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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: I have an audio file titled '237-134500-0000.flac'. Could you transcribe the audio and then generate a story that could naturally follow this conversation? 
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 the function for speech recognition to transcribe the audio file. Once I have the transcription, I can search for a function that generates a story based on text input. I will start by searching for the speech recognition function in the codebase.
ACTION TYPE:
search
ACTION CONTENT:
text: speech recognition

-----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

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

------Action------
THOUGHT:
The function `automatic_speech_recognition` from `tool_api.py` is suitable for transcribing the audio file. Next, I need to find a function that can generate a story based on the transcribed text. I will search for a text generation function in the codebase.
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
}
---
(35 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_full_path_data(filename): ... # mnm/tool_api.py
def wikipedia_simple_search(text: str, ckpt=None): ... # 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_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

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

------Action------
THOUGHT:
The function `text_generation` from `tool_api.py` is suitable for generating a story based on the transcribed text. Now I will write the code to first transcribe the audio file using `automatic_speech_recognition` and then generate a story using `text_generation`.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import automatic_speech_recognition, text_generation

# Transcribe the audio file
transcription = automatic_speech_recognition("/root/input_data/237-134500-0000.flac")

# Generate a story that could naturally follow the conversation
story = text_generation(transcription['text'])

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': " He couldn't believe the injustices that seemed to be taking place, the way one party was being treated so unfairly. Frank's blood boiled as he read through the court documents, seeing the lies and manipulation that were being used to twist the truth.\n\nHe clenched his fists in frustration, feeling a sense of helplessness wash over him. How could someone be so heartless and deceitful, using their power and influence to ruin someone else's life? Frank knew he had to do something, to speak out against this injustice and fight for what was right.\n\nWith a newfound determination, Frank continued reading, but now with a sense of purpose. He was going to do everything in his power to help the victim in this case, to bring justice and fairness back to the situation. And as he read on, his anger fueled his resolve to make a difference."}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"automatic_speech_recognition": <function automatic_speech_recognition at 0x7f40cf4e6f80>, "text_generation": <function text_generation at 0x7f40cf4e6d40>, "transcription": {'text': ' Frank read English slowly, and the more he read about this divorce case, the angrier he grew.'}, "story": {'text': " He couldn't believe the injustices that seemed to be taking place, the way one party was being treated so unfairly. Frank's blood boiled as he read through the court documents, seeing the lies and manipulation that were being used to twist the truth.\n\nHe clenched his fists in frustration, feeling a sense of helplessness wash over him. How could someone be so heartless and deceitful, using their power and influence to ruin someone else's life? Frank knew he had to do something, to sp...}

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

------Action------
THOUGHT:
The code has been executed successfully, and the transcription and story generation tasks have been completed. The transcription of the audio file and the generated story are now stored in the variables `transcription` and `story`, respectively. 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_generation
transcription = automatic_speech_recognition(audio='/root/input_data/237-134500-0000.flac')
story = text_generation(text=transcription['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 code summary.
ACTION TYPE:
done
ACTION CONTENT:
True

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