******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: I want a quick fact about the year 2018 and need some interesting insight from that. Can you provide this fact in a shortened form and tell me who was the significant person involved in that event ? 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 a quick fact about a specific year. Once I have the fact, I will look for a function that can provide a summary or shortened form of the text. Lastly, I will search for a function that can identify significant people involved in the event described by the fact. ACTION TYPE: search ACTION CONTENT: text: quick fact year text: summarize text: significant person -----Response----- RESPONSE (Retrieval Result): QUERY: text: quick fact year CODE BLOCKS: --- 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 } --- 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=[943, 971] type=FUNCTION content={ def search_movie(movie_title: str, movie_year: str, ckpt=None): """ Retrieve basic movie information, including title, year, genre, and director. """ # paren_start = input['movie_title'].find('(') url = "http://www.omdbapi.com/" params = { "t": movie_title, "y": movie_year, "plot": "short", "r": "json", "apikey": OMDB_API_KEY, } response = requests.get(url, params=params) result_formatted = response.json() selected_keys = ["Title", "Year", "Genre", "Director", "Plot"] desc = "" if len(result_formatted) > 0: for k, v in result_formatted.items(): # print(k, v) if k in selected_keys and len(v) > 0: desc += f"{k}: {v}\n" else: desc = "Movie not found!" result_formatted = {"text": desc} return result_formatted } --- (18 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 14 classes/functions found in unexpanded results (search again with the same query to see details): def get_math_fact(number: str, ckpt=None): ... # mnm/tool_api.py def get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py def object_processing(file_path): ... # mnm/tool_api.py def image_processing(img): ... # mnm/tool_api.py def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py QUERY: text: summarize CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[143, 163] type=FUNCTION content={ def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): """ Summarizes a given text into a few sentences using a pre-trained model. Args: text (str): The text to be summarized. ckpt: The checkpoint or model identifier to use for the summarization pipeline. Defaults to a pre-selected model checkpoint. Returns: dict: A dictionary containing the summarized text under the key 'text'. Examples: summary = text_summarization("The quick brown fox jumps over the lazy dog.") print(summary) # Outputs a summarized version of the input text. """ ... } --- file_path=mnm/tool_api.py lines=[985, 1011] type=FUNCTION content={ def wikipedia_simple_search(text: str, ckpt=None): """ Performs a basic search on Wikipedia to retrieve a summary of the most relevant page. Args: text (str): The search query text. ckpt: Unused parameter, can be anything (default is None). Returns: dict: A dictionary containing the formatted text summary of the most relevant Wikipedia page. Raises: This function may raise exceptions related to network issues or JSON decoding if the Wikipedia API changes or is unreachable. Examples: result = wikipedia_simple_search("Python programming language") # result might be something like: # {'text': 'Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with its use of significant indentation.'} """ ... } --- 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']) """ ... } --- (16 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 14 classes/functions found in unexpanded results (search again with the same query to see details): def image_crop_left(image, ckpt=None): ... # mnm/tool_api.py def image_crop_top(image, ckpt=None): ... # mnm/tool_api.py def image_crop_bottom(image, ckpt=None): ... # mnm/tool_api.py def image_crop_right(image, ckpt=None): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py def image_generation(text: str, ckpt=MODEL_SELECTION['image_generation']): ... # mnm/tool_api.py QUERY: text: significant person 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=[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'} """ ... } --- (20 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 16 classes/functions found in unexpanded results (search again with the same query to see details): def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py def text_processing(file_path): ... # mnm/tool_api.py def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py def get_location(city: str, ckpt=None): ... # mnm/tool_api.py def get_math_fact(number: str, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `get_year_fact` from `mnm/tool_api.py` can be used to retrieve an interesting fact about the year 2018. Then, the `text_summarization` function can be used to provide a shortened form of the fact. However, there doesn't seem to be a direct function to identify significant people involved in the event. I will use the `wikipedia_simple_search` function to search for the year 2018 on Wikipedia, which might include significant people in the summary. If this does not yield the desired result, I may need to consider alternative approaches or inform the user that the task cannot be fully completed as per the current codebase capabilities. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import get_year_fact, text_summarization, wikipedia_simple_search # Retrieve an interesting fact about the year 2018 year_fact = get_year_fact("2018") # Summarize the fact to get a shortened form summarized_fact = text_summarization(year_fact['text']) # Search Wikipedia for the year 2018 to find significant people involved in events of that year wikipedia_summary = wikipedia_simple_search("2018") -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"get_year_fact": , "text_summarization": , "wikipedia_simple_search": , "year_fact": {'text': 'we do not know what happened', 'number': 2018, 'found': False, 'type': 'year'}, "summarized_fact": {'text': 'We do not know what'}, "wikipedia_summary": {'text': '2018 January February March April May June July August September October November December 2018 (MMXVIII) was a common year starting on Monday of the'}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The `get_year_fact` function did not return a known fact for the year 2018, as indicated by the 'found': False in the result. The `text_summarization` function returned a very short summary stating "We do not know what", which is not useful. The `wikipedia_simple_search` function returned a partial summary of the year 2018, but it does not contain information about significant people. I will inform the user that the task cannot be fully completed as per the current codebase capabilities because there is no function to directly identify significant people involved in the events of the year 2018. ACTION TYPE: done ACTION CONTENT: False -----Response----- None ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): Please immediately take a code_summary action to generate a summary of your code produced so far. If you have not produced any code yet, make an educated guess based on what you have learned so far. ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: I will summarize the code produced so far, which includes calls to `get_year_fact`, `text_summarization`, and `wikipedia_simple_search` functions from the `mnm` codebase. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import get_year_fact, text_summarization, wikipedia_simple_search output0 = get_year_fact("2018") output1 = text_summarization(output0['text']) output2 = wikipedia_simple_search("2018") -----Response----- None