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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "Hey, I recently watched Twilight and wondered what's the compatibility between Edward Cullen and Bella Swan in a love relationship. Also, entertain me with an intriguing trivia fact related to their love score. Then, could you analyze the sentiment of the fact you just shared?"
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 the function that calculates the compatibility between two names using the love calculator. Then, I will search for a function that can retrieve an interesting trivia fact. Finally, I will look for a function that can analyze the sentiment of a given text. I will start by searching for the love calculator function.
ACTION TYPE:
search
ACTION CONTENT:
text: love calculator

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: love calculator

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[911, 929]
type=FUNCTION
content={
def love_calculator(first_name: str, second_name: str, ckpt=None):
    """
    Enter your name and the name of your partner/lover/crush to find Love compatibility & chances of successful love relationship.
    """
    url = "https://love-calculator.p.rapidapi.com/getPercentage"
    params = {"fname": first_name, "sname": second_name}
    headers = {
        "X-RapidAPI-Key": RAPID_API_KEY,
        "X-RapidAPI-Host": "love-calculator.p.rapidapi.com",
    }
    response = requests.get(url, headers=headers, params=params)
    result_formatted = response.json()
    result_formatted = {
        "number": result_formatted["percentage"],
        "message": result_formatted["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=[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
}
---
(19 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 get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def audio_processing(audio_file): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py
def image_crop_top(image, ckpt=None): ... # 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

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

------Action------
THOUGHT:
The `love_calculator` function has been found and it takes two string arguments for the names. Next, I need to search for a function that can retrieve an interesting trivia fact. I will issue another search query for this.
ACTION TYPE:
search
ACTION CONTENT:
text: trivia

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

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[895, 909]
type=FUNCTION
content={
def get_trivia_fact(number: str, ckpt=None):
    """
    It provides interesting trivia facts about number.
    """
    url = f"https://numbersapi.p.rapidapi.com/{number}/trivia"
    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=[973, 983]
type=FUNCTION
content={
def get_weather(lon: str, lat: str, ckpt=None):
    """
    Provides weather forecast data based on specific geographical coordinates.
    """
    url = "http://www.7timer.info/bin/api.pl"
    params = {"lon": lon, "lat": lat, "product": "civil", "output": "json"}
    response = requests.get(url, params=params)
    result_formatted = {"objects": response.json()["dataseries"]}

    return result_formatted
}
---
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
}
---
(All code blocks matching the query were returned.)

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

------Action------
THOUGHT:
The `get_trivia_fact` function has been found and it takes a number as a string argument to provide a trivia fact. Since the user asked for a trivia fact related to the love score, I will first use the `love_calculator` function to get the compatibility score between Edward Cullen and Bella Swan. Then, I will use the score to get a trivia fact using the `get_trivia_fact` function. Lastly, I will need to find a function to analyze the sentiment of the trivia fact.
ACTION TYPE:
search
ACTION CONTENT:
text: sentiment analysis

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: sentiment analysis

CODE BLOCKS:
---
file_path=mnm/tool_api.py
lines=[165, 176]
type=FUNCTION
content={
def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']):
    """
    Classifies a given text into a category based on the model's vocabulary.
    
    Args:
        text (str): The text to be classified.
        ckpt: The model checkpoint to use for classification. Defaults to MODEL_SELECTION["text_classification"].
    
    Returns:
        dict: A dictionary with the key 'text' and the value as the classification label.
    
    Examples:
        >>> text_classification("I love sunny days.")
        {'text': 'POSITIVE'}
    """
    ...
}
---
file_path=mnm/tool_api.py
lines=[491, 534]
type=FUNCTION
content={
def image_crop(image, object, ckpt=None):
    """
    Crops an image to the region within a specified bounding box.
    
    Args:
        image: The image to be cropped.
        object: A dictionary containing the bounding box coordinates with the key 'bbox'.
        ckpt: Optional; a checkpoint or state to use for additional processing (not used in the current implementation).
    
    Returns:
        A dictionary with the key 'image' containing the cropped image.
    
    Raises:
        ValueError: If the bounding box is a string and cannot be converted to a list of coordinates.
    
    Examples:
        # Assuming `img` is an image object and `obj` is a dict with bbox coordinates
        cropped_image_result = image_crop(img, obj)
        cropped_image = cropped_image_result['image']
    """
    ...
}
---
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
}
---
(24 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 22 classes/functions found in unexpanded results (search again with the same query to see details):
def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py
def image_processing(img): ... # mnm/tool_api.py
def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py
def get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py
def get_math_fact(number: str, ckpt=None): ... # mnm/tool_api.py
def get_location(city: str, ckpt=None): ... # mnm/tool_api.py
def select_object(objects, object_name, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The `text_classification` function can be used for sentiment analysis as it classifies text into categories, which likely include sentiment labels such as 'POSITIVE' or 'NEGATIVE'. Now I have all the necessary functions to fulfill the user's request. I will write the code to first calculate the love compatibility between Edward Cullen and Bella Swan, then get a trivia fact based on their love score, and finally analyze the sentiment of that trivia fact.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import love_calculator, get_trivia_fact, text_classification

# Calculate the love compatibility between Edward Cullen and Bella Swan
love_score = love_calculator('Edward Cullen', 'Bella Swan')

# Get a trivia fact based on their love score
trivia_fact = get_trivia_fact(love_score['number'])

# Analyze the sentiment of the trivia fact
sentiment_result = text_classification(trivia_fact['text'])

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"love_calculator": <function love_calculator at 0x7f5e7073c040>, "get_trivia_fact": <function get_trivia_fact at 0x7f5e70723f40>, "text_classification": <function text_classification at 0x7f5e70723130>, "love_score": {'number': '99', 'message': 'Congratulations! Good choice'}, "trivia_fact": {'text': "the number of names of Allah, the names or attributes of God in the Qur'an", 'number': 99, 'found': True, 'type': 'trivia'}, "sentiment_result": {'text': 'POSITIVE'}}

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

------Action------
THOUGHT:
The code executed successfully and the relevant variables contain the results. The love compatibility score between Edward Cullen and Bella Swan is 99 with a message "Congratulations! Good choice". The trivia fact related to the number 99 is "the number of names of Allah, the names or attributes of God in the Qur'an", and the sentiment of this fact is 'POSITIVE'. I will now summarize the solution for the user.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import love_calculator, get_trivia_fact, text_classification
output0 = love_calculator('Edward Cullen', 'Bella Swan')
output1 = get_trivia_fact(output0['number'])
output2 = text_classification(output1['text'])

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

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

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

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