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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: I'm working on a digital art project around the movie "Moonlight" from 2016 and need some inspiration. Could you find out the basic information about the movie and use the details to generate an image that captures its essence? Afterwards, could you help me understand this image by breaking it down into different parts?
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 retrieve factual information about the movie "Moonlight" from 2016. I will search for a function in the codebase that can retrieve information about movies. Once I have the information, I can use it to generate an image that captures the essence of the movie. After generating the image, I will look for a function that can analyze the image and break it down into different parts.
ACTION TYPE:
search
ACTION CONTENT:
text: movie AND (type: FUNCTION)

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

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[943, 971]
type=FUNCTION
content={
def search_movie(movie_title: str, movie_year: str, ckpt=None):
    """
    Retrieve basic movie information, including title, year, genre, and director.
    """
    # paren_start = input['movie_title'].find('(')
    url = "http://www.omdbapi.com/"
    params = {
        "t": movie_title,
        "y": movie_year,
        "plot": "short",
        "r": "json",
        "apikey": OMDB_API_KEY,
    }
    response = requests.get(url, params=params)
    result_formatted = response.json()

    selected_keys = ["Title", "Year", "Genre", "Director", "Plot"]
    desc = ""
    if len(result_formatted) > 0:
        for k, v in result_formatted.items():
            # print(k, v)
            if k in selected_keys and len(v) > 0:
                desc += f"{k}: {v}\n"
    else:
        desc = "Movie not found!"
    result_formatted = {"text": desc}

    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=[491, 534]
type=FUNCTION
content={
def image_crop(image, object, ckpt=None):
    """
    Crops an image to the region within a specified bounding box.
    
    Args:
        image: The image to be cropped.
        object: A dictionary containing the bounding box coordinates with the key 'bbox'.
        ckpt: Optional; a checkpoint or state to use for additional processing (not used in the current implementation).
    
    Returns:
        A dictionary with the key 'image' containing the cropped image.
    
    Raises:
        ValueError: If the bounding box is a string and cannot be converted to a list of coordinates.
    
    Examples:
        # Assuming `img` is an image object and `obj` is a dict with bbox coordinates
        cropped_image_result = image_crop(img, obj)
        cropped_image = cropped_image_result['image']
    """
    ...
}
---
(8 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 8 classes/functions found in unexpanded results (search again with the same query to see details):
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py
def get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def get_math_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def get_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py
def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py
def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `search_movie` from the codebase can be used to retrieve basic information about the movie "Moonlight". I will use this function to get the details needed for inspiration. After obtaining the movie information, I will search for a function that can generate an image based on text description to capture the essence of the movie. Finally, I will look for a function that can analyze the image and break it down into different parts.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import search_movie

# Retrieve basic information about the movie "Moonlight" from 2016
movie_info = search_movie("Moonlight", "2016")
print(movie_info)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'Title: Moonlight\nYear: 2016\nGenre: Drama\nDirector: Barry Jenkins\nPlot: A young African-American man grapples with his identity and sexuality while experiencing the everyday struggles of childhood, adolescence, and burgeoning adulthood.\n'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"search_movie": <function search_movie at 0x7f5e7070bf40>, "movie_info": {'text': 'Title: Moonlight\nYear: 2016\nGenre: Drama\nDirector: Barry Jenkins\nPlot: A young African-American man grapples with his identity and sexuality while experiencing the everyday struggles of childhood, adolescence, and burgeoning adulthood.\n'}}

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

------Action------
THOUGHT:
Now that I have the movie information, I will search for a function that can generate an image based on a text description. This will help in creating an image that captures the essence of the movie "Moonlight". After generating the image, I will proceed to find a function that can analyze the image and break it down into different parts.
ACTION TYPE:
search
ACTION CONTENT:
text: generate AND image AND (type: FUNCTION)

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

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

Prototypes for the next 7 out of 9 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 tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # 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
def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_generation` can be used to generate an image based on a text description. I will use the movie's plot description obtained from the `search_movie` function to generate an image that captures the essence of the movie "Moonlight". After generating the image, I will search for a function that can analyze the image and break it down into different parts.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation

# Use the plot description from the movie "Moonlight" to generate an image
plot_description = "A young African-American man grapples with his identity and sexuality while experiencing the everyday struggles of childhood, adolescence, and burgeoning adulthood."
generated_image = image_generation(plot_description)
print(generated_image)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F5E69F76CB0>}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_generation": <function image_generation at 0x7f5e7070b130>, "plot_description": A young African-American man grapples with his identity and sexuality while experiencing the everyday struggles of childhood, adolescence, and burgeoning adulthood., "generated_image": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F5E69F76CB0>}}

