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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: I have this image, "581668-input.png" and I want to add a few cookies to the tray in the picture. Afterwards, I want to understand what the modified image portrays. Finally, can we determine if the resulting description paints a positive or negative picture?
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 cookies to the tray. Once the image is modified, I will need to find a function that can provide a description of the image. Finally, I will need to search for a function that can classify the description as positive or negative. I will start by searching for the image editing function to add cookies to the image.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: edit image)

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

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=[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
}
---
(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 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
def image_crop_right(image, ckpt=None): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_editing` from `tool_api.py` seems to be the right choice for editing the image by adding cookies to the tray. It takes an image and a text prompt describing the desired edits and returns the edited image. Next, I will use the `image_captioning` function to understand what the modified image portrays. However, I still need to find a function that can classify the description as positive or negative. I will search for such a function after using `image_editing` and `image_captioning`.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_editing, image_captioning

# Edit the image by adding cookies to the tray
edited_image_result = image_editing(image="/root/input_data/581668-input.png", prompt="Add a few cookies to the tray in the picture.")

# If the image is successfully edited, use image captioning to understand what the modified image portrays
if 'image' in edited_image_result:
    caption_result = image_captioning(image=edited_image_result['image'])
else:
    caption_result = {"text": "Failed to edit the image."}

-----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 0x7f2c0f967010>, "image_captioning": <function image_captioning at 0x7f2c0f966f80>, "edited_image_result": {'image': <PIL.Image.Image image mode=RGB size=496x496 at 0x7F2C08F48D60>}, "caption_result": {'text': 'there is a container with cookies and other food in it'}}

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

------Action------
THOUGHT:
The image has been successfully edited to add cookies to the tray, and the caption describes the modified image as containing "a container with cookies and other food in it." Now, I need to find a function that can classify this description as positive or negative to determine the sentiment of the resulting description.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: sentiment)

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

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[165, 176]
type=FUNCTION
content={
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']):
    """
    Classifies a given text into a category based on the model's vocabulary.
    
    Args:
        text (str): The text to be classified.
        ckpt: The model checkpoint to use for classification. Defaults to MODEL_SELECTION["text_classification"].
    
    Returns:
        dict: A dictionary with the key 'text' and the value as the classification label.
    
    Examples:
        >>> text_classification("I love sunny days.")
        {'text': 'POSITIVE'}
    """
    ...
}
---
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=[840, 861]
type=FUNCTION
content={
def get_date_fact(date: str, ckpt=None):
    """
    It provides interesting facts about dates.
    """
    from dateutil import parser

    date_str = date
    dt = parser.parse(date_str)

    formatted_date = dt.strftime("%m/%d")

    url = f"https://numbersapi.p.rapidapi.com/{formatted_date}/date"
    params = {"fragment": "true", "json": "true"}
    headers = {
        "X-RapidAPI-Key": RAPID_API_KEY,
        "X-RapidAPI-Host": "numbersapi.p.rapidapi.com",
    }
    response = requests.get(url, headers=headers, params=params)
    result_formatted = response.json()

    return result_formatted
}
---
(13 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 13 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 automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py
def get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py
def get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `text_classification` from `tool_api.py` can classify a given text into a category such as 'POSITIVE' or 'NEGATIVE'. This function will be used to determine the sentiment of the description obtained from the image captioning.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import text_classification

# Classify the sentiment of the description
sentiment_result = text_classification(text=caption_result['text'])

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"text_classification": <function text_classification at 0x7f2c0f966d40>, "sentiment_result": {'text': 'NEGATIVE'}}

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

------Action------
THOUGHT:
The sentiment of the description "there is a container with cookies and other food in it" has been classified as 'NEGATIVE'. This completes the user's query. I will now provide a summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import image_editing, image_captioning, text_classification
edited_image_result = image_editing(image="/root/input_data/581668-input.png", prompt="Add a few cookies to the tray in the picture.")
caption_result = image_captioning(image=edited_image_result['image'])
sentiment_result = text_classification(text=caption_result['text'])

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

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

------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