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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: I am creating a presentation about hiking and I'm currently picturing a scenario where two men are standing in some sand walking up a hill. After creating a visual representation of this using AI, I would like to have this image described to me. Additionally, I'd like to understand - from the image - if it's clear how prepared the hikers are for their journey. Please answer the question: Do the men in the image appear to be equipped for their hike?.
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:
Before generating the image, I need to search for the function in the `mnm` codebase that can generate images based on a text description. Once the image is generated, I will then search for a function that can provide a caption or description for the image. Finally, I will look for a function that can analyze the image to determine if the subjects are equipped for a hike. I will start by searching for the image generation function.
ACTION TYPE:
search
ACTION CONTENT:
text: generate images based on a text description

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: generate images based on a 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
}
---
(41 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 visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py
def get_full_path_data(filename): ... # mnm/tool_api.py
def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # 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 optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py
def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `image_generation` from `mnm/tool_api.py` can be used to generate an image based on a text description. After generating the image, I will use the `image_captioning` function to describe the image. To answer the question about the hikers' preparedness, I will likely need to use the `visual_question_answering` function mentioned in the prototypes. I will now write the code to generate the image using the `image_generation` function.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_generation

# Text description for generating the image
description = "two men standing in some sand walking up a hill"
# Generate the image based on the text description
generated_image = image_generation(text=description)

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"image_generation": <function image_generation at 0x7f9d76606ef0>, "description": two men standing in some sand walking up a hill, "generated_image": {'image': <PIL.Image.Image image mode=RGB size=1024x1024 at 0x7F9E34D17970>}}

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

------Action------
THOUGHT:
The image has been successfully generated. Now I will write code to use the `image_captioning` function to describe the generated image. After that, I will use the `visual_question_answering` function to determine if the men in the image appear to be equipped for their hike. I will start by writing the code for image captioning.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import image_captioning

# Get a caption for the generated image
image_description = image_captioning(image=generated_image['image'])

-----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 0x7f9d76606f80>, "image_description": {'text': 'three people walking in the sand dunes in the fog'}}

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

------Action------
THOUGHT:
The image has been captioned, but the caption mentions "three people" instead of two, which could be an error in the AI's generation or captioning. Regardless, I will proceed to use the `visual_question_answering` function to answer the user's specific question about the hikers' preparedness. I need to search for the `visual_question_answering` function to get its exact usage.
ACTION TYPE:
search
ACTION CONTENT:
text: visual_question_answering

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: visual_question_answering

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=[178, 193]
type=FUNCTION
content={
def question_answering(
    question: str, text: str, ckpt=MODEL_SELECTION["question_answering"]
):  # alternative: "deepset/roberta-base-squad2"
    """
    It takes a text and a question, and outputs an answer to that question based on the text.
    """
    question = text_processing(question)
    text = text_processing(text)
    pipe = pipeline("question-answering", model=ckpt)
    result_raw = pipe(
        question=question, context=text
    )  # {'score': 0.01082150824368, 'start': 0, 'end': 10, 'answer': 'My name is'}
    result_formatted = {"text": result_raw["answer"]}

    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
}
---
(37 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 32 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 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_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py
def text_processing(file_path): ... # 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

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

------Action------
THOUGHT:
I have found the `visual_question_answering` function which can be used to answer questions about an image. I will now write the code to use this function to determine if the men in the generated image appear to be equipped for their hike.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import visual_question_answering

# The question to be asked about the image
question_about_preparedness = "Do the men in the image appear to be equipped for their hike?"

# Use the visual_question_answering function to get an answer to the question
preparedness_answer = visual_question_answering(image=generated_image['image'], question=question_about_preparedness)

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"visual_question_answering": <function visual_question_answering at 0x7f9d76607130>, "question_about_preparedness": Do the men in the image appear to be equipped for their hike?, "preparedness_answer": {'text': 'no'}}

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

------Action------
THOUGHT:
The visual question answering function has been executed and returned the answer 'no', indicating that the men in the image do not appear to be equipped for their hike. I will now summarize the steps taken to solve the user's query.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import image_generation, image_captioning, visual_question_answering
description = "two men standing in some sand walking up a hill"
generated_image = image_generation(text=description)
image_description = image_captioning(image=generated_image['image'])
question_about_preparedness = "Do the men in the image appear to be equipped for their hike?"
preparedness_answer = visual_question_answering(image=generated_image['image'], question=question_about_preparedness)

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

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

------Action------
THOUGHT:
The user has acknowledged the receipt of the code summary. The task is complete.
ACTION TYPE:
done
ACTION CONTENT:
True

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