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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: Query: I recently uploaded a photo (407550-input.png) to my website's healthy recipes blog, but mistakenly used an image of a carrot instead of broccoli. Could you modify the picture to reflect this and provide a short, snappy description for the newly altered image? Further, can you distil this image caption into a quick concise tagline that I can use as a post title?
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 the function that can edit images based on a description, as we need to replace the carrot with broccoli in the image. After that, I will look for functions that can generate a caption for the image and distill it into a concise tagline.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: edit images)
(type: FUNCTION) AND (text: generate caption)
(type: FUNCTION) AND (text: concise tagline)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (text: edit images)

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[269, 323]
type=FUNCTION
content={
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']):
    """
    Edits an image based on a given text prompt using a specified model checkpoint.
    
    Args:
        image: The input image to be edited.
        prompt (str): The text prompt describing the desired edits.
        ckpt: The model checkpoint to use for image editing. Defaults to MODEL_SELECTION["image_editing"].
    
    Returns:
        A dictionary with the key 'image' containing the edited image.
    
    Raises:
        ImportError: If the necessary libraries for the chosen model are not installed.
    
    Examples:
        # Using the default checkpoint
        edited_image = image_editing(input_image, "A cat with a party hat.")
        
        # Using a specific checkpoint
        edited_image = image_editing(input_image, "A dog on a skateboard.", ckpt="timbrooks/instruct-pix2pix")
    """
    ...
}
---
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=[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
}
---
(23 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 23 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_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 image_crop_left(image, ckpt=None): ... # mnm/tool_api.py
def image_crop_top(image, ckpt=None): ... # mnm/tool_api.py
def image_crop_bottom(image, ckpt=None): ... # mnm/tool_api.py

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

CODE BLOCKS:
---
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=[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=[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."
    """
    ...
}
---
(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 get_full_path_data(filename): ... # mnm/tool_api.py
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py

QUERY:
(type: FUNCTION) AND (text: concise tagline)

CODE BLOCKS:
---
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']
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[178, 193]
type=FUNCTION
content={
def question_answering(
    question: str, text: str, ckpt=MODEL_SELECTION["question_answering"]
):  # alternative: "deepset/roberta-base-squad2"
    """
    It takes a text and a question, and outputs an answer to that question based on the text.
    """
    question = text_processing(question)
    text = text_processing(text)
    pipe = pipeline("question-answering", model=ckpt)
    result_raw = pipe(
        question=question, context=text
    )  # {'score': 0.01082150824368, 'start': 0, 'end': 10, 'answer': 'My name is'}
    result_formatted = {"text": result_raw["answer"]}

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

Prototypes for the next 7 out of 16 classes/functions found in unexpanded results (search again with the same query to see details):
def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py
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 optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_editing` from `tool_api.py` can be used to edit the image based on a text prompt. For generating a caption, the function `image_captioning` is available. To distill the caption into a concise tagline, I can use the `text_summarization` function which summarizes text. I will now write the code to perform these tasks step by step.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_editing, image_captioning, text_summarization

# Step 1: Edit the image to replace the carrot with broccoli
edited_image_result = image_editing(image='/root/input_data/407550-input.png', prompt='Replace the carrot with broccoli.')

# Step 2: Generate a caption for the edited image
# This step will be executed after confirming that Step 1 is successful

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_editing": <function image_editing at 0x7f3115113400>, "image_captioning": <function image_captioning at 0x7f3115113370>, "text_summarization": <function text_summarization at 0x7f31151130a0>, "edited_image_result": {'image': <PIL.Image.Image image mode=RGB size=496x496 at 0x7F2E0F52CD60>}}

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

------Action------
THOUGHT:
The image has been successfully edited. Now I will proceed to generate a caption for the edited image using the `image_captioning` function.
ACTION TYPE:
code
ACTION CONTENT:
# Step 2: Generate a caption for the edited image
caption_result = image_captioning(image=edited_image_result['image'])

# Step 3: Distill the caption into a concise tagline
# This step will be executed after confirming that Step 2 is successful

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"caption_result": {'text': 'there are four containers of food in the blue container'}}

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

------Action------
THOUGHT:
The caption for the edited image has been generated. Now I will use the `text_summarization` function to distill this caption into a concise tagline.
ACTION TYPE:
code
ACTION CONTENT:
# Step 3: Distill the caption into a concise tagline
tagline_result = text_summarization(text=caption_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):
{"tagline_result": {'text': 'There are four containers of food in the blue'}}

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

------Action------
THOUGHT:
The tagline has been successfully generated. The task is now complete, and I will provide a summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import image_editing, image_captioning, text_summarization
edited_image_result = image_editing(image='/root/input_data/407550-input.png', prompt='Replace the carrot with broccoli.')
caption_result = image_captioning(image=edited_image_result['image'])
tagline_result = text_summarization(text=caption_result['text'])

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

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

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

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