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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: Can you provide me with an interesting fact about the year 2023, and then use that fact to generate a short story? 
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 an interesting fact about the year 2023. Once I have the fact, I will look for a function that can generate a short story based on the fact.
ACTION TYPE:
search
ACTION CONTENT:
text: interesting fact year
text: generate short story

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: interesting fact year

CODE BLOCKS:
---
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
}
---
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
}
---
(40 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_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # 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 visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def select_object(objects, object_name, ckpt=None): ... # mnm/tool_api.py

QUERY:
text: generate short story

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

Prototypes for the next 7 out of 24 classes/functions found in unexpanded results (search again with the same query to see details):
def get_full_path_data(filename): ... # mnm/tool_api.py
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 search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py
def audio_processing(audio_file): ... # mnm/tool_api.py
def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `get_year_fact` from `mnm/tool_api.py` can be used to retrieve an interesting fact about the year 2023. After obtaining the fact, I will use the `text_generation` function, which is mentioned in the prototypes of unexpanded results, to generate a short story based on that fact. I will now write the code to call these functions sequentially.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_year_fact, text_generation

# Retrieve an interesting fact about the year 2023
year_fact = get_year_fact("2023")

# Check if we got a fact successfully
if 'text' in year_fact:
    # Use the fact to generate a short story
    story = text_generation(text=year_fact['text'])
else:
    story = "Could not retrieve a fact for the year 2023."

year_fact, story

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': " in 2023, as part of Turkey's government's plans to alleviate congestion on the Bosphorus and create a new waterway for international shipping. The canal is expected to be a major infrastructure project and will drastically change the landscape of Istanbul."}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"get_year_fact": <function get_year_fact at 0x7f31150e7d00>, "text_generation": <function text_generation at 0x7f31150e6ef0>, "year_fact": {'text': 'Istanbul Canal will be finished', 'number': 2023, 'found': True, 'type': 'year'}, "story": {'text': " in 2023, as part of Turkey's government's plans to alleviate congestion on the Bosphorus and create a new waterway for international shipping. The canal is expected to be a major infrastructure project and will drastically change the landscape of Istanbul."}}

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

------Action------
THOUGHT:
The interesting fact about the year 2023 has been successfully retrieved, and it is about the completion of the Istanbul Canal. The text generation function seems to have returned a continuation of the fact rather than a short story. I will now attempt to use the text generation function again, but this time I will provide a more explicit prompt to generate a short story based on the fact about the Istanbul Canal.
ACTION TYPE:
code
ACTION CONTENT:
# Generate a short story based on the fact about the Istanbul Canal
prompt_for_story = "Tell a short story about the completion of the Istanbul Canal in 2023 and its impact on the city."
short_story = text_generation(text=prompt_for_story)

short_story

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': "In 2023, after years of planning and construction, the Istanbul Canal was finally completed. The canal, which runs parallel to the Bosphorus Strait, was designed to ease traffic congestion and reduce the risk of accidents in one of the busiest waterways in the world.\n\nThe completion of the Istanbul Canal had a significant impact on the city. Not only did it provide a more efficient and safer route for ships to travel through Istanbul, but it also created new opportunities for development and growth in the surrounding areas.\n\nWith the canal up and running, trade and transportation in Istanbul flourished. The city became a hub for international shipping and commerce, attracting businesses and investors from all over the world. This influx of economic activity brought a boost to the local economy and created thousands of new jobs for residents of Istanbul.\n\nFurthermore, the completion of the Istanbul Canal transformed the city's waterfront, turning it into a vibrant and bustling district with shops, restaurants, and entertainment venues. The canal became a popular tourist attraction, drawing visitors from far and wide to witness the marvel of modern engineering.\n\nOverall, the completion of the Istanbul Canal in 2023 was a game-changer for the city. It not only improved transportation and commerce but also enhanced the quality of life for the residents of Istanbul, making it a more dynamic and prosperous metropolis."}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"prompt_for_story": Tell a short story about the completion of the Istanbul Canal in 2023 and its impact on the city., "short_story": {'text': "In 2023, after years of planning and construction, the Istanbul Canal was finally completed. The canal, which runs parallel to the Bosphorus Strait, was designed to ease traffic congestion and reduce the risk of accidents in one of the busiest waterways in the world.\n\nThe completion of the Istanbul Canal had a significant impact on the city. Not only did it provide a more efficient and safer route for ships to travel through Istanbul, but it also created new opportunities for develop...}

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

------Action------
THOUGHT:
The short story based on the completion of the Istanbul Canal in 2023 has been successfully generated. The task is now complete.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_year_fact, text_generation
year_fact = get_year_fact("2023")
prompt_for_story = "Tell a short story about the completion of the Istanbul Canal in 2023 and its impact on the city."
short_story = text_generation(text=prompt_for_story)

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

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

------Action------
THOUGHT:
The task has been completed successfully, and the user has acknowledged receipt of the summary.
ACTION TYPE:
done
ACTION CONTENT:
True

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