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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I have a lengthy recording, 2300-131720-0000.flac. Can you help me provide a short and concise summary of what was discussed, so I can easily review the main points?"
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 transcribe the audio file to text. Once I have the transcribed text, I will search for a function that can summarize the text to provide a concise summary of the main points discussed in the recording.
ACTION TYPE:
search
ACTION CONTENT:
text: speech recognition
text: summarization

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: speech recognition

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

Prototypes for the next 7 out of 34 classes/functions found in unexpanded results (search again with the same query to see details):
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def text_processing(file_path): ... # 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
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py

QUERY:
text: summarization

CODE BLOCKS:
---
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=[985, 1011]
type=FUNCTION
content={
def wikipedia_simple_search(text: str, ckpt=None):
    """
    Performs a basic search on Wikipedia to retrieve a summary of the most relevant page.
    
    Args:
        text (str): The search query text.
        ckpt: Unused parameter, can be anything (default is None).
    
    Returns:
        dict: A dictionary containing the formatted text summary of the most relevant Wikipedia page.
    
    Raises:
        This function may raise exceptions related to network issues or JSON decoding if the Wikipedia API changes or is unreachable.
    
    Examples:
        result = wikipedia_simple_search("Python programming language")
        # result might be something like:
        # {'text': 'Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with its use of significant indentation.'}
    """
    ...
}
---
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'])
    """
    ...
}
---
(21 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 18 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 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_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py
def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The `automatic_speech_recognition` function from `mnm/tool_api.py` can be used to transcribe the audio file to text. After obtaining the transcription, the `text_summarization` function from the same file can be used to summarize the transcribed text. I will now provide the code to perform these two steps.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import automatic_speech_recognition, text_summarization

# Transcribe the audio file to text
transcription = automatic_speech_recognition(audio='/root/input_data/2300-131720-0000.flac')

# Summarize the transcribed text
summary = text_summarization(text=transcription['text'])
summary_text = summary['text']

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"automatic_speech_recognition": <function automatic_speech_recognition at 0x7f5e7070b0a0>, "text_summarization": <function text_summarization at 0x7f5e7070aef0>, "transcription": {'text': ' The Paris plant, like that at the Crystal Palace, was a temporary exhibit.'}, "summary": {'text': 'The Paris plant, like that at the Crystal Palace, was a temporary exhibit'}, "summary_text": The Paris plant, like that at the Crystal Palace, was a temporary exhibit}

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

------Action------
THOUGHT:
The code has been executed successfully, and the audio file has been transcribed and summarized. The summary of the audio file is available in the variable `summary_text`. I will now provide a code summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import automatic_speech_recognition, text_summarization
transcription = automatic_speech_recognition(audio='/root/input_data/2300-131720-0000.flac')
summary = text_summarization(text=transcription['text'])
summary_text = summary['text']

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

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

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

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