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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "Can you analyze the romantic compatibility between 'Fitzwilliam Darcy' and 'Elizabeth Bennet'? Based on the numerical result, can we have an interesting trivia related to the number? Using these details, could you write a unique text or piece of fiction?"
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 romantic compatibility between two names. After obtaining the compatibility score, I will search for a function that can retrieve an interesting trivia related to the number obtained from the compatibility score. Finally, I will look for a function that can generate a unique text or piece of fiction based on the details we have. I will start by searching for the love calculator function to calculate the romantic compatibility.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: love calculator)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (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
}
---
(15 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` codebase is exactly what we need to calculate the romantic compatibility between 'Fitzwilliam Darcy' and 'Elizabeth Bennet'. I will write the code to call this function and store the result. Once we have the compatibility score, I will search for a function to retrieve trivia related to the number obtained.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import love_calculator

# Calculate the romantic compatibility between 'Fitzwilliam Darcy' and 'Elizabeth Bennet'
compatibility_result = love_calculator(first_name='Fitzwilliam Darcy', second_name='Elizabeth Bennet')
compatibility_score = compatibility_result['number']
compatibility_message = compatibility_result['message']

-----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 0x7f9d76634160>, "compatibility_result": {'number': '94', 'message': 'Congratulations! Good choice'}, "compatibility_score": 94, "compatibility_message": Congratulations! Good choice}

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

------Action------
THOUGHT:
We have successfully obtained the compatibility score and message. Now, I will search for a function that can retrieve an interesting trivia related to the number 94, which is the compatibility score we got.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: trivia)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (text: 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=[973, 983]
type=FUNCTION
content={
def get_weather(lon: str, lat: str, ckpt=None):
    """
    Provides weather forecast data based on specific geographical coordinates.
    """
    url = "http://www.7timer.info/bin/api.pl"
    params = {"lon": lon, "lat": lat, "product": "civil", "output": "json"}
    response = requests.get(url, params=params)
    result_formatted = {"objects": response.json()["dataseries"]}

    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
}
---
(All code blocks matching the query were returned.)

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

------Action------
THOUGHT:
The `get_trivia_fact` function is suitable for retrieving trivia about the number 94. I will write the code to call this function and store the trivia fact. After that, I will search for a function that can generate a unique text or piece of fiction based on the details we have.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_trivia_fact

# Retrieve an interesting trivia fact related to the number 94
trivia_fact_result = get_trivia_fact(number=str(compatibility_score))
trivia_fact = trivia_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):
{"get_trivia_fact": <function get_trivia_fact at 0x7f9d766340d0>, "trivia_fact_result": {'text': 'the length of an NBA court in feet', 'number': 94, 'found': True, 'type': 'trivia'}, "trivia_fact": the length of an NBA court in feet}

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

------Action------
THOUGHT:
We have obtained the trivia fact related to the number 94. Now, I need to find a function that can generate a unique text or piece of fiction based on the details we have: the compatibility score, the compatibility message, and the trivia fact. I will search for a text generation function in the `mnm` codebase.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: text generation)

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

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[111, 141]
type=FUNCTION
content={
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']):
    """
    Generates a continuation of the given input text using a specified language model.
    
    Args:
        text (str): The input text prompt to be continued.
        ckpt (str, optional): The identifier for the language model to be used. Defaults to the model specified in MODEL_SELECTION["text_generation"].
    
    Returns:
        dict: A dictionary containing the generated text under the key 'text'.
    
    Raises:
        KeyError: If the OPENAI_API_KEY environment variable is not set.
        APIError: If the OpenAI API encounters an issue with the request.
    
    Examples:
        >>> generated_text = text_generation("Once upon a time")
        >>> print(generated_text)
        {'text': '...continued text from the model...'}
    """
    ...
}
---
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."
    """
    ...
}
---
(28 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 28 classes/functions found in unexpanded results (search again with the same query to see details):
def get_full_path_data(filename): ... # mnm/tool_api.py
def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py
def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The `text_generation` function can be used to generate a unique text or piece of fiction. I will write the code to call this function, using the compatibility message and trivia fact as part of the input prompt to inspire the generated text.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import text_generation

# Generate a unique text or piece of fiction based on the compatibility message and trivia fact
prompt = f"{compatibility_message} Fitzwilliam Darcy and Elizabeth Bennet's love is as enduring as the {trivia_fact}."
generated_text_result = text_generation(text=prompt)
generated_text = generated_text_result['text']

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'Their love story has captured the hearts of readers for generations and continues to inspire countless adaptations and retellings. May their love story continue to stand the test of time, just like the length of an NBA court in feet.'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"text_generation": <function text_generation at 0x7f9d7660f130>, "prompt": Congratulations! Good choice Fitzwilliam Darcy and Elizabeth Bennet's love is as enduring as the the length of an NBA court in feet., "generated_text_result": {'text': 'Their love story has captured the hearts of readers for generations and continues to inspire countless adaptations and retellings. May their love story continue to stand the test of time, just like the length of an NBA court in feet.'}, "generated_text": Their love story has captured the hearts of readers for generations and continues to inspire countless adaptations and retellings. May their love story continue to stand the test of time, just like the length of an NBA court in feet.}

********************************************************************************
Step 8
********************************************************************************

------Action------
THOUGHT:
The unique text or piece of fiction has been successfully generated based on the compatibility message and trivia fact. 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 love_calculator, get_trivia_fact, text_generation
output0 = love_calculator(first_name='Fitzwilliam Darcy', second_name='Elizabeth Bennet')
output1 = get_trivia_fact(number=str(output0['number']))
output2 = text_generation(text=f"{output0['message']} Fitzwilliam Darcy and Elizabeth Bennet's love is as enduring as the {output1['text']}.")

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

********************************************************************************
Step 9
********************************************************************************

------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