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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I am planning a special event in honor of what happened on 24th of August in history. Could you give me a historical fact about it and then create a speech, inspired by that fact, that I could present at the event? Once the speech is generated, could you also determine the overall tone of the speech?"
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 historical fact about a specific date. I will search for a function related to retrieving historical facts or information about dates. Once I have the historical fact, I can then look for a function that can generate a speech based on text input. Finally, I will need to find a function that can analyze the tone of the text.
ACTION TYPE:
search
ACTION CONTENT:
text: historical fact date
text: generate speech
text: tone analysis

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

Prototypes for the next 7 out of 32 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_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def get_math_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py
def get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py

QUERY:
text: generate speech

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

Prototypes for the next 7 out of 18 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 tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py

QUERY:
text: tone analysis

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[810, 838]
type=FUNCTION
content={
def select_object(objects, object_name, ckpt=None):
    """
    Selects an object from a list of objects based on the input object name.
    
    Args:
        objects (list): A list of dictionaries, where each dictionary represents an object with a "label" key.
        object_name (str): The name of the object to be selected.
        ckpt (optional): A checkpoint or state to resume from, not used in the current implementation.
    
    Returns:
        dict: A dictionary with a single key "object" that maps to the selected object dictionary.
    
    Examples:
        >>> objects_list = [{'label': 'apple'}, {'label': 'banana'}]
        >>> select_object(objects_list, 'apple')
        {'object': {'label': 'apple'}}
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[581, 602]
type=FUNCTION
content={
def image_crop_top(image, ckpt=None):
    """
    Crops the top half of an image and returns it in a dictionary.
    
    Args:
        image: The image to be cropped.
        ckpt: Optional checkpoint data (unused in the current implementation).
    
    Returns:
        A dictionary containing the cropped image with the key 'image'.
    
    Examples:
        >>> from PIL import Image
        >>> original_image = Image.open('path/to/image.jpg')
        >>> cropped_image_info = image_crop_top(original_image)
        >>> cropped_image = cropped_image_info['image']
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[721, 729]
type=FUNCTION
content={
def count(objects, ckpt=None):
    """
    It takes a list of objects and returns the count of the objects.
    """
    objs = objects
    result_formatted = {"number": len(objs)}

    return result_formatted
}
---
(22 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 image_crop_left(image, ckpt=None): ... # mnm/tool_api.py
def image_crop(image, object, 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 color_pop(image, object, ckpt=None): ... # mnm/tool_api.py
def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py
def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `get_date_fact` from `tool_api.py` can be used to retrieve a historical fact about a specific date. For generating a speech, the function `text_generation` seems appropriate, and it is mentioned in the unexpanded results. However, I did not find a direct function for tone analysis in the search results. I will need to search again specifically for text analysis functions that could include tone analysis. For now, I will proceed with retrieving the historical fact and generating the speech.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_date_fact, text_generation

# Retrieve a historical fact about August 24th
date_fact = get_date_fact("08/24")

# Generate a speech inspired by the historical fact
speech = text_generation(text=date_fact['text'])

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': " in the year 455 AD. They had besieged the city for two weeks before finally breaching the walls and entering the city. The Vandals looted a great deal of wealth and valuables, including gold and silver treasures from the city's churches and palaces.\n\nMany Romans were killed or taken captive, and the city was left in ruins. The sack of Rome by the Vandals was a significant event in the decline of the Roman Empire, as it demonstrated the weakness of the once-mighty city and its inability to defend itself against outside invaders.\n\nKing Genseric and his Vandals would go on to establish a kingdom in North Africa, where they would continue to raid and plunder Roman territories for many years to come. The sack of Rome by the Vandals remains a dark chapter in the history of the once-great Roman Empire."}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"get_date_fact": <function get_date_fact at 0x7f40cf2f7d00>, "text_generation": <function text_generation at 0x7f40cf2f6f80>, "date_fact": {'text': 'the Vandals, led by king Genseric, begin to plunder Rome', 'year': 455, 'number': 237, 'found': True, 'type': 'date'}, "speech": {'text': " in the year 455 AD. They had besieged the city for two weeks before finally breaching the walls and entering the city. The Vandals looted a great deal of wealth and valuables, including gold and silver treasures from the city's churches and palaces.\n\nMany Romans were killed or taken captive, and the city was left in ruins. The sack of Rome by the Vandals was a significant event in the decline of the Roman Empire, as it demonstrated the weakness of the once-mighty city and its inabil...}

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

------Action------
THOUGHT:
The historical fact about August 24th has been retrieved and a speech has been generated based on that fact. Now, I need to search for a function that can analyze the tone of the text to determine the overall tone of the speech.
ACTION TYPE:
search
ACTION CONTENT:
text: text analysis tone
text: sentiment analysis
text: emotion detection

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: text analysis tone

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[731, 765]
type=FUNCTION
content={
def tag(image, objects, ckpt=None):
    """
    Tags objects in an image with bounding boxes and class labels.
    
