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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I want to learn more about the number 3. Could you start by providing me an interesting fact about this number, and then write a caption or story that includes this fact? After that, could you produce an illustration that encapsulates this story or caption?"
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 retrieve an interesting fact about the number 3. I will start by searching for a function related to retrieving facts about numbers.
ACTION TYPE:
search
ACTION CONTENT:
text: interesting facts AND (text: number OR text: numbers)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: interesting facts AND (text: number OR text: numbers)

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=[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=[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 get_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def count(objects, ckpt=None): ... # mnm/tool_api.py
def select_object(objects, object_name, ckpt=None): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py
def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `get_trivia_fact` from the `mnm` codebase seems suitable for retrieving an interesting fact about the number 3. I will use this function to get the fact. After obtaining the fact, I will search for a function that can generate a caption or story that includes this fact.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_trivia_fact

# Retrieve an interesting fact about the number 3
number_fact = get_trivia_fact('3')

-----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 0x7f9e8f6dba30>, "number_fact": {'text': "the number of witches in William Shakespeare's Macbeth", 'number': 3, 'found': True, 'type': 'trivia'}}

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

------Action------
THOUGHT:
Now that I have an interesting fact about the number 3, I need to find a function that can generate a caption or story including this fact. I will search for a text generation function in the `mnm` codebase.
ACTION TYPE:
search
ACTION CONTENT:
text: generate AND text: text

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: generate AND 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=[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=[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.'}
    """
    ...
}
---
(11 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 text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # 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 text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # 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 `text_generation` appears in the unexpanded results, which is likely suitable for generating a caption or story. I will search again with the same query to get the details of this function.
ACTION TYPE:
search
ACTION CONTENT:
text: generate AND text: text

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

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=[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=[731, 765]
type=FUNCTION
content={
def tag(image, objects, ckpt=None):
    """
    Tags objects in an image with bounding boxes and class labels.
    
    Args:
        image: The image to be processed and tagged.
        objects: A list of dictionaries, each containing 'bbox' (bounding box) and 'label' (class) for an object.
        ckpt (optional): A checkpoint or model state to use for tagging, not used in the current implementation.
    
    Returns:
        A dictionary with a key 'image' containing the tagged image.
    
    Examples:
        # Assuming 'image' is an image object and 'objects' is a list of object dictionaries with 'bbox' and 'label'
        result = tag(image, objects)
        tagged_image = result['image']
    """
    ...
}
---
(8 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 6 out of 6 classes/functions found in unexpanded results (search again with the same query to see details):
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
I have found the `text_generation` function which can be used to generate a caption or story that includes the interesting fact about the number 3. I will now write code to use this function to generate the text.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import text_generation

# Generate a caption or story that includes the interesting fact about the number 3
fact_about_number_3 = number_fact['text']
story_prompt = f"Tell a story that includes the following fact: {fact_about_number_3}."
generated_story = text_generation(story_prompt)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': "Once upon a time, in the midst of a dark and stormy night, three witches gathered in the shadowy depths of the forest. These witches were said to be the descendants of the infamous Weird Sisters from William Shakespeare's play Macbeth.\n\nAs the witches huddled around a bubbling cauldron, chanting incantations and casting spells, they spoke of the number of witches that featured in Shakespeare's tragic tale. They marveled at how their ancestors had been portrayed as three in number, stirring up chaos and prophecy wherever they went.\n\nBut as the night wore on, a fourth witch appeared, her presence unexpected and unsettling. The other witches eyed her warily, sensing that she brought with her a dark and mysterious power that they had not encountered before.\n\nThe fourth witch revealed that she had been banished from their coven long ago for delving too deeply into forbidden magics. She had been living in exile, honing her skills and gathering strength, until finally she had returned to seek revenge on those who had cast her out.\n\nThe other witches knew that they were no match for this vengeful sorceress, and so they fled into the night, leaving her alone with her dark intentions. And as the storm raged on, the fourth witch began to weave a new prophecy, one that would bring about the downfall of all who stood in her way.\n\nAnd so, the legacy of the Weird Sisters lived on, their number now increased by one more witch of darkness and"}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"text_generation": <function text_generation at 0x7f9e8f6dab00>, "fact_about_number_3": the number of witches in William Shakespeare's Macbeth, "story_prompt": Tell a story that includes the following fact: the number of witches in William Shakespeare's Macbeth., "generated_story": {'text': "Once upon a time, in the midst of a dark and stormy night, three witches gathered in the shadowy depths of the forest. These witches were said to be the descendants of the infamous Weird Sisters from William Shakespeare's play Macbeth.\n\nAs the witches huddled around a bubbling cauldron, chanting incantations and casting spells, they spoke of the number of witches that featured in Shakespeare's tragic tale. They marveled at how their ancestors had been portrayed as three in number, st...}

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

------Action------
THOUGHT:
With the generated story that includes the fact about the number 3, the next step is to create an illustration that encapsulates this story. I will search for an image generation function in the `mnm` codebase that can take a text description and generate a corresponding image.
ACTION TYPE:
search
ACTION CONTENT:
text: image_generation AND text: text

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: image_generation AND text: text

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[325, 346]
type=FUNCTION
content={
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']):
    """
    Classifies an image into one of the predefined categories using a Vision Transformer (ViT) model.
    
