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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "Looking at the photo labelled as image 2375698.jpg, I am curious to know which place it is. Once we identify the location, it would be great if you could create a new image that represents the cultural aspects or landmarks of that city. Could you also produce a captivating caption for the new image that encompasses the essence of the city?"
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 identify the location depicted in the image "image 2375698.jpg". To do this, I will search for a function in the `mnm` codebase that can perform image captioning or image classification, as these functions might provide information about the location. Once the location is identified, I will look for a function that can generate images based on text descriptions, which could be used to create an image representing the cultural aspects or landmarks of the identified city. Finally, I will search for a function that can generate captions for images to create a captivating caption for the new image.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: image classification)
(type: FUNCTION) AND (text: image captioning)
(type: FUNCTION) AND (text: generate images)
(type: FUNCTION) AND (text: caption)

-----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

QUERY:
(type: FUNCTION) AND (text: image captioning)

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=[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=[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
}
---
(33 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 33 classes/functions found in unexpanded results (search again with the same query to see details):
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
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 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 image_processing(img): ... # mnm/tool_api.py
def image_crop_left(image, ckpt=None): ... # mnm/tool_api.py

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

CODE BLOCKS:
---
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."
    """
    ...
}
---
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=[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)
}
---
(21 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 21 classes/functions found in unexpanded results (search again with the same query to see details):
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # 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 image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # 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

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

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=[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
}
---
(12 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 12 classes/functions found in unexpanded results (search again with the same query to see details):
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def select_object(objects, object_name, ckpt=None): ... # mnm/tool_api.py
def text_processing(file_path): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The `image_captioning` function from the `mnm` codebase can be used to generate a caption for the image "image 2375698.jpg", which may include information about the location. Once the location is identified, I can use the `image_generation` function to create a new image that represents the cultural aspects or landmarks of that city. Finally, I can use the `image_captioning` function again to produce a captivating caption for the new image. I will now write the code to execute these steps.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_captioning, image_generation

# Step 1: Generate a caption for the image to identify the location
image_path = '/root/input_data/2375698.jpg'
caption_result = image_captioning(image_path)

# Assuming the location is mentioned in the caption, we will use it to generate a new image
# Step 2: Generate an image representing the cultural aspects or landmarks of the identified city
# For demonstration purposes, let's assume the location is "Paris"
# The user will need to replace 'Paris' with the actual location from the caption_result
generated_image_result = image_generation('Cultural landmarks of Paris')

# Step 3: Generate a captivating caption for the new image
# Assuming the generated image is stored in generated_image_result['image']
new_image_caption_result = image_captioning(generated_image_result['image'])

# Output the results
print("Original Image Caption:", caption_result['text'])
print("Generated Image:", generated_image_result['image'])
print("Caption for the New Image:", new_image_caption_result['text'])

-----Response-----
RESPONSE (Execution Result):
STDOUT:
Original Image Caption: there is a train that is coming down the tracks at the station
Generated Image: <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F9E42420DC0>
Caption for the New Image: arafed image of a wallpaper with a drawing of a city

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_captioning": <function image_captioning at 0x7f9d7660f490>, "image_generation": <function image_generation at 0x7f9d7660f400>, "image_path": /root/input_data/2375698.jpg, "caption_result": {'text': 'there is a train that is coming down the tracks at the station'}, "generated_image_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F9E42420DC0>}, "new_image_caption_result": {'text': 'arafed image of a wallpaper with a drawing of a city'}}

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

------Action------
THOUGHT:
The caption generated for the original image suggests that the location might be a train station, but it does not provide a specific city name. Since the task requires identifying the city to generate a new image representing its cultural aspects or landmarks, I will use the `visual_question_answering` function to ask a question about the location of the train station in the image. Once the city is identified, I will proceed to generate a new image and caption based on the city's cultural aspects.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import visual_question_answering

# Step 1: Use visual question answering to identify the city by asking about the location of the train station
question_about_location = "Which city is this train station located in?"
location_result = visual_question_answering(image_path, question_about_location)

# Output the result
print("Identified City:", location_result['text'])

-----Response-----
RESPONSE (Execution Result):
STDOUT:
Identified City: london

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"visual_question_answering": <function visual_question_answering at 0x7f9d7660f640>, "question_about_location": Which city is this train station located in?, "location_result": {'text': 'london'}}

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

------Action------
THOUGHT:
The city has been identified as London. Now I will proceed to generate a new image that represents the cultural aspects or landmarks of London. After generating the image, I will use the `image_captioning` function to create a captivating caption for the new image.
ACTION TYPE:
code
ACTION CONTENT:
# Step 2: Generate an image representing the cultural aspects or landmarks of London
generated_image_result = image_generation('Cultural landmarks of London')

# Step 3: Generate a captivating caption for the new image
# Assuming the generated image is stored in generated_image_result['image']
new_image_caption_result = image_captioning(generated_image_result['image'])

# Output the results
print("Generated Image:", generated_image_result['image'])
print("Caption for the New Image:", new_image_caption_result['text'])

-----Response-----
RESPONSE (Execution Result):
STDOUT:
Generated Image: <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F9E35497280>
Caption for the New Image: araffatured double decker buses are driving down a busy street

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"generated_image_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F9E35497280>}, "new_image_caption_result": {'text': 'araffatured double decker buses are driving down a busy street'}}

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

------Action------
THOUGHT:
The task has been completed. A new image representing the cultural aspects or landmarks of London has been generated, and a caption for the new image has been created. I will now provide a summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import image_captioning, image_generation, visual_question_answering
image_path = '/root/input_data/2375698.jpg'
caption_result = image_captioning(image_path)
question_about_location = "Which city is this train station located in?"
location_result = visual_question_answering(image_path, question_about_location)
generated_image_result = image_generation('Cultural landmarks of London')
new_image_caption_result = image_captioning(generated_image_result['image'])

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

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

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

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