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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: Given the '18404.jpg', I need to identify what's portrayed in it. Once we figure that out, could you create a new image that depicts the same subject? I would also like to only see the top portion of the newly 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:
First, I need to identify the subject in the image '18404.jpg'. I will search for an image classification function in the codebase that can classify the image into one of the 1000 ImageNet categories. Once I have the subject, I will then search for a function that can generate an image based on a text description. Finally, I will look for a function that can crop the top portion of an image, as the user wants to see only the top portion of the newly generated image.
ACTION TYPE:
search
ACTION CONTENT:
type: FUNCTION AND text: image classification
type: FUNCTION AND text: generate image
type: FUNCTION AND text: crop

-----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=[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=[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'}, ...]
    """
    ...
}
---
(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 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
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py

QUERY:
type: FUNCTION AND text: generate image

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

Prototypes for the next 7 out of 14 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 get_full_path_data(filename): ... # mnm/tool_api.py
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py

QUERY:
type: FUNCTION AND text: crop

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[536, 556]
type=FUNCTION
content={
def image_crop_left(image, ckpt=None):
    """
    Crops the left half of the provided image and returns it in a formatted dictionary.
    
    Args:
        image: The image to be cropped.
        ckpt: Optional checkpoint data (unused in the current implementation).
    
    Returns:
        A dictionary containing the cropped image with the key 'image'.
    
    Examples:
        Assuming `image` is a PIL Image object:
        >>> result = image_crop_left(image)
        >>> result['image']  # This will contain the left half of the original image.
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[581, 602]
type=FUNCTION
content={
def image_crop_top(image, ckpt=None):
    """
    Crops the top half of an image and returns it in a dictionary.
    
    Args:
        image: The image to be cropped.
        ckpt: Optional checkpoint data (unused in the current implementation).
    
    Returns:
        A dictionary containing the cropped image with the key 'image'.
    
    Examples:
        >>> from PIL import Image
        >>> original_image = Image.open('path/to/image.jpg')
        >>> cropped_image_info = image_crop_top(original_image)
        >>> cropped_image = cropped_image_info['image']
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[604, 625]
type=FUNCTION
content={
def image_crop_bottom(image, ckpt=None):
    """
    Crops the bottom half of an image and returns it in a dictionary.
    
    Args:
        image: The image to be cropped.
        ckpt: Optional checkpoint data (unused in the current implementation).
    
    Returns:
        A dictionary containing the cropped image with the key 'image'.
    
    Examples:
        Assuming `image` is a PIL Image object:
        >>> cropped_image_result = image_crop_bottom(image)
        >>> cropped_image = cropped_image_result['image']
    """
    ...
}
---
(All code blocks matching the query were returned.)

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

------Action------
THOUGHT:
The search results have provided the necessary functions to solve the user's query. I will use the `image_classification` function to identify the subject in the image '18404.jpg'. Then, I will use the `image_generation` function to create a new image that depicts the same subject. Finally, I will use the `image_crop_top` function to crop the top portion of the newly generated image as requested by the user.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_classification, image_generation, image_crop_top

# Load the image
image_path = '/root/input_data/18404.jpg'
with open(image_path, 'rb') as file:
    image_data = file.read()

# Identify the subject in the image
classification_result = image_classification(image_data)
subject = classification_result['text']

# Generate a new image based on the subject
generation_result = image_generation(text=subject)
generated_image = generation_result['image']

# Crop the top portion of the newly generated image
cropped_image_result = image_crop_top(generated_image)
cropped_image = cropped_image_result['image']

-----Response-----
RESPONSE (Execution Result):
EXECUTION ERROR:
Traceback (most recent call last):
  File "/root/codenav/codenav/environments/code_env.py", line 88, in exec_with_informative_errors
    exec(code_str, global_vars)
  File "<string>", line 9, in <module>
  File "/root/mnm/mnm/tool_api.py", line 338, in image_classification
    inputs = processor(images=image, return_tensors="pt")
  File "/opt/conda/lib/python3.10/site-packages/transformers/image_processing_utils.py", line 551, in __call__
    return self.preprocess(images, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/transformers/models/vit/image_processing_vit.py", line 233, in preprocess
    images = make_list_of_images(images)
  File "/opt/conda/lib/python3.10/site-packages/transformers/image_utils.py", line 162, in make_list_of_images
    raise ValueError(
ValueError: Invalid image type. Expected either PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray, but got <class 'NoneType'>.

