********************************************************************************
Step 1
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------Action------
THOUGHT:
None
ACTION TYPE:
None
ACTION CONTENT:
None

-----Response-----
RESPONSE (User Message):
USER QUERY: "I have an image COCO_train2014_000000256855.jpg. The image seems to have some text on it. Could you help me find out what the text says?" 
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'])
```

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Step 2
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------Action------
THOUGHT:
The user has an image with text on it and wants to find out what the text says. This sounds like a task for optical character recognition (OCR). I will search the codebase for a function that can perform OCR to transcribe the text from 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=[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=[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'])
    """
    ...
}
---
(39 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 35 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 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

********************************************************************************
Step 3
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------Action------
THOUGHT:
The search results returned a function called `optical_character_recognition` in the `tool_api.py` file which is designed to recognize and extract text from images. This is exactly what we need for the user's request. I will write code to call this function with the image provided by the user.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import optical_character_recognition

# The path to the image file provided by the user
image_path = '/root/input_data/COCO_train2014_000000256855.jpg'

# Call the optical_character_recognition function to extract text from the image
text_recognition_result = optical_character_recognition(image_path)

-----Response-----
RESPONSE (Execution Result):
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RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"optical_character_recognition": <function optical_character_recognition at 0x7f9e8f6db1c0>, "image_path": /root/input_data/COCO_train2014_000000256855.jpg, "text_recognition_result": {'text': 'Qenn'}}

********************************************************************************
Step 4
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------Action------
THOUGHT:
The optical character recognition function has successfully extracted text from the image. The result is stored in the variable `text_recognition_result` with the key 'text' containing the recognized text "Qenn". I will now summarize the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import optical_character_recognition
text_recognition_result = optical_character_recognition(image='/root/input_data/COCO_train2014_000000256855.jpg')

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

********************************************************************************
Step 5
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------Action------
THOUGHT:
The task has been completed successfully, and the user has acknowledged receipt of the summary.
ACTION TYPE:
done
ACTION CONTENT:
True

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