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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: I'm participating in a debate on contemporary socio-political terminologies and their impact on societies. One of these terms is 'woke'. I need to understand its meaning and implications better. Can you find a simplified definition of 'woke' and then generate a paragraph that illustrates its usage? Finally, identify the general sentiment or tone associated with its usage.
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 simplified definition of the term 'woke'. I will search for a function related to retrieving factual information or definitions. Once I have the definition, I will look for a function that can generate a paragraph illustrating its usage. Finally, I will search for a function that can analyze the sentiment or tone of a given text.
ACTION TYPE:
search
ACTION CONTENT:
text: definition AND (text: woke)
text: generate AND (text: paragraph)
text: sentiment OR (text: tone)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: definition AND (text: woke)

CODE BLOCKS:
---
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=[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.
    """
    ...
}
---
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."
    """
    ...
}
---
(All code blocks matching the query were returned.)

QUERY:
text: generate AND (text: paragraph)

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[931, 941]
type=FUNCTION
content={
def get_location(city: str, ckpt=None):
    """
    Convert a city name or address to geographical coordinates using OpenStreetMap's Nominatim API.
    """
    url = "https://nominatim.openstreetmap.org/search"
    headers = {"User-Agent": "m&ms"}
    params = {"q": city, "format": "json"}
    response = requests.get(url, params=params, headers=headers)
    result_formatted = response.json()[0]
    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=[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
}
---
(5 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 3 out of 3 classes/functions found in unexpanded results (search again with the same query to see details):
def love_calculator(first_name: str, second_name: str, ckpt=None): ... # 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

QUERY:
text: sentiment OR (text: tone)

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=[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'])
    """
    ...
}
---
(25 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 image_processing(img): ... # 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 love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def text_processing(file_path): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The search results did not return a function specifically for retrieving definitions, but the `text_classification` function can classify text and might return a label that could be used as a simplified definition. However, this is not guaranteed to work as expected for the term 'woke'. The `text_generation` function seems promising for generating a paragraph illustrating the usage of 'woke'. Lastly, the `text_classification` function can also be used to identify the sentiment or tone associated with the usage of 'woke'. I will proceed with these functions and provide the user with the code to execute them.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import text_classification, text_generation

# Step 1: Generate a paragraph illustrating the usage of 'woke'
paragraph = text_generation(text="woke")

# Step 2: Classify the sentiment or tone of the generated paragraph
sentiment = text_classification(text=paragraph['text'])

# Output the generated paragraph and its sentiment
print("Generated Paragraph:", paragraph['text'])
print("Sentiment/Tone:", sentiment['text'])

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'Woke is a term often used to describe someone who is socially and politically aware, particularly of issues regarding race, gender, and social justice. It can also refer to the act of becoming aware or educated about such issues.'}
Generated Paragraph: Woke is a term often used to describe someone who is socially and politically aware, particularly of issues regarding race, gender, and social justice. It can also refer to the act of becoming aware or educated about such issues.
Sentiment/Tone: POSITIVE

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"text_classification": <function text_classification at 0x7f38ccb9ee60>, "text_generation": <function text_generation at 0x7f38ccb9ed40>, "paragraph": {'text': 'Woke is a term often used to describe someone who is socially and politically aware, particularly of issues regarding race, gender, and social justice. It can also refer to the act of becoming aware or educated about such issues.'}, "sentiment": {'text': 'POSITIVE'}}

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

------Action------
THOUGHT:
The code execution was successful. The `text_generation` function provided a definition and usage of the term 'woke', and the `text_classification` function identified the sentiment as POSITIVE. I will now summarize the solution for the user.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import text_generation, text_classification
paragraph = text_generation(text="woke")
sentiment = text_classification(text=paragraph['text'])

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

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

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

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