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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I want to learn something unique about the year 2015. Could you find a compelling fact from that year, create a suitable image that captures the essence of that fact, and identify any texts appearing in the image?"
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 fact about the year 2015. Once I have the fact, I will search for a function that can generate an image based on the text description of the fact. Finally, I will look for a function that can perform optical character recognition (OCR) on the generated image to identify any texts appearing in it. I will start by searching for the information retrieval function that can get a fact about the year 2015.
ACTION TYPE:
search
ACTION CONTENT:
(text: 2015) AND (type: FUNCTION) AND (text: fact)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(text: 2015) AND (type: FUNCTION) AND (text: fact)

CODE BLOCKS:
No code blocks found.

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

------Action------
THOUGHT:
The search for a function specifically mentioning the year 2015 and the word "fact" did not yield any results. I will broaden the search to look for a function that can retrieve facts about dates or years in general, without specifying the year 2015 in the search query.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: fact OR text: year OR text: date)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (text: fact OR 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
}
---
(23 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 image_crop(image, object, 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 wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py
def get_full_path_data(filename): ... # 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

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

------Action------
THOUGHT:
The function `get_year_fact` from the `tool_api.py` file seems to be the right choice for retrieving an interesting fact about the year 2015. I will use this function to get the fact. After obtaining the fact, I will proceed to search for a function that can generate an image based on the text description of the fact.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_year_fact

# Retrieve an interesting fact about the year 2015
year_fact = get_year_fact("2015")
print(year_fact)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'this is the earliest year the European Space Agency might launch the Darwin Mission to search for exoplanets that could possibly harbour life', 'number': 2015, 'found': True, 'type': 'year'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"get_year_fact": <function get_year_fact at 0x7f751332fc70>, "year_fact": {'text': 'this is the earliest year the European Space Agency might launch the Darwin Mission to search for exoplanets that could possibly harbour life', 'number': 2015, 'found': True, 'type': 'year'}}

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

------Action------
THOUGHT:
Now that we have an interesting fact about the year 2015, the next step is to search for a function that can generate an image based on the text description of this fact. I will look for an image generation function in the codebase.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: generate) AND (text: image OR text: description)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (text: generate) AND (text: image OR 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
}
---
(13 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 13 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 tag(image, objects, 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 6
********************************************************************************

------Action------
THOUGHT:
The function `image_generation` from the `tool_api.py` file is suitable for generating an image based on the text description of the fact. I will use this function to create an image that captures the essence of the fact about the year 2015. After generating the image, I will search for the OCR function to identify any texts appearing in the image.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation

# Generate an image based on the text description of the fact
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 0x7F74D84F0EB0>}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_generation": <function image_generation at 0x7f751332f130>, "fact_description": this is the earliest year the European Space Agency might launch the Darwin Mission to search for exoplanets that could possibly harbour life, "generated_image": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F74D84F0EB0>}}

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

------Action------
THOUGHT:
Now that we have generated an image, the final step is to identify any texts appearing in the image. I will search for the optical character recognition (OCR) function in the codebase to perform this task.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: optical_character_recognition)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (text: optical_character_recognition)

CODE BLOCKS:
---
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=[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=[51, 77]
type=FUNCTION
content={
def text_processing(file_path):
    """
    preprocess the text file

    Args:
    - the path of .doc .docx .txt file

    Returns:
    - text with string format

    Notes:
    - This function is designed for text input.

    """

    # Check the file extension
    if file_path.endswith(".txt"):
        with open(file_path, "r") as file:
            content = file.read()
    elif file_path.endswith(".doc") or file_path.endswith(".docx"):
        # Use textract to extract text from doc and docx files
        content = textract.process(file_path).decode("utf-8")
    else:
        # if the file is not .txt .doc .docx, then it is a string, directly return the stirng
        return file_path
    return content
}
---
(21 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_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py
def love_calculator(first_name: str, second_name: str, ckpt=None): ... # 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 text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `optical_character_recognition` from the `tool_api.py` file is the correct choice for performing OCR on the generated image. I will use this function to identify any texts appearing in the image.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import optical_character_recognition

# Perform OCR on the generated image to identify any texts
recognized_texts = optical_character_recognition(generated_image['image'])
print(recognized_texts)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': ''}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"optical_character_recognition": <function optical_character_recognition at 0x7f751332f520>, "recognized_texts": {'text': ''}}

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

------Action------
THOUGHT:
The OCR function did not detect any text in the generated image, which means the image likely does not contain any readable text, or the text is not in a format that the OCR engine could recognize. The tasks of retrieving a fact about the year 2015, generating an image based on that fact, and attempting to identify any text in the image have been completed.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_year_fact, image_generation, optical_character_recognition
output0 = get_year_fact("2015")
output1 = image_generation(output0['text'])
output2 = optical_character_recognition(output1['image'])

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

********************************************************************************
Step 10
********************************************************************************

------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