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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I would like to have a unique fact about the number '10' and create an image to depict this fact in a fun way. Once the initial image is generated, I would like if you can edit the image and include a group of ten animals in the foreground to personify the quantity 10 in nature."
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 retrieve a unique fact about the number '10'. I will search for a function related to information retrieval that can handle this task. Once I have the fact, I will look for a function that can generate an image based on text descriptions to create an initial image depicting the fact. After that, I will search for an image editing function that can edit the initial image to include a group of ten animals in the foreground.
ACTION TYPE:
search
ACTION CONTENT:
text: unique fact about number
text: generate image
text: edit image

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: unique fact about number

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[879, 893]
type=FUNCTION
content={
def get_math_fact(number: str, ckpt=None):
    """
    It provides interesting math facts about numbers.
    """
    url = f"https://numbersapi.p.rapidapi.com/{number}/math"
    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
}
---
file_path=mnm/tool_api.py
lines=[895, 909]
type=FUNCTION
content={
def get_trivia_fact(number: str, ckpt=None):
    """
    It provides interesting trivia facts about number.
    """
    url = f"https://numbersapi.p.rapidapi.com/{number}/trivia"
    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
}
---
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
}
---
(33 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 get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def count(objects, ckpt=None): ... # mnm/tool_api.py
def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py

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

Prototypes for the next 7 out of 25 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:
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
}
---
(22 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 19 classes/functions found in unexpanded results (search again with the same query to see details):
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
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The `get_trivia_fact` function seems suitable for retrieving a unique fact about the number '10'. Next, I will use the `image_generation` function to create an initial image based on the retrieved fact. Finally, I will use the `image_editing` function to edit the generated image to include a group of ten animals in the foreground. I will start by retrieving the fact about the number '10' using `get_trivia_fact`.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_trivia_fact

# Retrieve a unique fact about the number '10'
fact_about_number_10 = get_trivia_fact(number='10')

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"get_trivia_fact": <function get_trivia_fact at 0x7f40cf4e7eb0>, "fact_about_number_10": {'text': 'the number of Provinces in Canada', 'number': 10, 'found': True, 'type': 'trivia'}}

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

------Action------
THOUGHT:
The fact about the number '10' has been successfully retrieved: "the number of Provinces in Canada". Now, I will use the `image_generation` function to create an initial image based on this fact. After generating the image, I will proceed to edit it to include a group of ten animals.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation

# Generate an initial image based on the retrieved fact
initial_image = image_generation(text=fact_about_number_10['text'])

-----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 0x7f40cf4e7250>, "initial_image": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F418D5FFF10>}}

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

------Action------
THOUGHT:
The initial image has been generated. Now, I will use the `image_editing` function to edit the generated image to include a group of ten animals in the foreground, which will personify the quantity '10' in nature as requested.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_editing

# Edit the initial image to include a group of ten animals in the foreground
edited_image_with_animals = image_editing(image=initial_image['image'], prompt="Include a group of ten animals in the foreground to personify the quantity 10 in nature.")

-----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 0x7f40cf4e7370>, "edited_image_with_animals": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F418DA2D210>}}

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

------Action------
THOUGHT:
The image has been successfully edited to include a group of ten animals. The task is now complete, and I will provide a summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_trivia_fact, image_generation, image_editing
fact_about_number_10 = get_trivia_fact(number='10')
initial_image = image_generation(text=fact_about_number_10['text'])
edited_image_with_animals = image_editing(image=initial_image['image'], prompt="Include a group of ten animals in the foreground to personify the quantity 10 in nature.")

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

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

------Action------
THOUGHT:
The task has been completed successfully, and the user has acknowledged receipt of the summary.
ACTION TYPE:
done
ACTION CONTENT:
True

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