******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: I'm trying to understand the religious teaching landscape better. Within religious and spiritual instruction, teachers like gurus, mullahs, rabbis, pastors/youth pastors, and lamas teach religious texts including the Quran, Torah, or Bible. Could I trouble you to find another broad term that might be commonly used to describe such teachers? After you find this out, could you pull up the most relevant Wikipedia page about that term? Once we have that information, can you categorize the overall mood or sentiment found in that Wikipedia page's summary? 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 broad term that is commonly used to describe religious and spiritual teachers. I will use the information retrieval functions from the `mnm` codebase to retrieve factual information or interesting facts that might include such a term. Once I have the term, I will proceed to pull up the most relevant Wikipedia page about that term. Finally, I will categorize the overall mood or sentiment found in the Wikipedia page's summary using the text understanding functions from the `mnm` codebase. I will start by searching for a function that can help me find a broad term for religious teachers. ACTION TYPE: search ACTION CONTENT: text: (religious OR spiritual) AND (teachers OR gurus OR mullahs OR rabbis OR pastors OR lamas) AND (term OR title OR name) -----Response----- RESPONSE (Retrieval Result): QUERY: text: (religious OR spiritual) AND (teachers OR gurus OR mullahs OR rabbis OR pastors OR lamas) AND (term OR title OR name) 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=[228, 249] type=FUNCTION content={ def image_generation(text: str, ckpt=MODEL_SELECTION["image_generation"]): """ It takes a text prompt and generates an image that matches the text description. """ if ckpt == "stabilityai/stable-diffusion-xl-base-1.0": text = text_processing(text) from diffusers import DiffusionPipeline generator = DiffusionPipeline.from_pretrained( pretrained_model_name_or_path=ckpt, torch_dtype=torch.float16, use_safetensors=True, variant="fp16", ).to(DEVICE) result = generator(prompt=text).images[0] result_formatted = {"image": result} return result_formatted } --- file_path=mnm/tool_api.py lines=[269, 323] type=FUNCTION content={ def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): """ Edits an image based on a given text prompt using a specified model checkpoint. Args: image: The input image to be edited. prompt (str): The text prompt describing the desired edits. ckpt: The model checkpoint to use for image editing. Defaults to MODEL_SELECTION["image_editing"]. Returns: A dictionary with the key 'image' containing the edited image. Raises: ImportError: If the necessary libraries for the chosen model are not installed. Examples: # Using the default checkpoint edited_image = image_editing(input_image, "A cat with a party hat.") # Using a specific checkpoint edited_image = image_editing(input_image, "A dog on a skateboard.", ckpt="timbrooks/instruct-pix2pix") """ ... } --- (8 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 6 out of 6 classes/functions found in unexpanded results (search again with the same query to see details): def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The search results did not directly provide a function that finds a broad term for religious teachers. However, the prototype for a function named `wikipedia_simple_search` was mentioned in the unexpanded results. This function seems like it could be used to search Wikipedia for a term related to religious teachers. I will search again with the same query to get the details of the `wikipedia_simple_search` function and use it to find a broad term for religious teachers. ACTION TYPE: search ACTION CONTENT: text: wikipedia_simple_search -----Response----- RESPONSE (Retrieval Result): QUERY: 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=[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 } --- (42 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 27 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 image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # 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: Now that I have the details of the `wikipedia_simple_search` function, I can use it to search for a broad term that describes religious and spiritual teachers. I will execute a search with a query that encompasses the terms provided by the user (gurus, mullahs, rabbis, pastors/youth pastors, and lamas) and religious texts (Quran, Torah, Bible) to find a common term. Once I have the term, I will use the same function to pull up the most relevant Wikipedia page about that term. Then, I will use the text understanding functions to analyze the sentiment of the Wikipedia page's summary. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import wikipedia_simple_search # Search for a broad term that describes religious and spiritual teachers broad_term_search_result = wikipedia_simple_search("religious and spiritual teachers like gurus, mullahs, rabbis, pastors, lamas teaching Quran, Torah, Bible") -----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": , "broad_term_search_result": {'text': ''}} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The search for a broad term that describes religious and spiritual teachers did not return any text in the result. This could be due to the specificity of the query or an issue with the Wikipedia API. I will attempt a more general search using the `wikipedia_simple_search` function to find a common term for religious teachers. ACTION TYPE: code ACTION CONTENT: # Attempt a more general search to find a common term for religious teachers general_term_search_result = wikipedia_simple_search("religious teacher") -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"general_term_search_result": {'text': 'This is an index of religious honorifics from various religions. List of religious topics List of education topics "Canon 406". Code of Canon Law. The'}} ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The search returned a text snippet that mentions an "index of religious honorifics from various religions." This suggests that the term "religious honorifics" might be a broad term used to describe religious teachers. I will now use the `wikipedia_simple_search` function again to pull up the most relevant Wikipedia page about "religious honorifics" and then analyze the sentiment of the page's summary. ACTION TYPE: code ACTION CONTENT: # Pull up the most relevant Wikipedia page about "religious honorifics" wikipedia_page_result = wikipedia_simple_search("religious honorifics") -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"wikipedia_page_result": {'text': 'This is an index of religious honorifics from various religions. List of religious topics List of education topics "Canon 406". Code of Canon Law. The'}} ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The result from the Wikipedia search for "religious honorifics" is the same as the previous search result, which indicates that it might be the summary of the Wikipedia page for this term. Now, I will search for a text understanding function that can analyze the sentiment of the provided text summary. ACTION TYPE: search ACTION CONTENT: text: sentiment AND (analysis OR classification) -----Response----- RESPONSE (Retrieval Result): QUERY: text: sentiment AND (analysis OR classification) 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=[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'} """ ... } --- 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.'} """ ... } --- (14 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 12 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 image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def get_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py ******************************************************************************** Step 8 ******************************************************************************** ------Action------ THOUGHT: The `text_classification` function appears to be suitable for sentiment analysis, as it classifies text into categories which could include sentiment labels such as 'POSITIVE' or 'NEGATIVE'. I will use this function to analyze the sentiment of the Wikipedia page summary that we retrieved earlier. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import text_classification # Analyze the sentiment of the Wikipedia page summary sentiment_result = text_classification(wikipedia_page_result['text']) -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"text_classification": , "sentiment_result": {'text': 'POSITIVE'}} ******************************************************************************** Step 9 ******************************************************************************** ------Action------ THOUGHT: The sentiment analysis of the Wikipedia page summary has been successfully performed, and the result indicates that the sentiment is 'POSITIVE'. This completes the user's request to categorize the overall mood or sentiment found in the Wikipedia page's summary. ACTION TYPE: done ACTION CONTENT: True -----Response----- None ******************************************************************************** Step 10 ******************************************************************************** ------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 11 ******************************************************************************** ------Action------ THOUGHT: The user has requested a summary of the code produced so far. I will summarize the steps taken to find a broad term for religious teachers, retrieve the Wikipedia page summary for that term, and analyze the sentiment of the summary. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import wikipedia_simple_search, text_classification general_term_search_result = wikipedia_simple_search("religious teacher") wikipedia_page_result = wikipedia_simple_search("religious honorifics") sentiment_result = text_classification(wikipedia_page_result['text']) -----Response----- None