******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I'm working on a project about notable events in recent years. For 2016, could you provide a captivating fact followed by a visual representation of it? I intend to use the generated image for my PowerPoint presentation." 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 that can retrieve a captivating fact about the year 2016. Once I have the fact, I will search for a function that can generate a visual representation of that fact. I will start by searching for a function that can retrieve facts about a specific year. ACTION TYPE: search ACTION CONTENT: text: (interesting facts OR factual information) AND (text: year) -----Response----- RESPONSE (Retrieval Result): QUERY: text: (interesting facts OR factual information) AND (text: year) CODE BLOCKS: --- 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=[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']) """ ... } --- (All code blocks matching the query were returned.) ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `get_year_fact` from the `tool_api.py` file seems to be the right choice for retrieving an interesting fact about the year 2016. I will use this function to get the fact. After obtaining the fact, I will need to find a function that can generate a visual representation of the text. I will search for such a function next. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import get_year_fact # Retrieve an interesting fact about the year 2016 year_fact = get_year_fact("2016") print(year_fact) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': "NASA's Juno spacecraft is expected to arrive at Jupiter", 'number': 2016, 'found': True, 'type': 'year'} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"get_year_fact": , "year_fact": {'text': "NASA's Juno spacecraft is expected to arrive at Jupiter", 'number': 2016, 'found': True, 'type': 'year'}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: Now that I have an interesting fact about the year 2016, I need to find a function that can generate a visual representation of this fact. I will search for a function that can create an image from a text description. ACTION TYPE: search ACTION CONTENT: text: generate image AND (text: description OR text: text) -----Response----- RESPONSE (Retrieval Result): QUERY: text: generate image AND (text: description OR text: text) CODE BLOCKS: --- 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=[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=[251, 267] type=FUNCTION content={ def image_captioning( image, ckpt=MODEL_SELECTION["image_captioning"] ): # alternative: nlpconnect/vit-gpt2-image-captioning (testing, blip is better than vit-gpt2)z """ It takes an image and generates a text caption of the image. """ image = image_processing(image) pipe = pipeline("image-to-text", model=ckpt) result = pipe( image ) # [{'generated_text': 'there is a small white dog sitting next to a cell phone'}] result_formatted = {"text": result[0]["generated_text"]} return result_formatted } --- (17 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 15 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 text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # 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 ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The function `image_generation` from the `tool_api.py` file is suitable for generating an image from a text description. I will use this function to create a visual representation of the fact about NASA's Juno spacecraft arriving at Jupiter in 2016. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation # Generate an image based on the fact about NASA's Juno spacecraft in 2016 fact_image = image_generation("NASA's Juno spacecraft is expected to arrive at Jupiter") print(fact_image) -----Response----- RESPONSE (Execution Result): STDOUT: {'image': } RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_generation": , "fact_image": {'image': }} ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The image has been successfully generated. The user can now use this image for their PowerPoint presentation. The task is complete. ACTION TYPE: done ACTION CONTENT: True -----Response----- None ******************************************************************************** Step 7 ******************************************************************************** ------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 8 ******************************************************************************** ------Action------ THOUGHT: The user has requested a code summary of the actions taken to retrieve a fact about the year 2016 and to generate a visual representation of that fact. I will provide a summary of the function calls made. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import get_year_fact, image_generation output0 = get_year_fact("2016") output1 = image_generation("NASA's Juno spacecraft is expected to arrive at Jupiter") -----Response----- None