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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "With the specified file titled '12633.jpg', I'd like you to determine what the image is of. After that, could you generate a new stylized image of the same subject to use in my upcoming presentation?"
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 search for a function that can classify the image to determine what the image is of. Once I have the subject of the image, I can then search for a function that can generate a new stylized image based on the subject.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: image classification)

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

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[325, 346]
type=FUNCTION
content={
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']):
    """
    Classifies an image into one of the predefined categories using a Vision Transformer (ViT) model.
    
    Args:
        image: The image to be classified, which will be processed by `image_processing` function.
        ckpt (str, optional): The checkpoint or model identifier to use for loading the pre-trained model. Defaults to `MODEL_SELECTION["image_classification"]`.
    
    Returns:
        dict: A dictionary with a key "text" that maps to the predicted category of the image.
    
    Examples:
        >>> result = image_classification(my_image)
        >>> print(result)
        {'text': 'cat'}
    """
    ...
}
---
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=[731, 765]
type=FUNCTION
content={
def tag(image, objects, ckpt=None):
    """
    Tags objects in an image with bounding boxes and class labels.
    
    Args:
        image: The image to be processed and tagged.
        objects: A list of dictionaries, each containing 'bbox' (bounding box) and 'label' (class) for an object.
        ckpt (optional): A checkpoint or model state to use for tagging, not used in the current implementation.
    
    Returns:
        A dictionary with a key 'image' containing the tagged image.
    
    Examples:
        # Assuming 'image' is an image object and 'objects' is a list of object dictionaries with 'bbox' and 'label'
        result = tag(image, objects)
        tagged_image = result['image']
    """
    ...
}
---
(35 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 35 classes/functions found in unexpanded results (search again with the same query to see details):
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def image_generation(text: str, ckpt=MODEL_SELECTION['image_generation']): ... # mnm/tool_api.py
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_classification` from `tool_api.py` is suitable for classifying the image to determine its subject. After obtaining the classification, I will need to search for a function that can generate a stylized image based on the subject.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: image generation)

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

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=[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=[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
}
---
(31 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 automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def get_full_path_data(filename): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_generation` from `tool_api.py` can generate an image based on a text description. I will use the `image_classification` function to classify the image and then use the `image_generation` function to generate a new stylized image based on the classification result.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_classification, image_generation

# Classify the image to determine its subject
classification_result = image_classification(image='/root/input_data/12633.jpg')

# Generate a new stylized image based on the subject
stylized_image_result = image_generation(text=classification_result['text'])

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_classification": <function image_classification at 0x7f9d709070a0>, "image_generation": <function image_generation at 0x7f9d70906ef0>, "classification_result": {'text': 'steel drum'}, "stylized_image_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F9D708FBF10>}}

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

------Action------
THOUGHT:
The image has been successfully classified as a 'steel drum', and a new stylized image has been generated based on this classification. The task is complete.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import image_classification, image_generation
classification_result = image_classification(image='/root/input_data/12633.jpg')
stylized_image_result = image_generation(text=classification_result['text'])

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

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

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

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