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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: I'm trying to get a grasp on what exactly a recession entails and how it impacts the economy. After you provide me with a basic understanding of 'What is a recession', can you expand on the major effects it has had on global economies in recent history? Following that, I'd like to get a visual representation of a recession's impact on the stock market.
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 factual information or interesting facts about a given topic, which in this case is 'What is a recession'. After obtaining the basic understanding, I will look for a function that can expand on the major effects of recessions on global economies in recent history. Lastly, I will search for a function that can provide a visual representation of a recession's impact on the stock market. I will start by searching for a function that can retrieve information about 'What is a recession'.
ACTION TYPE:
search
ACTION CONTENT:
text: recession AND (type: FUNCTION) AND (text: retrieve OR text: information OR text: facts)

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

CODE BLOCKS:
---
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
}
---
file_path=mnm/tool_api.py
lines=[985, 1011]
type=FUNCTION
content={
def wikipedia_simple_search(text: str, ckpt=None):
    """
    Performs a basic search on Wikipedia to retrieve a summary of the most relevant page.
    
    Args:
        text (str): The search query text.
        ckpt: Unused parameter, can be anything (default is None).
    
    Returns:
        dict: A dictionary containing the formatted text summary of the most relevant Wikipedia page.
    
    Raises:
        This function may raise exceptions related to network issues or JSON decoding if the Wikipedia API changes or is unreachable.
    
    Examples:
        result = wikipedia_simple_search("Python programming language")
        # result might be something like:
        # {'text': 'Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with its use of significant indentation.'}
    """
    ...
}
---
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'}, ...]
    """
    ...
}
---
(31 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 31 classes/functions found in unexpanded results (search again with the same query to see details):
def image_generation(text: str, ckpt=MODEL_SELECTION['image_generation']): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
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 visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `wikipedia_simple_search` from the `mnm` codebase seems to be the most appropriate for retrieving a summary of the most relevant Wikipedia page about a recession. I will use this function to provide the user with a basic understanding of 'What is a recession'. After that, I will search for a function that can expand on the major effects of recessions on global economies in recent history. Lastly, I will look for a function that can provide a visual representation of a recession's impact on the stock market.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import wikipedia_simple_search

# Retrieve a basic understanding of 'What is a recession'
recession_info = wikipedia_simple_search("Recession")
print(recession_info['text'])

-----Response-----
RESPONSE (Execution Result):
STDOUT:
In economics, a recession is a business cycle contraction that occurs when there is a general decline in economic activity. Recessions generally occur

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"wikipedia_simple_search": <function wikipedia_simple_search at 0x7f9d766343a0>, "recession_info": {'text': 'In economics, a recession is a business cycle contraction that occurs when there is a general decline in economic activity. Recessions generally occur'}}

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

------Action------
THOUGHT:
The user now has a basic understanding of what a recession is. Next, I need to find a function that can provide information on the major effects of recessions on global economies in recent history. I will search for a function that can retrieve such detailed information.
ACTION TYPE:
search
ACTION CONTENT:
text: (effects OR impact) AND recession AND (type: FUNCTION) AND (text: retrieve OR text: information OR text: facts)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: (effects OR impact) AND recession AND (type: FUNCTION) AND (text: retrieve OR text: information OR text: facts)

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

Prototypes for the next 7 out of 25 classes/functions found in unexpanded results (search again with the same query to see details):
def text_processing(file_path): ... # mnm/tool_api.py
def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py
def get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The search did not return a function specifically designed to retrieve information on the effects of recessions on global economies. However, the `wikipedia_simple_search` function used previously can be repurposed to search for a summary of the effects of recessions on global economies in recent history. I will use this function again to retrieve the requested information. After obtaining this information, I will proceed to find a function that can provide a visual representation of a recession's impact on the stock market.
ACTION TYPE:
code
ACTION CONTENT:
# Retrieve information on the major effects of recessions on global economies in recent history
effects_of_recession_info = wikipedia_simple_search("Effects of recessions on global economies")
print(effects_of_recession_info['text'])

-----Response-----
RESPONSE (Execution Result):
STDOUT:
to these declines. U.S. recessions have increasingly affected economies on a worldwide scale, especially as countries' economies become more intertwined

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"effects_of_recession_info": {'text': "to these declines. U.S. recessions have increasingly affected economies on a worldwide scale, especially as countries' economies become more intertwined"}}

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

------Action------
THOUGHT:
Now that the user has information on the effects of recessions on global economies, the next step is to find a function that can provide a visual representation of a recession's impact on the stock market. I will search for a function that can generate or retrieve such visual data.
ACTION TYPE:
search
ACTION CONTENT:
text: (stock market OR recession) AND (type: FUNCTION) AND (text: visual OR text: graph OR text: chart OR text: image)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: (stock market OR recession) AND (type: FUNCTION) AND (text: visual OR text: graph OR text: chart OR text: image)

CODE BLOCKS:
---
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=[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=[269, 323]
type=FUNCTION
content={
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']):
    """
    Edits an image based on a given text prompt using a specified model checkpoint.
    
    Args:
        image: The input image to be edited.
        prompt (str): The text prompt describing the desired edits.
        ckpt: The model checkpoint to use for image editing. Defaults to MODEL_SELECTION["image_editing"].
    
    Returns:
        A dictionary with the key 'image' containing the edited image.
    
    Raises:
        ImportError: If the necessary libraries for the chosen model are not installed.
    
    Examples:
        # Using the default checkpoint
        edited_image = image_editing(input_image, "A cat with a party hat.")
        
        # Using a specific checkpoint
        edited_image = image_editing(input_image, "A dog on a skateboard.", ckpt="timbrooks/instruct-pix2pix")
    """
    ...
}
---
(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 text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def image_crop_left(image, ckpt=None): ... # mnm/tool_api.py
def image_crop_top(image, ckpt=None): ... # mnm/tool_api.py
def image_crop_bottom(image, ckpt=None): ... # mnm/tool_api.py
def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
None of the functions returned by the search seem to directly provide a visual representation of a recession's impact on the stock market. However, the `image_editing` function can be used to edit an image based on a given text prompt. If we had an image of a stock market chart, we could potentially use this function to highlight the periods of recession. Since we do not have such an image, we cannot proceed with this approach.

Given the limitations of the available functions, it seems we cannot fulfill the user's request for a visual representation of a recession's impact on the stock market using the `mnm` codebase. I will inform the user of this limitation.
ACTION TYPE:
done
ACTION CONTENT:
False

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

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

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

-----Response-----
RESPONSE (User Message):
Please immediately take a code_summary action to generate a summary of your code produced so far. If you have not produced any code yet, make an educated guess based on what you have learned so far.

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

------Action------
THOUGHT:
The user has requested a code summary of the actions taken so far. I will summarize the code that was used to retrieve information about what a recession is and the effects of recessions on global economies using the `wikipedia_simple_search` function from the `mnm` codebase.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import wikipedia_simple_search
output0 = wikipedia_simple_search("Recession")
output1 = wikipedia_simple_search("Effects of recessions on global economies")

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