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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "Can you tell me something interesting about July 11th, then condense the information into a concise and readable format, and finally, provide a related engaging visual representation?"
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 in the `mnm` codebase that can retrieve interesting facts about a specific date. Once I have the information, I will look for a function that can summarize the text into a concise format. Finally, I will search for a function that can provide a visual representation related to the date or the information retrieved. I will start by searching for the information retrieval function for dates.
ACTION TYPE:
search
ACTION CONTENT:
text: interesting facts about dates

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: interesting facts about dates

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

Prototypes for the next 7 out of 36 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 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_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def select_object(objects, object_name, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `get_date_fact` from `tool_api.py` is suitable for retrieving interesting facts about a specific date. I will use this function to get facts about July 11th. After obtaining the facts, I will search for a summarization function to condense the information. Lastly, I will look for a function that can generate a visual representation related to the date or the information.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_date_fact

# Retrieve interesting facts about July 11th
date_fact = get_date_fact("July 11")

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"get_date_fact": <function get_date_fact at 0x7f31150e7c70>, "date_fact": {'text': 'the Lumière brothers demonstrate film technology to scientists', 'year': 1895, 'number': 193, 'found': True, 'type': 'date'}}

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

------Action------
THOUGHT:
Now that I have the interesting fact about July 11th, I need to find a function that can summarize this information into a concise and readable format. I will search for a text summarization function in the `mnm` codebase.
ACTION TYPE:
search
ACTION CONTENT:
text: summarization

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: summarization

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[143, 163]
type=FUNCTION
content={
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']):
    """
    Summarizes a given text into a few sentences using a pre-trained model.
    
    Args:
        text (str): The text to be summarized.
        ckpt: The checkpoint or model identifier to use for the summarization pipeline. Defaults to a pre-selected model checkpoint.
    
    Returns:
        dict: A dictionary containing the summarized text under the key 'text'.
    
    Examples:
        summary = text_summarization("The quick brown fox jumps over the lazy dog.")
        print(summary)  # Outputs a summarized version of the input text.
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[985, 1011]
type=FUNCTION
content={
def wikipedia_simple_search(text: str, ckpt=None):
    """
    Performs a basic search on Wikipedia to retrieve a summary of the most relevant page.
    
    Args:
        text (str): The search query text.
        ckpt: Unused parameter, can be anything (default is None).
    
    Returns:
        dict: A dictionary containing the formatted text summary of the most relevant Wikipedia page.
    
    Raises:
        This function may raise exceptions related to network issues or JSON decoding if the Wikipedia API changes or is unreachable.
    
    Examples:
        result = wikipedia_simple_search("Python programming language")
        # result might be something like:
        # {'text': 'Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with its use of significant indentation.'}
    """
    ...
}
---
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'])
    """
    ...
}
---
(24 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 automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
def image_generation(text: str, ckpt=MODEL_SELECTION['image_generation']): ... # mnm/tool_api.py
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `text_summarization` from `tool_api.py` is suitable for summarizing the interesting fact about July 11th into a concise format. I will use this function to summarize the fact obtained previously. After summarizing the text, I will search for a function that can generate a visual representation related to the date or the information.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import text_summarization

# Summarize the interesting fact about July 11th
summary = text_summarization(date_fact['text'])

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"text_summarization": <function text_summarization at 0x7f31150e6f80>, "summary": {'text': 'Lumière brothers demonstrate film technology to scientists'}}

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

------Action------
THOUGHT:
Now that I have the summarized text, I need to find a function that can generate a visual representation related to the date or the information. I will search for a function that can generate images based on text descriptions.
ACTION TYPE:
search
ACTION CONTENT:
text: image generation

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: image generation

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

Prototypes for the next 7 out of 28 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 visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def get_full_path_data(filename): ... # 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 object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_generation` from `tool_api.py` is suitable for generating an image based on a text description. I will use this function to generate a visual representation related to the summarized fact about the Lumière brothers demonstrating film technology to scientists on July 11th.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation

# Generate a visual representation related to the summarized fact
visual_representation = image_generation(summary['text'])

-----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 0x7f31150e71c0>, "visual_representation": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F3115166F50>}}

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

------Action------
THOUGHT:
The task has been completed successfully. I retrieved an interesting fact about July 11th, summarized the information, and generated a visual representation related to the fact. I will now provide a summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_date_fact, text_summarization, image_generation
output0 = get_date_fact("July 11")
output1 = text_summarization(output0['text'])
output2 = image_generation(output1['text'])

-----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