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

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

-----Response-----
RESPONSE (User Message):
USER QUERY: "I would like to find out an interesting fact about May 7th and then analyze the sentiment of the fact presented. Is the fact generally positive or negative?"
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 in the `mnm` codebase that can retrieve an interesting fact about a specific date. Once I have the fact, I will then need to search for a function that can analyze the sentiment of a given text. I will start by searching for a function related to retrieving facts about dates.
ACTION TYPE:
search
ACTION CONTENT:
text: interesting fact date

-----Response-----
RESPONSE (Retrieval Result):
QUERY:
text: interesting fact date

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
}
---
(42 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 image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py
def image_crop(image, object, ckpt=None): ... # 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 select_object(objects, object_name, ckpt=None): ... # mnm/tool_api.py
def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py

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

------Action------
THOUGHT:
The function `get_date_fact` from the `mnm` codebase is suitable for retrieving an interesting fact about a specific date. Next, I need to find a function that can analyze the sentiment of the retrieved fact. I will search for a sentiment analysis function in the codebase.
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=[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.'}
    """
    ...
}
---
(26 additional code blocks not shown. Search again with the same query to see additional results.)

Prototypes for the next 7 out of 24 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 get_weather(lon: str, lat: str, ckpt=None): ... # 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_trivia_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 4
********************************************************************************

------Action------
THOUGHT:
The function `text_classification` can be used to classify the sentiment of the text as positive or negative. Now, I will write the code to first retrieve an interesting fact about May 7th using `get_date_fact` and then analyze the sentiment of the fact using `text_classification`.
ACTION TYPE:
code
ACTION CONTENT:
from mnm.tool_api import get_date_fact, text_classification

# Retrieve an interesting fact about May 7th
fact_result = get_date_fact("05/07")

# Analyze the sentiment of the fact
if 'text' in fact_result:
    sentiment_result = text_classification(fact_result['text'])
else:
    sentiment_result = "No fact text available"

-----Response-----
RESPONSE (Execution Result):
CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY
RELEVANT VARIABLES (only shown if string rep. has changed after code exec):
{"get_date_fact": <function get_date_fact at 0x7f38ccbbfe20>, "text_classification": <function text_classification at 0x7f38ccbbf1c0>, "fact_result": {'text': 'Israeli archaeologists discover the tomb of Herod the Great south of Jerusalem', 'year': 2007, 'number': 128, 'found': True, 'type': 'date'}, "sentiment_result": {'text': 'POSITIVE'}}

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

------Action------
THOUGHT:
The code executed successfully, and we retrieved an interesting fact about May 7th: "Israeli archaeologists discover the tomb of Herod the Great south of Jerusalem". The sentiment analysis classified this fact as 'POSITIVE'. I will now summarize the solution.
ACTION TYPE:
code_summary
ACTION CONTENT:
from mnm.tool_api import get_date_fact, text_classification
fact_result = get_date_fact("05/07")
sentiment_result = text_classification(fact_result['text'])

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

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

------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