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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I am helping plan a special date for Jacob Black and Bella Swan. Could you help me figure out their compatibility chances? Then, to give the date a cute brainy twist, please generate an intriguing math fact. Then, let's top that off by creating a creative visual representation of that fact to be used as part of their date's decoration."
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 the function that calculates compatibility chances based on names. I will search for a function related to the love calculator or compatibility check. After that, I will look for a function that can generate an intriguing math fact. Lastly, I will search for a function that can create a visual representation of text, which we can use to create a decoration based on the math fact.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: compatibility)
(type: FUNCTION) AND (text: math fact)
(type: FUNCTION) AND (text: visual representation) AND (text: text)

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

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[911, 929]
type=FUNCTION
content={
def love_calculator(first_name: str, second_name: str, ckpt=None):
    """
    Enter your name and the name of your partner/lover/crush to find Love compatibility & chances of successful love relationship.
    """
    url = "https://love-calculator.p.rapidapi.com/getPercentage"
    params = {"fname": first_name, "sname": second_name}
    headers = {
        "X-RapidAPI-Key": RAPID_API_KEY,
        "X-RapidAPI-Host": "love-calculator.p.rapidapi.com",
    }
    response = requests.get(url, headers=headers, params=params)
    result_formatted = response.json()
    result_formatted = {
        "number": result_formatted["percentage"],
        "message": result_formatted["result"],
    }

    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=[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")
    """
    ...
}
---
(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 text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def select_object(objects, object_name, ckpt=None): ... # mnm/tool_api.py
def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py

QUERY:
(type: FUNCTION) AND (text: math fact)

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=[863, 877]
type=FUNCTION
content={
def get_year_fact(year: str, ckpt=None):
    """
    It provides interesting facts about years.
    """
    url = f"https://numbersapi.p.rapidapi.com/{year}/year"
    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 33 classes/functions found in unexpanded results (search again with the same query to see details):
def get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def text_processing(file_path): ... # mnm/tool_api.py
def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def get_full_path_data(filename): ... # mnm/tool_api.py

QUERY:
(type: FUNCTION) AND (text: visual representation) AND (text: text)

CODE BLOCKS:
---
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."
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[413, 461]
type=FUNCTION
content={
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']):
    """
    Segments an image into different parts using a pre-trained MaskFormer model and outputs segmentation masks along with additional information for each segment.
    
    Args:
        image: The input image to be segmented.
        ckpt: A string or a dictionary key that specifies the pre-trained model checkpoint to be used for segmentation. Defaults to a pre-selected model checkpoint from a MODEL_SELECTION dictionary.
    
    Returns:
        A dictionary containing the original image and a list of objects. Each object in the list is a dictionary with keys 'mask', 'label', 'bbox', and 'inst_id', representing the segmentation mask, label, bounding box, and instance ID, respectively.
    
    Raises:
        ImportError: If required modules (numpy, torch, transformers, PIL) are not installed.
    
    Examples:
        # Assuming `image` is a PIL image and `MODEL_SELECTION` is defined
        segmentation_result = image_segmentation(image)
        for obj in segmentation_result['objects']:
            print(obj['label'], obj['bbox'])
    """
    ...
}
---
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'}
    """
    ...
}
---
(12 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 12 classes/functions found in unexpanded results (search again with the same query to see details):
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py
def text_processing(file_path): ... # mnm/tool_api.py
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # 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
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `love_calculator` from `tool_api.py` can be used to calculate the compatibility chances between Jacob Black and Bella Swan. For generating an intriguing math fact, the function `get_math_fact` can be used. However, there doesn't seem to be a direct function for creating a visual representation of text. The closest function available is `image_generation`, which takes a text prompt and generates an image matching the description. I will use this function to create a visual representation of the math fact. I will now write the code to execute these functions in sequence.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import love_calculator, get_math_fact, image_generation

# Calculate compatibility chances
compatibility_result = love_calculator(first_name="Jacob", second_name="Bella")

# Generate an intriguing math fact
math_fact_result = get_math_fact(number="random")  # Assuming we want a random math fact

# Generate a visual representation of the math fact
visual_representation = image_generation(text=math_fact_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):
{"love_calculator": <function love_calculator at 0x7f5e707381f0>, "get_math_fact": <function get_math_fact at 0x7f5e707380d0>, "image_generation": <function image_generation at 0x7f5e70723490>, "compatibility_result": {'number': '55', 'message': 'All the best!'}, "math_fact_result": {'text': 'a number n whose 5^{th} root has a decimal part that begins with the digits of n', 'number': 5061, 'found': True, 'type': 'math'}, "visual_representation": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F5CA896D960>}}

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

------Action------
THOUGHT:
The code executed successfully, and we have obtained the compatibility result, a math fact, and a visual representation of the math fact. I will now summarize the solution using the code_summary action.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import love_calculator, get_math_fact, image_generation
compatibility_result = love_calculator(first_name="Jacob", second_name="Bella")
math_fact_result = get_math_fact(number="random")
visual_representation = image_generation(text=math_fact_result["text"])

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

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

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

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