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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I'd like to visualize an event from the year 2014. Could you find an interesting fact from this year and then provide an image representation of it? Afterwards, can you identify what the primary subject of that image is?"
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 an interesting fact about a specific year. Once I have the fact, I can then search for a function that can generate an image representation of that fact. Finally, I will need to find a function that can identify the primary subject of the generated image. I will start by searching for the function that retrieves facts about a specific year.
ACTION TYPE:
search
ACTION CONTENT:
text: interesting fact AND (text: year OR text: date)

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

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=[943, 971]
type=FUNCTION
content={
def search_movie(movie_title: str, movie_year: str, ckpt=None):
    """
    Retrieve basic movie information, including title, year, genre, and director.
    """
    # paren_start = input['movie_title'].find('(')
    url = "http://www.omdbapi.com/"
    params = {
        "t": movie_title,
        "y": movie_year,
        "plot": "short",
        "r": "json",
        "apikey": OMDB_API_KEY,
    }
    response = requests.get(url, params=params)
    result_formatted = response.json()

    selected_keys = ["Title", "Year", "Genre", "Director", "Plot"]
    desc = ""
    if len(result_formatted) > 0:
        for k, v in result_formatted.items():
            # print(k, v)
            if k in selected_keys and len(v) > 0:
                desc += f"{k}: {v}\n"
    else:
        desc = "Movie not found!"
    result_formatted = {"text": desc}

    return result_formatted
}
---
(6 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 5 out of 5 classes/functions found in unexpanded results (search again with the same query to see details):
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `get_year_fact` from `tool_api.py` seems suitable for retrieving an interesting fact about the year 2014. I will use this function to get the fact. After obtaining the fact, I will search for a function that can generate an image representation of the text. Once the image is generated, I will search for a function that can identify the primary subject of the image.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_year_fact

# Retrieve an interesting fact about the year 2014
year_fact = get_year_fact("2014")
print(year_fact)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'Referendum on Scottish Independence to be held by the Scottish National Party', 'number': 2014, 'found': True, 'type': 'year'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"get_year_fact": <function get_year_fact at 0x7f9d7660ff40>, "year_fact": {'text': 'Referendum on Scottish Independence to be held by the Scottish National Party', 'number': 2014, 'found': True, 'type': 'year'}}

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

------Action------
THOUGHT:
Now that we have an interesting fact from the year 2014, the next step is to find a function that can generate an image representation of this fact. I will search for a function that can generate images from text descriptions.
ACTION TYPE:
search
ACTION CONTENT:
text: generate image AND text: description

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: generate image AND text: description

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

Prototypes for the next 7 out of 15 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 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 optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_generation` from `tool_api.py` is suitable for generating an image from the text description of the interesting fact. I will use this function to generate the image. After the image is generated, I will search for a function that can identify the primary subject of the image.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation

# Generate an image based on the interesting fact from the year 2014
fact_description = year_fact['text']
generated_image = image_generation(fact_description)
print(generated_image)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F99F85904C0>}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_generation": <function image_generation at 0x7f9d7660f400>, "fact_description": Referendum on Scottish Independence to be held by the Scottish National Party, "generated_image": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F99F85904C0>}}

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

------Action------
THOUGHT:
The image has been generated. Now, I need to find a function that can identify the primary subject of the image. I will search for a function that can perform image classification or object detection to identify the subject.
ACTION TYPE:
search
ACTION CONTENT:
text: image classification OR text: object detection

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: image classification OR text: object detection

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[325, 346]
type=FUNCTION
content={
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']):
    """
    Classifies an image into one of the predefined categories using a Vision Transformer (ViT) model.
    
    Args:
        image: The image to be classified, which will be processed by `image_processing` function.
        ckpt (str, optional): The checkpoint or model identifier to use for loading the pre-trained model. Defaults to `MODEL_SELECTION["image_classification"]`.
    
    Returns:
        dict: A dictionary with a key "text" that maps to the predicted category of the image.
    
