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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: I'm working on a project about the origin of everyday tools and I am particularly intrigued about chainsaws. Can you research the question 'Why were chainsaws invented', provide a detailed elaboration beyond the basic information and then evaluate the tone of that elaboration - whether it is generally positive, negative or neutral?
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 factual information or interesting facts about a topic, which in this case is the origin of chainsaws. I will search for a function in the `mnm` codebase that can handle information retrieval tasks. Once I find the appropriate function, I can use it to research the question 'Why were chainsaws invented'. After obtaining the detailed elaboration, I will look for a function that can evaluate the tone of the text.
ACTION TYPE:
search
ACTION CONTENT:
text: (information retrieval) AND (text: chainsaws)
text: (evaluate tone) OR (text: sentiment analysis)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: (information retrieval) AND (text: chainsaws)

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=[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=[377, 411]
type=FUNCTION
content={
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']):
    """
    Performs object detection on an image using a DETR (DEtection TRansformer) model.
    
    Args:
        image: The image on which to perform object detection.
        ckpt: A string representing the model checkpoint to use for object detection. Defaults to a value from MODEL_SELECTION["object_detection"].
    
    Returns:
        A dictionary with two keys:
        - 'image': The processed image.
        - 'objects': A list of dictionaries, each containing a 'bbox' key with the bounding box coordinates and a 'label' key with the object's label.
    
    Raises:
        ImportError: If the required modules 'torch' or 'transformers' are not installed.
    
    Examples:
        >>> result = object_detection(my_image)
        >>> print(result['objects'])
        [{'bbox': [x1, y1, x2, y2], 'label': 'object_label'}, ...]
    """
    ...
}
---
(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_generation(text: str, ckpt=MODEL_SELECTION['image_generation']): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # 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 text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py

QUERY:
text: (evaluate tone) OR (text: sentiment analysis)

CODE BLOCKS:
---
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=[491, 534]
type=FUNCTION
content={
def image_crop(image, object, ckpt=None):
    """
    Crops an image to the region within a specified bounding box.
    
    Args:
        image: The image to be cropped.
        object: A dictionary containing the bounding box coordinates with the key 'bbox'.
        ckpt: Optional; a checkpoint or state to use for additional processing (not used in the current implementation).
    
    Returns:
        A dictionary with the key 'image' containing the cropped image.
    
    Raises:
        ValueError: If the bounding box is a string and cannot be converted to a list of coordinates.
    
    Examples:
        # Assuming `img` is an image object and `obj` is a dict with bbox coordinates
        cropped_image_result = image_crop(img, obj)
        cropped_image = cropped_image_result['image']
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[840, 861]
type=FUNCTION
content={
def get_date_fact(date: str, ckpt=None):
    """
    It provides interesting facts about dates.
    """
    from dateutil import parser

    date_str = date
    dt = parser.parse(date_str)

    formatted_date = dt.strftime("%m/%d")

    url = f"https://numbersapi.p.rapidapi.com/{formatted_date}/date"
    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
}
---
(33 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 31 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 image_processing(img): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py
def get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def get_math_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The search results did not return a function specifically for retrieving information about the origin of chainsaws. However, the `text_generation` function prototype mentioned in the unexpanded results seems promising for generating detailed text based on a prompt. I will search again with the same query to get the details of the `text_generation` function. Additionally, the `text_classification` function can be used to evaluate the tone of the generated text. I will proceed with retrieving the full details of the `text_generation` function.
ACTION TYPE:
search
ACTION CONTENT:
text: text_generation

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
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=[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
}
---
(20 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 get_full_path_data(filename): ... # 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
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

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

------Action------
THOUGHT:
I have found the `text_generation` function which can be used to generate a detailed elaboration on the topic of the origin of chainsaws. I will now write code to use this function with a prompt related to the invention of chainsaws. After obtaining the generated text, I will use the `text_classification` function to evaluate the tone of the elaboration.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import text_generation, text_classification

# Generate a detailed elaboration on the origin of chainsaws
prompt = "Why were chainsaws invented? Provide a detailed elaboration on the history and original purpose of chainsaws."
elaboration_result = text_generation(prompt)

# Evaluate the tone of the elaboration
if 'text' in elaboration_result:
    elaboration_text = elaboration_result['text']
    tone_result = text_classification(elaboration_text)
else:
    tone_result = {'error': 'Failed to generate elaboration text.'}

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': "Chainsaws were originally invented in the 18th century to facilitate the process of cutting wood more efficiently and quickly. The first patent for a chainsaw-like device was granted in 1780 to two Scottish doctors, John Aitken and James Jeffray, who designed a chain carrying a set of teeth that was rotated using a hand crank. This early version of the chainsaw was primarily used for surgical purposes, such as amputations and cutting through bone during procedures.\n\nIt wasn't until the late 19th century that the chainsaw as we know it today began to take shape. In 1830, the German orthopedist Bernhard Heine invented a pedal-operated chainsaw for use in surgeries, which was later adapted for use in woodcutting. However, it was not until the early 20th century that chainsaws began to be widely used for logging and forestry purposes.\n\nOne of the key developments in the evolution of the chainsaw was the creation of the first gasoline-powered chainsaw in 1929 by Andreas Stihl, a German engineer. This innovation revolutionized the logging industry, allowing for faster and more efficient cutting of trees and timber.\n\nThroughout the 20th century, chainsaws continued to evolve, with improvements in design, power, and safety features. Today, chainsaws are used not only in forestry and logging but also in construction, landscaping, and even by firefighters and rescue workers.\n\nIn conclusion, chainsaws were invented to streamline and"}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"text_generation": <function text_generation at 0x7f38ccbb2dd0>, "text_classification": <function text_classification at 0x7f38ccbb2ef0>, "prompt": Why were chainsaws invented? Provide a detailed elaboration on the history and original purpose of chainsaws., "elaboration_result": {'text': "Chainsaws were originally invented in the 18th century to facilitate the process of cutting wood more efficiently and quickly. The first patent for a chainsaw-like device was granted in 1780 to two Scottish doctors, John Aitken and James Jeffray, who designed a chain carrying a set of teeth that was rotated using a hand crank. This early version of the chainsaw was primarily used for surgical purposes, such as amputations and cutting through bone during procedures.\n\nIt wasn't until t..., "elaboration_text": Chainsaws were originally invented in the 18th century to facilitate the process of cutting wood more efficiently and quickly. The first patent for a chainsaw-like device was granted in 1780 to two Scottish doctors, John Aitken and James Jeffray, who designed a chain carrying a set of teeth that was rotated using a hand crank. This early version of the chainsaw was primarily used for surgical purposes, such as amputations and cutting through bone during procedures.

It wasn't until the late 19th..., "tone_result": {'text': 'POSITIVE'}}

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

------Action------
THOUGHT:
The text generation function successfully produced a detailed elaboration on the origin of chainsaws. The tone of the elaboration was classified as 'POSITIVE' by the text classification function. I will now summarize the solution using the code_summary action.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import text_generation, text_classification
elaboration_result = text_generation(text="Why were chainsaws invented? Provide a detailed elaboration on the history and original purpose of chainsaws.")
elaboration_text = elaboration_result['text']
tone_result = text_classification(text=elaboration_text)

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

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

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

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