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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I have written a kids' story and I need some illustrations. For the scene where 'There is a baseball player who swung for the ball', could you generate an appropriate image for it? Afterwards, please extract whatever letters or numbers might exist within the image as text. I'd then wish to have a follow-up story context based on this detected text to continue the narrative."
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 generate an image based on a text description. I will search for a function that can handle image generation from text. After generating the image, I will look for a function that can perform optical character recognition (OCR) to extract any letters or numbers from the image. Finally, I will search for a function that can generate text based on the extracted text to continue the narrative.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: generate image description)
(type: FUNCTION) AND (text: optical character recognition)
(type: FUNCTION) AND (text: generate text)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (text: generate image description)

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=[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=[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
}
---
(34 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 visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py
def get_full_path_data(filename): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py

QUERY:
(type: FUNCTION) AND (text: optical character recognition)

CODE BLOCKS:
---
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=[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=[51, 77]
type=FUNCTION
content={
def text_processing(file_path):
    """
    preprocess the text file

    Args:
    - the path of .doc .docx .txt file

    Returns:
    - text with string format

    Notes:
    - This function is designed for text input.

    """

    # Check the file extension
    if file_path.endswith(".txt"):
        with open(file_path, "r") as file:
            content = file.read()
    elif file_path.endswith(".doc") or file_path.endswith(".docx"):
        # Use textract to extract text from doc and docx files
        content = textract.process(file_path).decode("utf-8")
    else:
        # if the file is not .txt .doc .docx, then it is a string, directly return the stirng
        return file_path
    return content
}
---
(32 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 32 classes/functions found in unexpanded results (search again with the same query to see details):
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py
def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py

QUERY:
(type: FUNCTION) AND (text: generate text)

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=[17, 22]
type=FUNCTION
content={
def get_full_path_data(
    filename,
):  # get the path of data which isn't generated during the process
    base_path = MNM_BASE_PATH_DATA
    return os.path.join(base_path, filename)
}
---
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."
    """
    ...
}
---
(18 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 tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # 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 wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The search results have provided us with the necessary functions to solve the user's query. We can use the `image_generation` function to generate an image based on the text description of the scene. Then, we can use the `optical_character_recognition` function to extract any letters or numbers from the generated image. Finally, we can use the `text_generation` function to create a follow-up story context based on the detected text. I will now write the code to execute these steps sequentially.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation, optical_character_recognition, text_generation

# Generate an image based on the text description
description = "There is a baseball player who swung for the ball"
generated_image = image_generation(description)

# Extract letters or numbers from the generated image as text
extracted_text = optical_character_recognition(generated_image['image'])

# Generate a follow-up story context based on the detected text
follow_up_context = text_generation(extracted_text['text'])

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'Hello! How can I assist you today?'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_generation": <function image_generation at 0x7f751332f130>, "optical_character_recognition": <function optical_character_recognition at 0x7f751332f520>, "text_generation": <function text_generation at 0x7f751332ee60>, "description": There is a baseball player who swung for the ball, "generated_image": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F73FE5F19F0>}, "extracted_text": {'text': ''}, "follow_up_context": {'text': 'Hello! How can I assist you today?'}}

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

------Action------
THOUGHT:
The code execution results show that the `image_generation` function successfully created an image, but the `optical_character_recognition` function did not detect any text within the image, as indicated by the empty string in `extracted_text`. This is likely because there were no letters or numbers present in the generated image to extract. Consequently, the `text_generation` function produced a generic greeting text, which is not related to the story context. Since there is no detected text to continue the narrative, I will inform the user that no text was detected in the image and therefore no follow-up story context can be generated based on text from the image.
ACTION TYPE:
done
ACTION CONTENT:
False

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

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

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

-----Response-----
RESPONSE (User Message):
Please immediately take a code_summary action to generate a summary of your code produced so far. If you have not produced any code yet, make an educated guess based on what you have learned so far.

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

------Action------
THOUGHT:
The user has requested a code summary of the actions taken so far. I will summarize the code that was written to generate an image from a text description, perform optical character recognition on the generated image, and attempt to generate a follow-up story context based on the detected text.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import image_generation, optical_character_recognition, text_generation
output0 = image_generation(text='There is a baseball player who swung for the ball')
output1 = optical_character_recognition(image=output0['image'])
output2 = text_generation(text=output1['text'])

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