******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I'm organizing a small film discussion about 'Birdman or (The Unexpected Virtue of Ignorance)' from 2014. Could you help me by pulling up the main details and using that data to create a brief introduction for the discussion? And once the intro is generated, I'd like to understand the sentiment of the discussion prompt – whether it leans more towards being 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 factual information about movies. This function will likely be part of the information retrieval functions. Once I have the details about the movie "Birdman or (The Unexpected Virtue of Ignorance)", I will search for a function that can generate a brief introduction based on the details. After generating the introduction, I will look for a function that can analyze the sentiment of the text to determine if it's positive or negative. I will start by searching for the movie information retrieval function. ACTION TYPE: search ACTION CONTENT: text: movie AND (type: FUNCTION) -----Response----- RESPONSE (Retrieval Result): QUERY: text: movie AND (type: FUNCTION) 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=[111, 141] type=FUNCTION content={ def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): """ Generates a continuation of the given input text using a specified language model. Args: text (str): The input text prompt to be continued. ckpt (str, optional): The identifier for the language model to be used. Defaults to the model specified in MODEL_SELECTION["text_generation"]. Returns: dict: A dictionary containing the generated text under the key 'text'. Raises: KeyError: If the OPENAI_API_KEY environment variable is not set. APIError: If the OpenAI API encounters an issue with the request. Examples: >>> generated_text = text_generation("Once upon a time") >>> print(generated_text) {'text': '...continued text from the model...'} """ ... } --- 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'] """ ... } --- (8 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 get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py def get_year_fact(year: 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 get_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `search_movie` from `tool_api.py` can be used to retrieve basic movie information. I will use this function to get the details about "Birdman or (The Unexpected Virtue of Ignorance)" from 2014. After obtaining the movie details, I will use the `text_generation` function to create a brief introduction for the discussion. Once the introduction is generated, I will need to find a function that can analyze the sentiment of the text. I will search for a sentiment analysis function next. ACTION TYPE: search ACTION CONTENT: text: sentiment AND (type: FUNCTION) -----Response----- RESPONSE (Retrieval Result): QUERY: text: sentiment AND (type: FUNCTION) 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=[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=[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']) """ ... } --- (13 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 13 classes/functions found in unexpanded results (search again with the same query to see details): def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # 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_year_fact(year: 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 ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The function `text_classification` can be used to classify the sentiment of the text as positive or negative. Now that I have identified the necessary functions, I will write the code to first retrieve the movie details using `search_movie`, then generate a brief introduction using `text_generation`, and finally analyze the sentiment of the introduction using `text_classification`. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import search_movie, text_generation, text_classification # Retrieve movie details movie_details = search_movie("Birdman or (The Unexpected Virtue of Ignorance)", "2014") # Generate a brief introduction for the discussion intro_prompt = f"Let's discuss the movie titled '{movie_details['text']}' which was released in 2014. " intro_details = text_generation(intro_prompt) # Analyze the sentiment of the discussion prompt sentiment_result = text_classification(intro_details['text']) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': '"Birdman or (The Unexpected Virtue of Ignorance)" is a unique and creative film that garnered critical acclaim and won four Academy Awards, including Best Picture and Best Director.\n\nOne of the most striking elements of the movie is its use of the long take technique. The film is edited to make it look like one continuous shot, creating a sense of immediacy and intensity. This technique adds to the feeling of chaos and pressure that the main character, Riggan Thomson, must face as he tries to mount a successful Broadway production.\n\nThe film explores themes of celebrity, identity, and artistic integrity. Riggan struggles with his own sense of self-worth and the pressures of trying to prove himself as a legitimate artist outside of his superhero role. The film also delves into the nature of fame and the impact it can have on a person\'s mental health.\n\nThe performances in the movie are outstanding, particularly Michael Keaton\'s portrayal of Riggan Thomson. Keaton brings a rawness and vulnerability to the character, making Riggan both sympathetic and frustrating at the same time. The supporting cast, including Edward Norton, Emma Stone, and Zach Galifianakis, all deliver strong performances as well.\n\nOverall, "Birdman" is a thought-provoking and visually stunning film that challenges audiences to think about the nature of art, performance, and celebrity. It\'s a must-watch for anyone interested in cinema that pushes the boundaries of traditional storytelling.'} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"search_movie": , "text_generation": , "text_classification": , "movie_details": {'text': 'Title: Birdman or (The Unexpected Virtue of Ignorance)\nYear: 2014\nGenre: Comedy, Drama\nDirector: Alejandro G. Iñárritu\nPlot: A washed-up superhero actor attempts to revive his fading career by writing, directing, and starring in a Broadway production.\n'}, "intro_prompt": Let's discuss the movie titled 'Title: Birdman or (The Unexpected Virtue of Ignorance) Year: 2014 Genre: Comedy, Drama Director: Alejandro G. Iñárritu Plot: A washed-up superhero actor attempts to revive his fading career by writing, directing, and starring in a Broadway production. ' which was released in 2014. , "intro_details": {'text': '"Birdman or (The Unexpected Virtue of Ignorance)" is a unique and creative film that garnered critical acclaim and won four Academy Awards, including Best Picture and Best Director.\n\nOne of the most striking elements of the movie is its use of the long take technique. The film is edited to make it look like one continuous shot, creating a sense of immediacy and intensity. This technique adds to the feeling of chaos and pressure that the main character, Riggan Thomson, must face as he..., "sentiment_result": {'text': 'POSITIVE'}} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The code execution was successful. The movie details were retrieved, a brief introduction was generated, and the sentiment of the introduction was classified as positive. I will now provide a summary of the solution. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import search_movie, text_generation, text_classification output0 = search_movie("Birdman or (The Unexpected Virtue of Ignorance)", "2014") output1 = text_generation(f"Let's discuss the movie titled '{output0['text']}' which was released in 2014. ") output2 = text_classification(output1['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