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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I am preparing for a trivia night with my friends. Can you help me create a fascinating story based on historical events that happened on 4th of May to impress them?"
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:
Before writing any code, I need to find a function in the `mnm` codebase that can retrieve historical events for a given date. I will search for a function that might be related to retrieving information about dates or historical events.
ACTION TYPE:
search
ACTION CONTENT:
text: historical events date

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: historical events 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=[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
}
---
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
}
---
(41 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 35 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_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # 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 the `mnm` codebase seems to be exactly what I need to retrieve interesting facts about a specific date. I will use this function to get historical events that happened on the 4th of May.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_date_fact

# Retrieve historical events for the 4th of May
date = "05/04"
historical_events = get_date_fact(date)
print(historical_events)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'the Cornwall Railway opens across the Royal Albert Bridge linking the counties of Devon and Cornwall in England', 'year': 1859, 'number': 125, 'found': True, 'type': 'date'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"get_date_fact": <function get_date_fact at 0x7f751332fbe0>, "date": 05/04, "historical_events": {'text': 'the Cornwall Railway opens across the Royal Albert Bridge linking the counties of Devon and Cornwall in England', 'year': 1859, 'number': 125, 'found': True, 'type': 'date'}}

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

------Action------
THOUGHT:
I have successfully retrieved a historical event for the 4th of May. Now, I will use this information to create a fascinating story. To do this, I need to find a function in the `mnm` codebase that can generate text or stories based on input data.
ACTION TYPE:
search
ACTION CONTENT:
text: generate text story

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: generate text 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.'}
    """
    ...
}
---
(28 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 23 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 get_full_path_data(filename): ... # mnm/tool_api.py
def tag(image, objects, ckpt=None): ... # 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 image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `text_generation` mentioned in the unexpanded results seems to be the right choice for generating a story based on the historical event I retrieved. I will search again to get the details of this function so I can use it to generate the story.
ACTION TYPE:
search
ACTION CONTENT:
text: text_generation

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
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=[348, 375]
type=FUNCTION
content={
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']):
    """
    Processes an image and a question about the image, then generates an answer using a pre-trained model.
    
    Args:
        image: The image to be processed.
        question: The question related to the image.
        ckpt: The checkpoint or model identifier for the pre-trained model. Defaults to MODEL_SELECTION["visual_question_answering"].
    
    Returns:
        A dictionary with a key 'text' containing the generated answer to the question.
    
    Examples:
        >>> answer = visual_question_answering(my_image, "What is in this image?")
        >>> print(answer['text'])
        "A cat sitting on a windowsill."
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[17, 22]
type=FUNCTION
content={
def get_full_path_data(
    filename,
):  # get the path of data which isn't generated during the process
    base_path = MNM_BASE_PATH_DATA
    return os.path.join(base_path, filename)
}
---
(21 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_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
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def text_processing(file_path): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
I have found the `text_generation` function which can be used to generate a continuation of the given input text. I will use this function to create a story based on the historical event that happened on the 4th of May.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import text_generation

# Historical event text to be used as a prompt for text generation
historical_event_text = historical_events['text']

# Generate a story based on the historical event
story_prompt = f"On the 4th of May, {historical_event_text}."
generated_story = text_generation(story_prompt)
print(generated_story)

-----Response-----
RESPONSE (Execution Result):
STDOUT (STDOUT was too long. Showing only the start and end separated by ellipsis.):
{'text': 'This new railway line provides a vital link for passengers and freight between the two counties, improving transportation and trade in the region. The Royal Albert Bridge, designed by renowned engineer Isambard Kingdom Brunel, is a masterpiece of Victorian engineering and a landmark in the area.\n\nThe opening of the Cornwall Railway marks a significant milestone in the development of rail transportation in the southwest of England, further connecting the region to the rest of the country. The line will provide quicker and more reliable travel for passengers and goods, boosting economic growth and opportunities for the local communities along the route.\n\nThe Cornwall Railway is set to revolutionize travel in the region, providing a faster and more efficient way of crossing the River Tamar and connecting Devon and Cornwall. This new railway line is expected to bring about a new era of prosperity and growth for the southwest of England, as it opens up new opportunities for tr

...

vital link for passengers and freight between the two counties, improving transportation and trade in the region. The Royal Albert Bridge, designed by renowned engineer Isambard Kingdom Brunel, is a masterpiece of Victorian engineering and a landmark in the area.\n\nThe opening of the Cornwall Railway marks a significant milestone in the development of rail transportation in the southwest of England, further connecting the region to the rest of the country. The line will provide quicker and more reliable travel for passengers and goods, boosting economic growth and opportunities for the local communities along the route.\n\nThe Cornwall Railway is set to revolutionize travel in the region, providing a faster and more efficient way of crossing the River Tamar and connecting Devon and Cornwall. This new railway line is expected to bring about a new era of prosperity and growth for the southwest of England, as it opens up new opportunities for trade and travel between the two counties.'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"text_generation": <function text_generation at 0x7f751332ee60>, "historical_event_text": the Cornwall Railway opens across the Royal Albert Bridge linking the counties of Devon and Cornwall in England, "story_prompt": On the 4th of May, the Cornwall Railway opens across the Royal Albert Bridge linking the counties of Devon and Cornwall in England., "generated_story": {'text': 'This new railway line provides a vital link for passengers and freight between the two counties, improving transportation and trade in the region. The Royal Albert Bridge, designed by renowned engineer Isambard Kingdom Brunel, is a masterpiece of Victorian engineering and a landmark in the area.\n\nThe opening of the Cornwall Railway marks a significant milestone in the development of rail transportation in the southwest of England, further connecting the region to the rest of the coun...}

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

------Action------
THOUGHT:
The story has been successfully generated based on the historical event of the 4th of May. I will now provide a summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_date_fact, text_generation
output0 = get_date_fact(date="05/04")
output1 = text_generation(text=f"On the 4th of May, {output0['text']}.")

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

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

------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