    Examples:
        >>> result = image_classification(my_image)
        >>> print(result)
        {'text': 'cat'}
    """
    ...
}
---
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=[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'}, ...]
    """
    ...
}
---
(33 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 29 classes/functions found in unexpanded results (search again with the same query to see details):
def tag(image, objects, ckpt=None): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def select_object(objects, object_name, ckpt=None): ... # mnm/tool_api.py
def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py
def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `object_detection` from `tool_api.py` is suitable for identifying the primary subject of the image by detecting objects within it. I will use this function to detect objects in the generated image and identify the primary subject.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import object_detection

# Identify the primary subject of the generated image
detected_objects = object_detection(generated_image['image'])
print(detected_objects)

-----Response-----
RESPONSE (Execution Result):
STDOUT (STDOUT was too long. Showing only the start and end separated by ellipsis.):
{'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F99F8259F30>, 'objects': [{'bbox': [234.28, 243.58, 271.12, 305.64], 'label': 'person'}, {'bbox': [971.97, 217.15, 1023.56, 295.0], 'label': 'person'}, {'bbox': [612.24, 258.64, 659.56, 337.5], 'label': 'person'}, {'bbox': [112.84, 261.43, 166.96, 326.76], 'label': 'person'}, {'bbox': [367.22, 244.16, 401.39, 296.38], 'label': 'person'}, {'bbox': [729.76, 286.71, 804.14, 395.43], 'label': 'person'}, {'bbox': [11.08, 268.31, 80.12, 336.43], 'label': 'person'}, {'bbox': [179.11, 234.72, 230.33, 312.79], 'label': 'person'}, {'bbox': [671.78, 263.05, 730.48, 420.51], 'label': 'person'}, {'bbox': [0.01, 432.24, 44.8, 884.35], 'label': 'person'}, {'bbox': [909.42, 250.31, 959.75, 359.3], 'label': 'person'}, {'bbox': [-0.2, 388.87, 110.05, 794.9], 'label': 'person'}, {'bbox': [800.61, 263.37, 857.23, 407.52], 'label': 'person'}, {'bbox': [137.37, 262.92, 171.73, 311.4], 'label': 'person'}, {'bbox': [744.44, 271.82, 799.4, 405.38],

...

ox': [7.96, 270.3, 84.1, 453.44], 'label': 'person'}, {'bbox': [378.29, 242.06, 443.41, 309.49], 'label': 'person'}, {'bbox': [677.28, 311.19, 727.42, 425.84], 'label': 'person'}, {'bbox': [652.52, 244.74, 698.13, 332.92], 'label': 'person'}, {'bbox': [49.67, 257.44, 723.88, 820.94], 'label': 'person'}, {'bbox': [679.89, 242.55, 714.05, 307.37], 'label': 'person'}, {'bbox': [821.71, 179.28, 934.82, 321.93], 'label': 'person'}, {'bbox': [819.27, 181.13, 942.9, 401.49], 'label': 'person'}, {'bbox': [406.55, 709.89, 567.47, 858.47], 'label': 'person'}, {'bbox': [954.48, 250.49, 1021.44, 393.63], 'label': 'person'}, {'bbox': [-0.08, 263.64, 107.93, 801.26], 'label': 'person'}, {'bbox': [572.32, 251.35, 615.99, 338.5], 'label': 'person'}, {'bbox': [944.65, 189.51, 973.07, 259.05], 'label': 'person'}, {'bbox': [689.29, 297.73, 724.46, 331.99], 'label': 'cell phone'}, {'bbox': [440.11, 283.64, 493.6, 330.31], 'label': 'person'}, {'bbox': [268.25, 246.05, 317.99, 302.04], 'label': 'person'}]}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"object_detection": <function object_detection at 0x7f9d7660f6d0>, "detected_objects": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F99F8259F30>, 'objects': [{'bbox': [234.28, 243.58, 271.12, 305.64], 'label': 'person'}, {'bbox': [971.97, 217.15, 1023.56, 295.0], 'label': 'person'}, {'bbox': [612.24, 258.64, 659.56, 337.5], 'label': 'person'}, {'bbox': [112.84, 261.43, 166.96, 326.76], 'label': 'person'}, {'bbox': [367.22, 244.16, 401.39, 296.38], 'label': 'person'}, {'bbox': [729.76, 286.71, 804.14, 395.43], 'label': 'person'}, {'bbox': [11.08, 268.31, 80.12, 33...}

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

------Action------
THOUGHT:
The primary subjects detected in the image are labeled as 'person'. The object detection function has returned multiple bounding boxes with the label 'person', indicating that there are several people in the image. This is consistent with the fact about the "Referendum on Scottish Independence" which likely involved gatherings of people. I will now summarize the solution using the code_summary action.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_year_fact, image_generation, object_detection
year_fact = get_year_fact("2014")
generated_image = image_generation(year_fact['text'])
detected_objects = object_detection(generated_image['image'])

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

********************************************************************************
Step 9
********************************************************************************

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

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