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

------Action------
THOUGHT:
Now that we have generated an image, I will search for a function that can detect and segment objects within the image. This will help us understand the image by breaking it down into different parts.
ACTION TYPE:
search
ACTION CONTENT:
text: detect AND objects AND (type: FUNCTION)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: detect AND objects AND (type: FUNCTION)

CODE BLOCKS:
---
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'}, ...]
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[721, 729]
type=FUNCTION
content={
def count(objects, ckpt=None):
    """
    It takes a list of objects and returns the count of the objects.
    """
    objs = objects
    result_formatted = {"number": len(objs)}

    return result_formatted
}
---
file_path=mnm/tool_api.py
lines=[810, 838]
type=FUNCTION
content={
def select_object(objects, object_name, ckpt=None):
    """
    Selects an object from a list of objects based on the input object name.
    
    Args:
        objects (list): A list of dictionaries, where each dictionary represents an object with a "label" key.
        object_name (str): The name of the object to be selected.
        ckpt (optional): A checkpoint or state to resume from, not used in the current implementation.
    
    Returns:
        dict: A dictionary with a single key "object" that maps to the selected object dictionary.
    
    Examples:
        >>> objects_list = [{'label': 'apple'}, {'label': 'banana'}]
        >>> select_object(objects_list, 'apple')
        {'object': {'label': 'apple'}}
    """
    ...
}
---
(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 tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py
def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py
def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py
def object_processing(file_path): ... # mnm/tool_api.py
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `object_detection` can be used to detect and segment objects within the generated image. I will use this function to analyze the image and break it down into different parts. This will help us understand the different elements present in the image.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import object_detection

# Perform object detection on the generated image to understand it by breaking it down into different parts
detected_objects = object_detection(generated_image['image'])
print(detected_objects)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F5CE48EE2C0>, 'objects': [{'bbox': [899.39, 623.32, 1023.61, 819.48], 'label': 'person'}, {'bbox': [354.35, 415.03, 451.55, 960.61], 'label': 'tie'}, {'bbox': [42.18, 295.18, 161.89, 515.72], 'label': 'person'}, {'bbox': [894.99, 355.88, 1023.75, 543.79], 'label': 'person'}, {'bbox': [879.82, 627.79, 931.06, 772.86], 'label': 'person'}, {'bbox': [891.89, 770.16, 1005.02, 957.0], 'label': 'person'}, {'bbox': [586.38, 428.93, 704.99, 895.24], 'label': 'tie'}, {'bbox': [667.12, 101.36, 828.96, 311.5], 'label': 'person'}, {'bbox': [364.19, 394.59, 672.1, 921.97], 'label': 'tie'}, {'bbox': [736.35, 361.88, 858.84, 541.18], 'label': 'person'}, {'bbox': [103.41, 36.37, 902.09, 1010.76], 'label': 'person'}, {'bbox': [-0.01, 786.34, 139.12, 981.34], 'label': 'person'}, {'bbox': [843.06, 236.07, 907.79, 333.02], 'label': 'person'}, {'bbox': [904.46, 161.18, 1023.69, 334.86], 'label': 'person'}, {'bbox': [209.3, 82.98, 343.77, 247.09], 'label': 'person'}]}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"object_detection": <function object_detection at 0x7f5e7070b400>, "detected_objects": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F5CE48EE2C0>, 'objects': [{'bbox': [899.39, 623.32, 1023.61, 819.48], 'label': 'person'}, {'bbox': [354.35, 415.03, 451.55, 960.61], 'label': 'tie'}, {'bbox': [42.18, 295.18, 161.89, 515.72], 'label': 'person'}, {'bbox': [894.99, 355.88, 1023.75, 543.79], 'label': 'person'}, {'bbox': [879.82, 627.79, 931.06, 772.86], 'label': 'person'}, {'bbox': [891.89, 770.16, 1005.02, 957.0], 'label': 'person'}, {'bbox': [586.38, 428.93, 704.99, 8...}

********************************************************************************
Step 8
********************************************************************************

------Action------
THOUGHT:
The object detection function has successfully identified various objects in the generated image, including multiple instances of 'person' and 'tie'. This breakdown provides insight into the composition of the image. 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 search_movie, image_generation, object_detection
output0 = search_movie("Moonlight", "2016")
output1 = image_generation(output0['text'])
output2 = object_detection(output1['image'])

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

********************************************************************************
Step 9
********************************************************************************

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

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