bounding box for the motorcycle, I can use another function to replace it with a goblin emoji. I will start by searching for the object detection function in the codebase.
ACTION TYPE:
search
ACTION CONTENT:
type: FUNCTION AND text: detect AND text: objects

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
type: FUNCTION AND text: detect AND text: 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'}}
    """
    ...
}
---
(21 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 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 3
********************************************************************************

------Action------
THOUGHT:
The `object_detection` function from `tool_api.py` seems to be the right choice for detecting objects in the image. It returns a list of objects with their bounding boxes and labels. After detecting the motorcycle, I can use the `emoji` function, which is mentioned in the prototypes, to replace the motorcycle with a goblin emoji. I will now search for the `emoji` function to understand how to use it.
ACTION TYPE:
search
ACTION CONTENT:
type: FUNCTION AND text: emoji

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

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[767, 808]
type=FUNCTION
content={
def emoji(image, object, emoji, ckpt=None):
    """
    Replaces objects in an image with a specified emoji.
    
    Args:
        image: The image in which objects are to be replaced with an emoji.
        object: A dictionary containing the bounding box coordinates of the object(s) to be replaced.
        emoji: The name of the emoji to use for replacement.
        ckpt: Optional; a checkpoint for loading the model, not used in the current implementation.
    
    Returns:
        A dictionary with the key 'image' containing the image with the objects replaced by the specified emoji.
    
    Raises:
        FileNotFoundError: If the specified emoji file does not exist in the EMOJI_DIR.
    
    Examples:
        result = emoji(image, {'bbox': [50, 50, 100, 100]}, 'smiling_face')
        # This will replace the object in the image within the bounding box [50, 50, 100, 100] with the 'smiling_face' emoji.
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[491, 534]
type=FUNCTION
content={
def image_crop(image, object, ckpt=None):
    """
    Crops an image to the region within a specified bounding box.
    
    Args:
        image: The image to be cropped.
        object: A dictionary containing the bounding box coordinates with the key 'bbox'.
        ckpt: Optional; a checkpoint or state to use for additional processing (not used in the current implementation).
    
    Returns:
        A dictionary with the key 'image' containing the cropped image.
    
    Raises:
        ValueError: If the bounding box is a string and cannot be converted to a list of coordinates.
    
    Examples:
        # Assuming `img` is an image object and `obj` is a dict with bbox coordinates
        cropped_image_result = image_crop(img, obj)
        cropped_image = cropped_image_result['image']
    """
    ...
}
---
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
}
---
(All code blocks matching the query were returned.)

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

------Action------
THOUGHT:
I have found the `emoji` function which can be used to replace objects in an image with a specified emoji. The function requires the image, the object's bounding box, and the name of the emoji. Now, I will write the code to first detect the motorcycle using `object_detection` and then replace it with a goblin emoji using the `emoji` function.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import object_detection, select_object, emoji

# Load the image
image_path = '/root/input_data/2382602.jpg'

# Perform object detection to find the motorcycle
detection_result = object_detection(image_path)

# Select the motorcycle object from the detection results
motorcycle_object = select_object(detection_result['objects'], 'motorcycle')

# Replace the motorcycle with a goblin emoji
result_image = emoji(image_path, motorcycle_object['object'], 'goblin')

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"object_detection": <function object_detection at 0x7f9e8f6db0a0>, "select_object": <function select_object at 0x7f9e8f6db7f0>, "emoji": <function emoji at 0x7f9e8f6db760>, "image_path": /root/input_data/2382602.jpg, "detection_result": {'image': <PIL.Image.Image image mode=RGB size=500x378 at 0x7F9C2D8EBFA0>, 'objects': [{'bbox': [85.54, 240.69, 192.98, 310.74], 'label': 'motorcycle'}, {'bbox': [98.11, 86.87, 405.53, 252.83], 'label': 'airplane'}, {'bbox': [112.93, 219.37, 165.7, 291.06], 'label': 'person'}]}, "motorcycle_object": {'object': {'bbox': [85.54, 240.69, 192.98, 310.74], 'label': 'motorcycle'}}, "result_image": {'image': <PIL.Image.Image image mode=RGB size=500x378 at 0x7F9C2DE313F0>}}

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

------Action------
THOUGHT:
The code has been executed without error, and the relevant variables indicate that the motorcycle has been detected and replaced with a goblin emoji in the image. The task is now complete.
ACTION TYPE:
done
ACTION CONTENT:
True

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

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

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

-----Response-----
RESPONSE (User Message):
Please immediately take a code_summary action to generate a summary of your code produced so far. If you have not produced any code yet, make an educated guess based on what you have learned so far.

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

------Action------
THOUGHT:
The user has requested a code summary of the actions taken to solve the query. I will summarize the code that detects a motorcycle in an image and replaces it with a goblin emoji.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import object_detection, select_object, emoji
detection_result = object_detection(image='/root/input_data/2382602.jpg')
motorcycle_object = select_object(objects=detection_result['objects'], object_name='motorcycle')
result_image = emoji(image='/root/input_data/2382602.jpg', object=motorcycle_object['object'], emoji='goblin')

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