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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "From an audio file named '237-134500-0000.flac', Is it possible to create a visual depiction of the narration and identify the key objects present in the generated image?"
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:
Before writing any code, I need to search for the functions that can transcribe the audio file to text and then generate an image from the text description. Once I have the image, I will need to identify key objects present in it. I will start by searching for the speech recognition function to transcribe the audio file.
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` can be used to transcribe the audio file to text. Next, I need to find a function that can generate an image from the text description. I will search for a function related to image generation from text.
ACTION TYPE:
search
ACTION CONTENT:
text: generate image from text

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: generate image from 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=[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...'}
    """
    ...
}
---
(38 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 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
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_generation` can be used to generate an image from the text description. After generating the image, I will need to identify key objects present in it. I will search for the object detection function to find the key objects in the generated image.
ACTION TYPE:
search
ACTION CONTENT:
text: object detection

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: object detection

CODE BLOCKS:
---
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'}, ...]
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[810, 838]
type=FUNCTION
content={
def select_object(objects, object_name, ckpt=None):
    """
    Selects an object from a list of objects based on the input object name.
    
    Args:
        objects (list): A list of dictionaries, where each dictionary represents an object with a "label" key.
        object_name (str): The name of the object to be selected.
        ckpt (optional): A checkpoint or state to resume from, not used in the current implementation.
    
    Returns:
        dict: A dictionary with a single key "object" that maps to the selected object dictionary.
    
    Examples:
        >>> objects_list = [{'label': 'apple'}, {'label': 'banana'}]
        >>> select_object(objects_list, 'apple')
        {'object': {'label': 'apple'}}
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[721, 729]
type=FUNCTION
content={
def count(objects, ckpt=None):
    """
    It takes a list of objects and returns the count of the objects.
    """
    objs = objects
    result_formatted = {"number": len(objs)}

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

Prototypes for the next 7 out of 29 classes/functions found in unexpanded results (search again with the same query to see details):
def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py
def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py
def object_processing(file_path): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
I have found the necessary functions to solve the user's query. I will now write the code to transcribe the audio file using `automatic_speech_recognition`, generate an image from the transcribed text using `image_generation`, and then detect objects in the generated image using `object_detection`.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import automatic_speech_recognition, image_generation, object_detection

# Transcribe the audio file to text
audio_file_path = '/root/input_data/237-134500-0000.flac'
transcription_result = automatic_speech_recognition(audio=audio_file_path)

# Generate an image from the transcribed text
text_description = transcription_result['text']
image_result = image_generation(text=text_description)

# Detect objects in the generated image
detected_objects_result = object_detection(image=image_result['image'])

-----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 0x7f7513347910>, "image_generation": <function image_generation at 0x7f75133479a0>, "object_detection": <function object_detection at 0x7f7513347c70>, "audio_file_path": /root/input_data/237-134500-0000.flac, "transcription_result": {'text': ' Frank read English slowly, and the more he read about this divorce case, the angrier he grew.'}, "text_description":  Frank read English slowly, and the more he read about this divorce case, the angrier he grew., "image_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F6C49CC9720>}, "detected_objects_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F6C4AC33970>, 'objects': [{'bbox': [249.62, 277.46, 309.91, 548.68], 'label': 'book'}, {'bbox': [885.44, 290.2, 930.62, 444.79], 'label': 'book'}, {'bbox': [980.25, -0.12, 1023.92, 198.25], 'label': 'book'}, {'bbox': [440.86, -0.44, 489.37, 186.93], 'label': 'book'}, {'bbox': [186.75, 272.87, 236.07, 603.5], 'label': 'book'}, {'bbox': [401.7, -0.61, 445.95, 191.63], 'label': 'book'}, {'bbox': [338.92, -0.38, 406.8, 189.75], 'label': ...}

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

------Action------
THOUGHT:
The code has been executed successfully, and the relevant variables have been updated with the results. The transcription of the audio file has been obtained, an image has been generated from the transcribed text, and objects have been detected in the generated image. I will now summarize the solution using the code_summary action.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import automatic_speech_recognition, image_generation, object_detection
output0 = automatic_speech_recognition(audio='/root/input_data/237-134500-0000.flac')
output1 = image_generation(text=output0['text'])
output2 = object_detection(image=output1['image'])

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

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

------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