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

-----Response-----
RESPONSE (User Message):
USER QUERY: "For the photo titled 2349496.jpg, generate a descriptive text summary."
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 search for a function in the `mnm` codebase that can generate a descriptive text summary for an image. This is likely to be an image captioning function or similar. I will start by searching for functions related to image captioning or image description.
ACTION TYPE:
search
ACTION CONTENT:
(type: FUNCTION) AND (text: image captioning OR text: image description)

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
(type: FUNCTION) AND (text: image captioning OR text: image description)

CODE BLOCKS:
---
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
}
---
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.'}
    """
    ...
}
---
(36 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 image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # 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 visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # 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 3
********************************************************************************

------Action------
THOUGHT:
The `image_captioning` function from the `mnm/tool_api.py` file seems to be exactly what we need to generate a descriptive text summary for an image. It takes an image as input and generates a text caption. I will write code to use this function with the image file provided by the user.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_captioning

# Path to the image file
image_path = '/root/input_data/2349496.jpg'

# Generate a descriptive text summary for the image
descriptive_text_summary = image_captioning(image_path)

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_captioning": <function image_captioning at 0x7f38ccb9f0a0>, "image_path": /root/input_data/2349496.jpg, "descriptive_text_summary": {'text': 'there is a large bridge that is over a river with a clock tower'}}

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

------Action------
THOUGHT:
The code executed successfully and we have obtained the descriptive text summary for the image. I will now provide a code summary of the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import image_captioning
descriptive_text_summary = image_captioning(image_path='/root/input_data/2349496.jpg')

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

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

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

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