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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I'm working on a 2020 year-in-review project. Can you give me a fun fact about the year 2020, create a striking representation of that fact as an image, and then transcribe any text present 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 fun fact about the year 2020. I will search for a function in the codebase that can perform this task.
ACTION TYPE:
search
ACTION CONTENT:
text: fun fact AND (text: 2020)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: fun fact AND (text: 2020)

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[1013, 1038]
type=CONDITIONAL
content={
if __name__ == '__main__':
    res = text_summarization({'text': 'John Taylor, who had supported her through college, was interested in cotton.'})
    print(res, '\n')
    pass
}
---
(All code blocks matching the query were returned.)

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

------Action------
THOUGHT:
The search did not return a function that directly retrieves a fun fact about the year 2020. I will try a broader search for a function that can retrieve fun facts or interesting information, which I can then specify to be about the year 2020.
ACTION TYPE:
search
ACTION CONTENT:
text: fun fact OR text: interesting information

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: fun fact OR text: interesting information

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=[879, 893]
type=FUNCTION
content={
def get_math_fact(number: str, ckpt=None):
    """
    It provides interesting math facts about numbers.
    """
    url = f"https://numbersapi.p.rapidapi.com/{number}/math"
    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=[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
}
---
(41 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 36 classes/functions found in unexpanded results (search again with the same query to see details):
def get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py
def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py
def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The search returned a function called `get_year_fact` which provides interesting facts about years. I will use this function to retrieve a fun fact about the year 2020. After obtaining the fact, I will proceed to create a striking representation of that fact as an image and then transcribe any text present in the image.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_year_fact

# Retrieve a fun fact about the year 2020
year_fact = get_year_fact('2020')
print(year_fact)

-----Response-----
RESPONSE (Execution Result):
STDOUT:
{'text': 'Earliest year that power aboard the space probes Voyager 1 and Voyager 2 is expected to terminate (though either probe may function past this date)', 'number': 2020, 'found': True, 'type': 'year'}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"get_year_fact": <function get_year_fact at 0x7f9e8f6e3be0>, "year_fact": {'text': 'Earliest year that power aboard the space probes Voyager 1 and Voyager 2 is expected to terminate (though either probe may function past this date)', 'number': 2020, 'found': True, 'type': 'year'}}

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

------Action------
THOUGHT:
Now that I have the fun fact about the year 2020, I need to find a function that can create an image representation of this fact. I will search for a function that can generate images based on a text description.
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
}
---
(16 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 search_movie(movie_title: str, movie_year: str, ckpt=None): ... # 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 6
********************************************************************************

------Action------
THOUGHT:
The function `image_generation` can generate an image based on a text description. I will use this function to create an image representation of the fun fact about the year 2020. After generating the image, I will need to transcribe any text present in the image using an optical character recognition (OCR) function, which I will search for next.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation

# Generate an image based on the fun fact about the year 2020
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 0x7F9D20B22C20>}

RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_generation": <function image_generation at 0x7f9e8f6e30a0>, "fact_description": Earliest year that power aboard the space probes Voyager 1 and Voyager 2 is expected to terminate (though either probe may function past this date), "generated_image": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F9D20B22C20>}}

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

------Action------
THOUGHT:
Now that the image has been generated, I need to find an optical character recognition (OCR) function to transcribe any text present in the image.
ACTION TYPE:
search
ACTION CONTENT:
text: optical character recognition

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
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
}
---
(32 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 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 wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `optical_character_recognition` can be used to transcribe text from the generated image. I will use this function to extract any text present in the image.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import optical_character_recognition

# Transcribe any text present in the generated image
transcribed_text = optical_character_recognition(generated_image['image'])
print(transcribed_text)

-----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 0x7f9e8f6e3490>, "transcribed_text": {'text': ''}}

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

------Action------
THOUGHT:
The optical character recognition function did not find any text in the generated image, which means the image likely does not contain any text or the text is not recognizable by the OCR. Since the task is completed, I will now provide a code summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_year_fact, image_generation, optical_character_recognition
output0 = get_year_fact('2020')
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