    Args:
        image: The image to be classified, which will be processed by `image_processing` function.
        ckpt (str, optional): The checkpoint or model identifier to use for loading the pre-trained model. Defaults to `MODEL_SELECTION["image_classification"]`.
    
    Returns:
        dict: A dictionary with a key "text" that maps to the predicted category of the image.
    
    Examples:
        >>> result = image_classification(my_image)
        >>> print(result)
        {'text': 'cat'}
    """
    ...
}
---
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")
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[463, 489]
type=FUNCTION
content={
def optical_character_recognition(
    image, ckpt=MODEL_SELECTION["optical_character_recognition"]
):
    """
    It takes an image and outputs recognized texts in the image.
    """
    import easyocr

    if isinstance(image, str):
        image_path = image if os.path.exists(image) else get_full_path_data(image)
    else:
        image = image_processing(image)
        image_path = os.path.join(MNM_BASE_PATH_DATA, "ocr_temp.jpg")
        image.save(image_path)

    assert ckpt == "easyOCR"  # only support easyocr for now
    reader = easyocr.Reader(
        ["en"]
    )  # this needs to run only once to load the model into memory
    result = reader.readtext(image_path)
    result_text = [
        text for _, text, _ in result
    ]  # Extract only the text from the result
    result_formatted = {"text": ", ".join(result_text)}

    return result_formatted
}
---
(10 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 8 classes/functions found in unexpanded results (search again with the same query to see details):
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def text_classification(text: str, ckpt=MODEL_SELECTION['text_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 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 text_processing(file_path): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The search did not return the `image_generation` function directly, but it was mentioned in the description of the `image_editing` function. I will search again with the same query to get the details of the `image_generation` function.
ACTION TYPE:
search
ACTION CONTENT:
text: image_generation AND text: text

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: image_generation AND text: text

CODE BLOCKS:
---
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'}
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[143, 163]
type=FUNCTION
content={
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']):
    """
    Summarizes a given text into a few sentences using a pre-trained model.
    
    Args:
        text (str): The text to be summarized.
        ckpt: The checkpoint or model identifier to use for the summarization pipeline. Defaults to a pre-selected model checkpoint.
    
    Returns:
        dict: A dictionary containing the summarized text under the key 'text'.
    
    Examples:
        summary = text_summarization("The quick brown fox jumps over the lazy dog.")
        print(summary)  # Outputs a summarized version of the input text.
    """
    ...
}
---
(7 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 5 out of 5 classes/functions found in unexpanded results (search again with the same query to see details):
def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # 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 text_processing(file_path): ... # mnm/tool_api.py
def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The `image_generation` function is still not directly returned in the search results. However, it was mentioned in the description of the `image_editing` function, and it is likely that the function exists in the codebase. I will use the `image_generation` function to create an illustration based on the generated story.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation

# Generate an illustration that encapsulates the generated story
illustration_prompt = generated_story['text']
illustration = image_generation(illustration_prompt)

-----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 0x7f9e8f6dadd0>, "illustration_prompt": Once upon a time, in the midst of a dark and stormy night, three witches gathered in the shadowy depths of the forest. These witches were said to be the descendants of the infamous Weird Sisters from William Shakespeare's play Macbeth.

As the witches huddled around a bubbling cauldron, chanting incantations and casting spells, they spoke of the number of witches that featured in Shakespeare's tragic tale. They marveled at how their ancestors had been portrayed as three in number, stirring up ch..., "illustration": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F9E88F2EE90>}}

********************************************************************************
Step 10
********************************************************************************

------Action------
THOUGHT:
The illustration has been generated successfully. The task is now complete with an interesting fact about the number 3, a story that includes this fact, and an illustration encapsulating the story.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_trivia_fact, text_generation, image_generation
output0 = get_trivia_fact('3')
output1 = text_generation(f"Tell a story that includes the following fact: {output0['text']}.")
output2 = image_generation(output1['text'])

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

********************************************************************************
Step 11
********************************************************************************

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

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