******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I've been digging into the history of the Super Bowl and found this piece of information which says, 'The league eventually narrowed the bids to three sites: New Orleans' Mercedes-Benz Superdome, Miami's Sun Life Stadium, and the San Francisco Bay Area's Levi's Stadium.' From this text, can you clarify which venue in Louisiana was in the running for Super Bowl 50 and afterwards, it would be helpful if you generate a representative image of this venue." 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: Before generating an image of the venue, I need to identify the venue in Louisiana that was in the running for Super Bowl 50 from the provided text. I will use the text understanding functions from the `mnm` codebase to extract the relevant information. Once I have the name of the venue, I will then search for an image generation function that can create a representative image of the venue. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: extract OR text: information OR text: retrieve) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: extract OR text: information OR text: retrieve) 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=[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']) """ ... } --- file_path=mnm/tool_api.py lines=[51, 77] type=FUNCTION content={ def text_processing(file_path): """ preprocess the text file Args: - the path of .doc .docx .txt file Returns: - text with string format Notes: - This function is designed for text input. """ # Check the file extension if file_path.endswith(".txt"): with open(file_path, "r") as file: content = file.read() elif file_path.endswith(".doc") or file_path.endswith(".docx"): # Use textract to extract text from doc and docx files content = textract.process(file_path).decode("utf-8") else: # if the file is not .txt .doc .docx, then it is a string, directly return the stirng return file_path return content } --- (36 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 wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # 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 object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `wikipedia_simple_search` seems like it could be used to retrieve information from Wikipedia, which might include details about the venue in Louisiana. However, since the user has provided a specific text snippet, I will search again to find a function that can extract information directly from the provided text. If no such function is found, I will then use `wikipedia_simple_search` as a fallback to search for the venue on Wikipedia. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: wikipedia_simple_search) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: wikipedia_simple_search) 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=[931, 941] type=FUNCTION content={ def get_location(city: str, ckpt=None): """ Convert a city name or address to geographical coordinates using OpenStreetMap's Nominatim API. """ url = "https://nominatim.openstreetmap.org/search" headers = {"User-Agent": "m&ms"} params = {"q": city, "format": "json"} response = requests.get(url, params=params, headers=headers) result_formatted = response.json()[0] 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.'} """ ... } --- (26 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 26 classes/functions found in unexpanded results (search again with the same query to see details): def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py def image_generation(text: str, ckpt=MODEL_SELECTION['image_generation']): ... # mnm/tool_api.py def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py def object_processing(file_path): ... # mnm/tool_api.py def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The function `wikipedia_simple_search` can be used to search Wikipedia for a summary of the most relevant page based on the search query. I will use this function to search for "Mercedes-Benz Superdome" since it is the venue mentioned in the user's text snippet that is located in Louisiana. After confirming the venue, I will use the `image_generation` function to generate a representative image of the venue. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import wikipedia_simple_search, image_generation # Search Wikipedia for the venue "Mercedes-Benz Superdome" in Louisiana venue_info = wikipedia_simple_search("Mercedes-Benz Superdome") # Generate a representative image of the venue venue_image = image_generation("Mercedes-Benz Superdome") -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"wikipedia_simple_search": , "image_generation": , "venue_info": {'text': 'The Caesars Superdome (originally Louisiana Superdome and formerly Mercedes-Benz Superdome), commonly known as the Superdome, is a domed multi-purpose'}, "venue_image": {'image': }} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The venue information has been successfully retrieved, and it confirms that the "Mercedes-Benz Superdome" (now known as the "Caesars Superdome") was the venue in Louisiana. Additionally, a representative image of the venue has been generated. I will now provide a code summary of the solution. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import wikipedia_simple_search, image_generation output0 = wikipedia_simple_search("Mercedes-Benz Superdome") output1 = image_generation("Mercedes-Benz Superdome") -----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