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_classification": <function image_classification at 0x7f31150e7370>, "image_generation": <function image_generation at 0x7f31150e71c0>, "image_crop_top": <function image_crop_top at 0x7f31150e77f0>, "image_path": /root/input_data/18404.jpg, "file": <_io.BufferedReader name='/root/input_data/18404.jpg'>, "image_data": b'\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x00\x00\x01\x00\x01\x00\x00\xff\xdb\x00C\x00\x08\x06\x06\x07\x06\x05\x08\x07\x07\x07\t\t\x08\n\x0c\x14\r\x0c\x0b\x0b\x0c\x19\x12\x13\x0f\x14\x1d\x1a\x1f\x1e\x1d\x1a\x1c\x1c $.\' ",#\x1c\x1c(7),01444\x1f\'9=82<.342\xff\xdb\x00C\x01\t\t\t\x0c\x0b\x0c\x18\r\r\x182!\x1c!22222222222222222222222222222222222222222222222222\xff\xc0\x00\x11\x08\x01h\x01\xd7\x03\x01"\x00\x02\x11\x01\x03\x11\x01\xff\xc4\x00\x1f\x00\x00\x01\x05\x01\x01\x01\x01\x01\x01\x00\x00\x00\x...}

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

------Action------
THOUGHT:
The execution error indicates that the `image_classification` function expected an image of a specific type (PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor, or jax.ndarray), but received None. This is likely because the image data was read as bytes and not converted into an image object. I need to modify the code to load the image using an appropriate library (such as PIL) to create an image object before passing it to the `image_classification` function.
ACTION TYPE:
code
ACTION CONTENT:
from PIL import Image
import io

# Load the image using PIL
image_path = '/root/input_data/18404.jpg'
with open(image_path, 'rb') as file:
    image_data = file.read()
image = Image.open(io.BytesIO(image_data))

# Identify the subject in the image
classification_result = image_classification(image)
subject = classification_result['text']

# Generate a new image based on the subject
generation_result = image_generation(text=subject)
generated_image = generation_result['image']

# Crop the top portion of the newly generated image
cropped_image_result = image_crop_top(generated_image)
cropped_image = cropped_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):
{"Image": <module 'PIL.Image' from '/opt/conda/lib/python3.10/site-packages/PIL/Image.py'>, "io": <module 'io' from '/opt/conda/lib/python3.10/io.py'>, "image": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=471x360 at 0x7F310D623F10>, "classification_result": {'text': 'beaver'}, "subject": beaver, "generation_result": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F31D370AB60>}, "generated_image": <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F31D370AB60>, "cropped_image_result": {'image': <PIL.Image.Image image mode=RGB size=1023x512 at 0x7F31D3579F00>}, "cropped_image": <PIL.Image.Image image mode=RGB size=1023x512 at 0x7F31D3579F00>}

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

------Action------
THOUGHT:
The code executed without errors, and the relevant variables indicate that the subject 'beaver' was identified in the original image. A new image was generated based on this subject, and the top portion of the generated image was successfully cropped. The task is now complete.
ACTION TYPE:
code_summary
ACTION CONTENT:
from PIL import Image, ImageOps
import io
from mnm.tool_api import image_classification, image_generation, image_crop_top

# Load the image using PIL
image_path = '/root/input_data/18404.jpg'
with open(image_path, 'rb') as file:
    image_data = file.read()
image = Image.open(io.BytesIO(image_data))

# Identify the subject in the image
classification_result = image_classification(image)
subject = classification_result['text']

# Generate a new image based on the subject
generation_result = image_generation(text=subject)
generated_image = generation_result['image']

# Crop the top portion of the newly generated image
cropped_image_result = image_crop_top(generated_image)
cropped_image = cropped_image_result['image']

-----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