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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I'm prepping for a trivia night and I want to blow everyone's mind with some unknown facts about July 20. Can you find out some engaging ones and spin them into a fascinating anecdote for me? After, could you then condense it all down to a brief, snappy version as well?"
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 interesting facts about a specific date. I will search for a function related to information retrieval that can handle dates.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: trivia) AND (text: date)

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

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[973, 983]
type=FUNCTION
content={
def get_weather(lon: str, lat: str, ckpt=None):
    """
    Provides weather forecast data based on specific geographical coordinates.
    """
    url = "http://www.7timer.info/bin/api.pl"
    params = {"lon": lon, "lat": lat, "product": "civil", "output": "json"}
    response = requests.get(url, params=params)
    result_formatted = {"objects": response.json()["dataseries"]}

    return result_formatted
}
---
(All code blocks matching the query were returned.)

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

------Action------
THOUGHT:
The search did not return a function that directly retrieves trivia about dates. I will adjust the search query to look for a function that can retrieve facts or interesting information, which might be more general and not necessarily tied to the word "trivia".
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: facts OR text: information) AND (text: date)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (text: facts OR text: information) AND (text: date)

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

Prototypes for the next 7 out of 13 classes/functions found in unexpanded results (search again with the same query to see details):
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 image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def image_generation(text: str, ckpt=MODEL_SELECTION['image_generation']): ... # 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 image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `get_date_fact` from the `tool_api.py` file seems to be exactly what we need to retrieve interesting facts about a specific date. I will use this function to get facts about July 20. After retrieving the facts, I will use a text generation function to spin them into a fascinating anecdote. I will then search for a text summarization function to create a brief, snappy version of the anecdote.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_date_fact

# Retrieve interesting facts about July 20
facts_about_july_20 = get_date_fact("July 20")

-----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 0x7f9d766079a0>, "facts_about_july_20": {'text': 'Václav Havel resigns as president of Czechoslovakia', 'year': 1992, 'number': 202, 'found': True, 'type': 'date'}}

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

------Action------
THOUGHT:
Now that we have retrieved a fact about July 20, I will search for a text generation function to create a fascinating anecdote based on this fact. After that, I will look for a text summarization function to condense the anecdote into a brief version.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: text_generation)

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

CODE BLOCKS:
---
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=[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 19 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 get_full_path_data(filename): ... # mnm/tool_api.py
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py
def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # 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

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

------Action------
THOUGHT:
The function `text_generation` from the `tool_api.py` file will be used to generate a fascinating anecdote based on the fact retrieved about July 20. After generating the anecdote, I will use the `text_summarization` function to create a brief, snappy version of it.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import text_generation, text_summarization

# Generate a fascinating anecdote based on the fact about July 20
fact = facts_about_july_20['text']
year = facts_about_july_20['year']
anecdote_prompt = f"On July 20, {year}, a significant event unfolded: {fact}. Let me tell you a story about that day."
anecdote = text_generation(anecdote_prompt)

# Condense the anecdote into a brief, snappy version
brief_anecdote = text_summarization(anecdote['text'])

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': "On July 20, 1992, Václav Havel, the beloved playwright and dissident who had led Czechoslovakia through its transition from communism to democracy, announced his resignation as president.\n\nIt was a solemn day in Prague, as crowds gathered in Wenceslas Square to hear Havel's emotional speech. He spoke of the challenges facing the newly independent country, and expressed his hope for a peaceful and prosperous future.\n\nHavel's resignation marked the end of an era in Czechoslovakia. He had been a symbol of hope and resistance during the dark days of communism, and his leadership had been crucial in guiding the country towards freedom and democracy.\n\nAs he bid farewell to the presidency, Havel left behind a legacy of courage, integrity, and compassion. He would continue to be a voice for human rights and democracy, both in his own country and around the world.\n\nThe people of Czechoslovakia were saddened to see Havel go, but they knew that his legacy would live on. As the sun set on that historic day, they looked to the future with hope and determination, knowing that they would continue to build a better world for themselves and future generations."}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"text_generation": <function text_generation at 0x7f9d76606c20>, "text_summarization": <function text_summarization at 0x7f9d76606cb0>, "fact": Václav Havel resigns as president of Czechoslovakia, "year": 1992, "anecdote_prompt": On July 20, 1992, a significant event unfolded: Václav Havel resigns as president of Czechoslovakia. Let me tell you a story about that day., "anecdote": {'text': "On July 20, 1992, Václav Havel, the beloved playwright and dissident who had led Czechoslovakia through its transition from communism to democracy, announced his resignation as president.\n\nIt was a solemn day in Prague, as crowds gathered in Wenceslas Square to hear Havel's emotional speech. He spoke of the challenges facing the newly independent country, and expressed his hope for a peaceful and prosperous future.\n\nHavel's resignation marked the end of an era in Czechoslovakia. He..., "brief_anecdote": {'text': "On July 20, 1992, Czechoslovakian President Vaclav Havel resigned from his post. Havel's resignation marked the end of an era and the beginning of a new era for the country."}}

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

------Action------
THOUGHT:
The anecdote has been successfully generated and summarized. I will now provide a code summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_date_fact, text_generation, text_summarization
facts_about_july_20 = get_date_fact("July 20")
anecdote = text_generation(f"On July 20, {facts_about_july_20['year']}, a significant event unfolded: {facts_about_july_20['text']}. Let me tell you a story about that day.")
brief_anecdote = text_summarization(anecdote['text'])

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

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

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

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