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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I'm wondering how likely a successful relationship would be between Christian Grey and Anastasia Steele. Can you determine their love compatibility 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
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------Action------
THOUGHT:
Before writing any code, I need to find a function in the `mnm` codebase that can calculate love compatibility based on two names. I will search for a function that might be related to love compatibility or a love calculator.
ACTION TYPE:
search
ACTION CONTENT:
text: love calculator

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: love calculator

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=[195, 226]
type=FUNCTION
content={
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']):
    """
    Performs automatic speech recognition on an audio file using a pre-trained model.
    
    Args:
        audio (str): The file path to the audio file to be transcribed.
        ckpt (str, optional): The checkpoint identifier of the pre-trained model to use. Defaults to the model specified in MODEL_SELECTION["automatic_speech_recognition"].
    
    Returns:
        dict: A dictionary containing the transcription of the audio file with the key 'text'.
    
    Raises:
        ImportError: If the required libraries (librosa, transformers) are not installed.
    
    Examples:
        >>> transcription = automatic_speech_recognition("path/to/audio/file.wav")
        >>> print(transcription)
        {'text': 'The transcribed text of the audio.'}
    """
    ...
}
---
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
}
---
(19 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 15 classes/functions found in unexpanded results (search again with the same query to see details):
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def audio_processing(audio_file): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def image_crop_top(image, ckpt=None): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The `love_calculator` function from the `mnm/tool_api.py` file is exactly what I need to calculate the love compatibility between Christian Grey and Anastasia Steele. I will write the code to call this function with the provided names.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import love_calculator

# Calculate the love compatibility between Christian Grey and Anastasia Steele
compatibility_result = love_calculator("Christian Grey", "Anastasia Steele")

-----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 0x7f9e8f6dbc70>, "compatibility_result": {'number': '95', 'message': 'Congratulations! Good choice'}}

********************************************************************************
Step 4
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------Action------
THOUGHT:
The love compatibility calculation was successful, and the result is stored in the variable `compatibility_result`. I will now summarize the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import love_calculator
output0 = love_calculator("Christian Grey", "Anastasia Steele")

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

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

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

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