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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "Could you analyze the love possibilities between Romeo Montague and Juliet Capulet based on their names? Additionally, based on whatever percentage of compatibility they receive, is it possible to share some fun trivia associated with that number, and maybe even generate a creatively designed image which portrays this fact?"
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 in the `mnm` codebase that can calculate the compatibility between two names, which is likely to be part of the information retrieval functions. After obtaining the compatibility percentage, I will search for a function that can provide fun trivia associated with a number. Lastly, I will look for a function that can generate an image based on a description, which could be used to portray the fun trivia creatively.
ACTION TYPE:
search
ACTION CONTENT:
text: love calculator
text: fun trivia
text: generate image

-----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

QUERY:
text: fun trivia

CODE BLOCKS:
---
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=[51, 77]
type=FUNCTION
content={
def text_processing(file_path):
    """
    preprocess the text file

    Args:
    - the path of .doc .docx .txt file

    Returns:
    - text with string format

    Notes:
    - This function is designed for text input.

    """

    # Check the file extension
    if file_path.endswith(".txt"):
        with open(file_path, "r") as file:
            content = file.read()
    elif file_path.endswith(".doc") or file_path.endswith(".docx"):
        # Use textract to extract text from doc and docx files
        content = textract.process(file_path).decode("utf-8")
    else:
        # if the file is not .txt .doc .docx, then it is a string, directly return the stirng
        return file_path
    return content
}
---
file_path=mnm/tool_api.py
lines=[87, 97]
type=FUNCTION
content={
def image_processing(img):
    # different models have different requirements for image input, so we will do preprocessing in each function
    if isinstance(img, Image.Image):
        return img.convert("RGB")
    elif isinstance(img, str):
        if os.path.exists(img):
            return Image.open(img).convert("RGB")
        else:
            full_path = get_full_path_data(img)
            return Image.open(full_path).convert("RGB")
}
---
(9 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 9 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 object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py
def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py
def get_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py
def get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # 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=[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=[17, 22]
type=FUNCTION
content={
def get_full_path_data(
    filename,
):  # get the path of data which isn't generated during the process
    base_path = MNM_BASE_PATH_DATA
    return os.path.join(base_path, filename)
}
---
(26 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 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
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The search results have provided the necessary functions to fulfill the user's request. The `love_calculator` function will be used to analyze the love possibilities between Romeo Montague and Juliet Capulet. The `get_trivia_fact` function will provide fun trivia associated with the compatibility percentage. Lastly, the `image_generation` function will be used to generate a creatively designed image that portrays the fun trivia 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_trivia_fact, image_generation

# Calculate the love compatibility between Romeo Montague and Juliet Capulet
compatibility_result = love_calculator("Romeo Montague", "Juliet Capulet")

# Retrieve a fun trivia fact associated with the compatibility percentage
trivia_result = get_trivia_fact(compatibility_result["number"])

# Generate a creatively designed image that portrays the fun trivia fact
image_result = image_generation(trivia_result["text"])

compatibility_result, trivia_result, image_result

-----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>, "get_trivia_fact": <function get_trivia_fact at 0x7f9e8f6dbbe0>, "image_generation": <function image_generation at 0x7f9e8f6daf80>, "compatibility_result": {'number': '96', 'message': 'Congratulations! Good choice'}, "trivia_result": {'text': 'the rating of Skyrim on metacritic.com', 'number': 96, 'found': True, 'type': 'trivia'}, "image_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F9F4DAFE860>}}

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

------Action------
THOUGHT:
The code executed successfully, and we have obtained the compatibility result, a fun trivia fact associated with the compatibility percentage, and a generated image. 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_trivia_fact, image_generation
output0 = love_calculator("Romeo Montague", "Juliet Capulet")
output1 = get_trivia_fact(output0["number"])
output2 = image_generation(output1["text"])

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

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

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

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