******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: Hey, can you look up the movie Parasite from the year 2019 and tell me who the director is . I'm also very curious about its origin, can you find that information from Wikipedia? 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 factual information about movies. I will search for a function that can handle movie information retrieval, which likely includes looking up movies by title and year. Once I find the function, I can use it to get the director of the movie "Parasite" from the year 2019. After that, I will search for a function that can fetch information from Wikipedia to find out about the movie's origin. ACTION TYPE: search ACTION CONTENT: text: movie AND text: information retrieval text: wikipedia AND text: information retrieval -----Response----- RESPONSE (Retrieval Result): QUERY: text: movie AND text: 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=[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=[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 } --- (9 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 8 classes/functions found in unexpanded results (search again with the same query to see details): def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py def get_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py 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 color_pop(image, object, ckpt=None): ... # mnm/tool_api.py def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py QUERY: text: wikipedia AND text: information retrieval CODE BLOCKS: --- 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=[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=[325, 346] type=FUNCTION content={ def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): """ Classifies an image into one of the predefined categories using a Vision Transformer (ViT) model. Args: image: The image to be classified, which will be processed by `image_processing` function. ckpt (str, optional): The checkpoint or model identifier to use for loading the pre-trained model. Defaults to `MODEL_SELECTION["image_classification"]`. Returns: dict: A dictionary with a key "text" that maps to the predicted category of the image. Examples: >>> result = image_classification(my_image) >>> print(result) {'text': 'cat'} """ ... } --- (11 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 8 classes/functions found in unexpanded results (search again with the same query to see details): def image_generation(text: str, ckpt=MODEL_SELECTION['image_generation']): ... # mnm/tool_api.py def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `search_movie` from `tool_api.py` can be used to retrieve basic movie information, including the director, which is what we need for the movie "Parasite" from the year 2019. Additionally, the function `wikipedia_simple_search` from the same file can be used to perform a basic search on Wikipedia to retrieve a summary of the most relevant page, which should include information about the movie's origin. I will now write the code to use these functions to get the required information. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import search_movie, wikipedia_simple_search # Retrieve information about the movie "Parasite" from the year 2019 movie_info = search_movie(movie_title="Parasite", movie_year="2019") # Perform a basic search on Wikipedia for the movie "Parasite" wikipedia_info = wikipedia_simple_search(text="Parasite 2019 film") -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"search_movie": , "wikipedia_simple_search": , "movie_info": {'text': 'Title: Parasite\nYear: 2019\nGenre: Drama, Thriller\nDirector: Bong Joon Ho\nPlot: Greed and class discrimination threaten the newly formed symbiotic relationship between the wealthy Park family and the destitute Kim clan.\n'}, "wikipedia_info": {'text': 'Parasite (Korean:\xa0기생충; RR:\xa0Gisaengchung) is a 2019 South Korean dark comedy thriller film directed by Bong Joon-ho, who co-wrote the screenplay with Han'}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The code executed successfully and we have retrieved the information about the movie "Parasite" from the year 2019, including the director's name, Bong Joon Ho. We also have a summary from Wikipedia that indicates the movie is a South Korean dark comedy thriller film. I will now provide a summary of the solution. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import search_movie, wikipedia_simple_search output0 = search_movie(movie_title="Parasite", movie_year="2019") output1 = wikipedia_simple_search(text="Parasite 2019 film") -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The task has been completed successfully. The user has received and stored the summary of the solution. ACTION TYPE: done ACTION CONTENT: True -----Response----- None