******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I recently heard about the movie '12 Years a Slave' from 2013. Can you find some information on it and provide a brief overview?" 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: To find information about the movie '12 Years a Slave', I will use the information retrieval functions from the `mnm` codebase. I will search for a function that can retrieve information about movies, which is likely to be a function that can query a database or an API for movie information. ACTION TYPE: search ACTION CONTENT: text: movie information retrieval -----Response----- RESPONSE (Retrieval Result): QUERY: text: movie information retrieval CODE BLOCKS: --- 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 } --- 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']) """ ... } --- (40 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_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py def image_generation(text: str, ckpt=MODEL_SELECTION['image_generation']): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `search_movie` from the `mnm` codebase seems to be the appropriate function to retrieve information about the movie '12 Years a Slave'. It takes the movie title and year as arguments and returns basic movie information. I will use this function to get the overview of the movie. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import search_movie # Retrieve information about the movie '12 Years a Slave' from 2013 movie_info = search_movie("12 Years a Slave", "2013") print(movie_info) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': 'Title: 12 Years a Slave\nYear: 2013\nGenre: Biography, Drama, History\nDirector: Steve McQueen\nPlot: In the antebellum United States, Solomon Northup, a free Black man from upstate New York, is abducted and sold into slavery.\n'} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"search_movie": , "movie_info": {'text': 'Title: 12 Years a Slave\nYear: 2013\nGenre: Biography, Drama, History\nDirector: Steve McQueen\nPlot: In the antebellum United States, Solomon Northup, a free Black man from upstate New York, is abducted and sold into slavery.\n'}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The code execution was successful, and we have retrieved the information about the movie '12 Years a Slave'. The output contains the title, year, genre, director, and a brief plot summary. I will now provide a code summary of the solution. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import search_movie movie_info = search_movie("12 Years a Slave", "2013") -----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 summary. ACTION TYPE: done ACTION CONTENT: True -----Response----- None