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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "Can you tell me a fascinating fact about the number '2' and then illustrate it with an image? After you've created the image, could you give me a clear description of what's shown in it?"
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 fascinating fact about the number '2'. Once I have the fact, I will then search for a function that can generate an image based on a text description. Finally, I will need to find a function that can provide a description of the image. I will start by searching for the information retrieval function that can get a fact about the number '2'.
ACTION TYPE:
search
ACTION CONTENT:
text: (fascinating fact) AND (text: number)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: (fascinating fact) AND (text: number)

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=[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
}
---
(12 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 10 classes/functions found in unexpanded results (search again with the same query to see details):
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def get_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py
def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `get_trivia_fact` from the `tool_api.py` file seems suitable for retrieving an interesting fact about the number '2'. I will use this function first to get the fact. After obtaining the fact, I will search for a function that can generate an image based on the text description of the fact.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_trivia_fact

# Retrieve a fascinating fact about the number '2'
fact_about_number_two = get_trivia_fact('2')

-----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 0x7f40cf4e7c70>, "fact_about_number_two": {'text': 'the number of polynucleotide strands in a DNA double helix', 'number': 2, 'found': True, 'type': 'trivia'}}

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

------Action------
THOUGHT:
Now that we have the fact about the number '2', I will search for a function that can generate an image based on a text description. This will allow us to illustrate the fact with an image.
ACTION TYPE:
search
ACTION CONTENT:
text: generate image AND (text: description OR text: text)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: generate image AND (text: description OR text: text)

CODE BLOCKS:
---
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=[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=[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
}
---
(18 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 16 classes/functions found in unexpanded results (search again with the same query to see details):
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_generation` from the `tool_api.py` file is suitable for generating an image based on a text description. I will use this function to generate an image that matches the description of the fact about the number '2'. After generating the image, I will use the `image_captioning` function to provide a clear description of what's shown in the image.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation, image_captioning

# Generate an image based on the fascinating fact about the number '2'
description_of_fact = fact_about_number_two['text']
generated_image = image_generation(description_of_fact)

# Once the image is generated, get a description of the image
if 'image' in generated_image:
    image_description = image_captioning(generated_image['image'])
else:
    image_description = "No image was generated."

-----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 0x7f40cf4e7010>, "image_captioning": <function image_captioning at 0x7f40cf4e70a0>, "description_of_fact": the number of polynucleotide strands in a DNA double helix, "generated_image": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F3CDA8C5720>}, "image_description": {'text': 'a close up of a double - stranded strand of colored beads'}}

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

------Action------
THOUGHT:
The image has been successfully generated and a description of the image has been provided. The task is now complete.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_trivia_fact, image_generation, image_captioning
fact_about_number_two = get_trivia_fact('2')
generated_image = image_generation(fact_about_number_two['text'])
image_description = image_captioning(generated_image['image'])

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

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

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

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