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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I'm creating a fun presentation about the number 7 for my math class. Could you help me find a unique mathematical fact about the number 7 and then generate a relevant image that illustrates that fact? To make it more effective, I want you to crop the image to highlight the top portion."
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 find a function that can retrieve a unique mathematical fact about the number 7. I will search for a function related to retrieving facts about numbers.
ACTION TYPE:
search
ACTION CONTENT:
text: (mathematical OR unique OR fact) AND (number OR numbers)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: (mathematical OR unique OR fact) AND (number OR numbers)

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[879, 893]
type=FUNCTION
content={
def get_math_fact(number: str, ckpt=None):
    """
    It provides interesting math facts about numbers.
    """
    url = f"https://numbersapi.p.rapidapi.com/{number}/math"
    params = {"fragment": "true", "json": "true"}
    headers = {
        "X-RapidAPI-Key": RAPID_API_KEY,
        "X-RapidAPI-Host": "numbersapi.p.rapidapi.com",
    }
    response = requests.get(url, headers=headers, params=params)
    result_formatted = response.json()

    return result_formatted
}
---
file_path=mnm/tool_api.py
lines=[895, 909]
type=FUNCTION
content={
def get_trivia_fact(number: str, ckpt=None):
    """
    It provides interesting trivia facts about number.
    """
    url = f"https://numbersapi.p.rapidapi.com/{number}/trivia"
    params = {"fragment": "true", "json": "true"}
    headers = {
        "X-RapidAPI-Key": RAPID_API_KEY,
        "X-RapidAPI-Host": "numbersapi.p.rapidapi.com",
    }
    response = requests.get(url, headers=headers, params=params)
    result_formatted = response.json()

    return result_formatted
}
---
file_path=mnm/tool_api.py
lines=[840, 861]
type=FUNCTION
content={
def get_date_fact(date: str, ckpt=None):
    """
    It provides interesting facts about dates.
    """
    from dateutil import parser

    date_str = date
    dt = parser.parse(date_str)

    formatted_date = dt.strftime("%m/%d")

    url = f"https://numbersapi.p.rapidapi.com/{formatted_date}/date"
    params = {"fragment": "true", "json": "true"}
    headers = {
        "X-RapidAPI-Key": RAPID_API_KEY,
        "X-RapidAPI-Host": "numbersapi.p.rapidapi.com",
    }
    response = requests.get(url, headers=headers, params=params)
    result_formatted = response.json()

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

Prototypes for the next 7 out of 20 classes/functions found in unexpanded results (search again with the same query to see details):
def get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py
def count(objects, ckpt=None): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_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

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

------Action------
THOUGHT:
The `get_math_fact` function from the `tool_api.py` file seems to be the right choice for retrieving a unique mathematical fact about the number 7. Next, I will need to find a function that can generate an image based on the fact retrieved. I will search for a function related to generating images from text descriptions.
ACTION TYPE:
search
ACTION CONTENT:
text: (generate OR create) AND (image OR picture) AND (text OR description)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: (generate OR create) AND (image OR picture) AND (text OR description)

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

Prototypes for the next 7 out of 17 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 visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # 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 image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The `image_generation` function from the `tool_api.py` file is suitable for generating an image based on a text description. After generating the image, I will need to crop it to highlight the top portion. I will search for a function related to cropping images.
ACTION TYPE:
search
ACTION CONTENT:
text: crop AND image

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: crop AND image

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

Prototypes for the next 4 out of 4 classes/functions found in unexpanded results (search again with the same query to see details):
def image_crop_right(image, ckpt=None): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
I have found the `image_crop_top` function which can be used to crop the top half of an image. Now, I will write the code to first get a mathematical fact about the number 7 using `get_math_fact`, then generate an image based on that fact using `image_generation`, and finally crop the top portion of the generated image using `image_crop_top`.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_math_fact, image_generation, image_crop_top

# Retrieve a unique mathematical fact about the number 7
math_fact = get_math_fact(number="7")

# Generate an image based on the retrieved fact
generated_image_info = image_generation(text=math_fact['text'])

# Crop the top portion of the generated image
cropped_image_info = image_crop_top(image=generated_image_info['image'])

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"get_math_fact": <function get_math_fact at 0x7f31150e7d90>, "image_generation": <function image_generation at 0x7f31150e71c0>, "image_crop_top": <function image_crop_top at 0x7f31150e77f0>, "math_fact": {'text': 'the sum of any two opposite sides on a standard six-sided die', 'number': 7, 'found': True, 'type': 'math'}, "generated_image_info": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F31D35523E0>}, "cropped_image_info": {'image': <PIL.Image.Image image mode=RGB size=1023x512 at 0x7F31D58C7010>}}

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

------Action------
THOUGHT:
The code has been executed successfully, and the relevant variables indicate that we have obtained a mathematical fact about the number 7, generated an image based on that fact, and cropped the top portion of the image. I will now provide a summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_math_fact, image_generation, image_crop_top
output0 = get_math_fact(number="7")
output1 = image_generation(text=output0['text'])
output2 = image_crop_top(image=output1['image'])

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

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

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

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