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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "My son is turning seven and I want to make a unique birthday card for him. Get me a math fact focused on the number 7. Then, let's make a fitting image using this fact, something that screams birthday fun and numbers! Finally, modify the image by overlaying a big, bold 'Happy 7th Birthday!' text at the top. Use Overlay big, bold, and colourful 'Happy 7th Birthday!' text at the top of the image. for the image modification instructions."
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 math fact focused on the number 7. I will search for a function that can handle information retrieval tasks, specifically one that can provide facts about numbers.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: fact) AND (text: number)

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

CODE BLOCKS:
---
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=[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=[863, 877]
type=FUNCTION
content={
def get_year_fact(year: str, ckpt=None):
    """
    It provides interesting facts about years.
    """
    url = f"https://numbersapi.p.rapidapi.com/{year}/year"
    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
}
---
(All code blocks matching the query were returned.)

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

------Action------
THOUGHT:
The function `get_math_fact` from the `tool_api.py` file seems to be the right choice for retrieving a math fact about the number 7. I will write code to call this function with the number 7 as an argument to get the math fact.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_math_fact

# Retrieve a math fact about the number 7
math_fact = get_math_fact('7')

-----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 0x7f5e7070bd00>, "math_fact": {'text': 'the only dimension, besides the familiar 3, in which a vector cross product can be defined', 'number': 7, 'found': True, 'type': 'math'}}

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

------Action------
THOUGHT:
Now that we have a math fact about the number 7, the next step is to create an image that incorporates this fact and has a birthday theme. I will search for a function that can generate images based on a text description.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: generate) AND (text: image) AND (text: description)

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

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

Prototypes for the next 7 out of 10 classes/functions found in unexpanded results (search again with the same query to see details):
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # 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_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # 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 image_crop(image, object, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_generation` from the `tool_api.py` file is suitable for generating an image based on a text description. I will write code to call this function with a description that includes the math fact about the number 7 and a birthday theme.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation

# Generate an image with a birthday theme and the math fact about the number 7
description = f"A birthday celebration with balloons, cake, and the number 7, which is the only dimension, besides the familiar 3, in which a vector cross product can be defined."
birthday_image = image_generation(description)

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_generation": <function image_generation at 0x7f5e7070b130>, "description": A birthday celebration with balloons, cake, and the number 7, which is the only dimension, besides the familiar 3, in which a vector cross product can be defined., "birthday_image": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F5B7F2B8820>}}

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

------Action------
THOUGHT:
With the generated birthday image, the next step is to overlay the text "Happy 7th Birthday!" on top of the image. I will search for a function that can edit images based on a description, which should include the capability to overlay text.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: edit) AND (text: image) AND (text: overlay) AND (text: text)

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

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[24, 49]
type=FUNCTION
content={
def save_output(task_idx, node_idx, output_dict, result_folder):
    new_dict = deepcopy(output_dict)
    full_result_path = os.path.join(result_folder, str(task_idx))
    if not os.path.exists(full_result_path):
        os.makedirs(full_result_path)
    for key, output in output_dict.items():
        # Determine the type of output and set the appropriate file extension
        if isinstance(output, Image.Image):
            file_path = os.path.join(full_result_path, f"node-{node_idx}.jpg")
            output.save(file_path)
            new_dict[key] = file_path

    print("Output dict to be saved:", new_dict)
    output_dict_path_json = os.path.join(full_result_path, f"node-{node_idx}.json")
    output_dict_path_pickle = os.path.join(full_result_path, f"node-{node_idx}.pkl")
    try:
        with open(output_dict_path_json, "w") as file:
            json.dump(new_dict, file)
        return output_dict_path_json
    except TypeError as e:
        if os.path.exists(output_dict_path_json):
            os.remove(output_dict_path_json)
        with open(output_dict_path_pickle, "wb") as file:
            pickle.dump(new_dict, file)
        return output_dict_path_pickle
}
---
(All code blocks matching the query were returned.)

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

------Action------
THOUGHT:
The search did not return a function specifically for overlaying text on an image. I will broaden the search to find an image editing function that can handle the description provided by the user.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: image_editing)

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

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

Prototypes for the next 7 out of 26 classes/functions found in unexpanded results (search again with the same query to see details):
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 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
def image_crop_right(image, ckpt=None): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_editing` from the `tool_api.py` file can be used to edit the image based on a text prompt. I will write code to call this function with the generated image and the prompt to overlay "Happy 7th Birthday!" text at the top of the image.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_editing

# Overlay "Happy 7th Birthday!" text on the generated birthday image
overlay_prompt = "Overlay big, bold, and colourful 'Happy 7th Birthday!' text at the top of the image."
final_image = image_editing(birthday_image['image'], overlay_prompt)

-----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 0x7f5e7070b250>, "overlay_prompt": Overlay big, bold, and colourful 'Happy 7th Birthday!' text at the top of the image., "final_image": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F5E648FE230>}}

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

------Action------
THOUGHT:
The image has been successfully modified with the overlay text. The task is now complete. I will provide a summary of the code used to solve the user's query.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_math_fact, image_generation, image_editing
math_fact = get_math_fact('7')
description = "A birthday celebration with balloons, cake, and the number 7, which is the only dimension, besides the familiar 3, in which a vector cross product can be defined."
birthday_image = image_generation(description)
overlay_prompt = "Overlay big, bold, and colourful 'Happy 7th Birthday!' text at the top of the image."
final_image = image_editing(birthday_image['image'], overlay_prompt)

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

********************************************************************************
Step 10
********************************************************************************

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

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