    Args:
        image: The image to be processed and tagged.
        objects: A list of dictionaries, each containing 'bbox' (bounding box) and 'label' (class) for an object.
        ckpt (optional): A checkpoint or model state to use for tagging, not used in the current implementation.
    
    Returns:
        A dictionary with a key 'image' containing the tagged image.
    
    Examples:
        # Assuming 'image' is an image object and 'objects' is a list of object dictionaries with 'bbox' and 'label'
        result = tag(image, objects)
        tagged_image = result['image']
    """
    ...
}
---
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=[178, 193]
type=FUNCTION
content={
def question_answering(
    question: str, text: str, ckpt=MODEL_SELECTION["question_answering"]
):  # alternative: "deepset/roberta-base-squad2"
    """
    It takes a text and a question, and outputs an answer to that question based on the text.
    """
    question = text_processing(question)
    text = text_processing(text)
    pipe = pipeline("question-answering", model=ckpt)
    result_raw = pipe(
        question=question, context=text
    )  # {'score': 0.01082150824368, 'start': 0, 'end': 10, 'answer': 'My name is'}
    result_formatted = {"text": result_raw["answer"]}

    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 23 classes/functions found in unexpanded results (search again with the same query to see details):
def wikipedia_simple_search(text: str, ckpt=None): ... # 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 text_processing(file_path): ... # 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 search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py

QUERY:
text: sentiment analysis

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[165, 176]
type=FUNCTION
content={
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']):
    """
    Classifies a given text into a category based on the model's vocabulary.
    
    Args:
        text (str): The text to be classified.
        ckpt: The model checkpoint to use for classification. Defaults to MODEL_SELECTION["text_classification"].
    
    Returns:
        dict: A dictionary with the key 'text' and the value as the classification label.
    
    Examples:
        >>> text_classification("I love sunny days.")
        {'text': 'POSITIVE'}
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[491, 534]
type=FUNCTION
content={
def image_crop(image, object, ckpt=None):
    """
    Crops an image to the region within a specified bounding box.
    
    Args:
        image: The image to be cropped.
        object: A dictionary containing the bounding box coordinates with the key 'bbox'.
        ckpt: Optional; a checkpoint or state to use for additional processing (not used in the current implementation).
    
    Returns:
        A dictionary with the key 'image' containing the cropped image.
    
    Raises:
        ValueError: If the bounding box is a string and cannot be converted to a list of coordinates.
    
    Examples:
        # Assuming `img` is an image object and `obj` is a dict with bbox coordinates
        cropped_image_result = image_crop(img, obj)
        cropped_image = cropped_image_result['image']
    """
    ...
}
---
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'])
    """
    ...
}
---
(20 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 18 classes/functions found in unexpanded results (search again with the same query to see details):
def image_processing(img): ... # mnm/tool_api.py
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py
def get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py
def get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def get_math_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py

QUERY:
text: emotion detection

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[377, 411]
type=FUNCTION
content={
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']):
    """
    Performs object detection on an image using a DETR (DEtection TRansformer) model.
    
    Args:
        image: The image on which to perform object detection.
        ckpt: A string representing the model checkpoint to use for object detection. Defaults to a value from MODEL_SELECTION["object_detection"].
    
    Returns:
        A dictionary with two keys:
        - 'image': The processed image.
        - 'objects': A list of dictionaries, each containing a 'bbox' key with the bounding box coordinates and a 'label' key with the object's label.
    
    Raises:
        ImportError: If the required modules 'torch' or 'transformers' are not installed.
    
    Examples:
        >>> result = object_detection(my_image)
        >>> print(result['objects'])
        [{'bbox': [x1, y1, x2, y2], 'label': 'object_label'}, ...]
    """
    ...
}
---
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=[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."
    """
    ...
}
---
(23 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 20 classes/functions found in unexpanded results (search again with the same query to see details):
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def text_processing(file_path): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py
def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py
def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `text_classification` from `tool_api.py` can be used for sentiment analysis, which is closely related to tone analysis. It classifies text into categories, which likely include tone-related categories such as positive, negative, or neutral. I will use this function to determine the overall tone of the generated speech.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import text_classification

# Determine the overall tone of the speech
speech_tone = text_classification(text=speech['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_classification": <function text_classification at 0x7f40cf2f70a0>, "speech_tone": {'text': 'NEGATIVE'}}

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

------Action------
THOUGHT:
The tone of the generated speech has been determined as 'NEGATIVE'. This completes the user's request. I will now provide a summary of the code used to solve the task.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_date_fact, text_generation, text_classification
output0 = get_date_fact("08/24")
output1 = text_generation(text=output0['text'])
output2 = text_classification(text=output1['text'])

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

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

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

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