******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I'm working on a presentation about cool things from 2022. Could you provide me with an interesting fact from that year and create an image based on it? Once the image is ready, could you also break it down into its constituent parts?" 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 an interesting fact from the year 2022. Once I have the fact, I can use it to generate an image. After the image is generated, I will need to find a function that can break down the image into its constituent parts, likely through object detection or segmentation. I will start by searching for a function that can retrieve interesting facts about a specific year. ACTION TYPE: search ACTION CONTENT: text: interesting fact AND (text: year OR text: date) -----Response----- RESPONSE (Retrieval Result): QUERY: text: interesting fact AND (text: year OR text: date) 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=[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=[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 } --- (6 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 5 out of 5 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 optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** 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 2022. I will use this function to get the fact. After obtaining the fact, I will search for a function that can generate an image based on text, and then another function to break down the image into its constituent parts. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import get_year_fact # Retrieve an interesting fact from the year 2022 fact = get_year_fact("2022") print(fact) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': "China's large orbital station expected to be completed", 'number': 2022, 'found': True, 'type': 'year'} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"get_year_fact": , "fact": {'text': "China's large orbital station expected to be completed", 'number': 2022, 'found': True, 'type': 'year'}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: Now that I have an interesting fact from 2022, I need to search for a function that can generate an image based on this text. After generating the image, I will look for a function that can segment the image into its constituent parts. ACTION TYPE: search ACTION CONTENT: text: generate image AND text: description -----Response----- RESPONSE (Retrieval Result): QUERY: text: generate image AND text: description 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 image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # 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 based on the text description we obtained from the interesting fact about 2022. I will use this function to generate the image. After that, I will search for the `image_segmentation` function to break down the generated image into its constituent parts. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation # Generate an image based on the interesting fact from 2022 description = fact['text'] generated_image = image_generation(description) print(generated_image) -----Response----- RESPONSE (Execution Result): STDOUT: {'image': } RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_generation": , "description": China's large orbital station expected to be completed, "generated_image": {'image': }} ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: Now that the image has been generated, I will search for the `image_segmentation` function to segment the image into its constituent parts. ACTION TYPE: search ACTION CONTENT: text: image_segmentation -----Response----- RESPONSE (Retrieval Result): QUERY: text: image_segmentation CODE BLOCKS: --- 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=[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=[87, 97] type=FUNCTION content={ def image_processing(img): # different models have different requirements for image input, so we will do preprocessing in each function if isinstance(img, Image.Image): return img.convert("RGB") elif isinstance(img, str): if os.path.exists(img): return Image.open(img).convert("RGB") else: full_path = get_full_path_data(img) return Image.open(full_path).convert("RGB") } --- (29 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 25 classes/functions found in unexpanded results (search again with the same query to see details): def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # 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_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The `image_segmentation` function is what I need to segment the generated image into its constituent parts. I will use this function and pass the generated image to it. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_segmentation # Segment the generated image into its constituent parts segmentation_result = image_segmentation(generated_image['image']) print(segmentation_result) -----Response----- RESPONSE (Execution Result): STDOUT: {'image': , 'objects': [{'mask': array([[1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]], dtype=float32), 'label': 'sky-other-merged', 'bbox': [0, 0, 1023, 363], 'inst_id': 1}, {'mask': array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]], dtype=float32), 'label': 'person', 'bbox': [558, 297, 598, 378], 'inst_id': 2}, {'mask': array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]], dtype=float32), 'label': 'person', 'bbox': [589, 307, 613, 362], 'inst_id': 3}, {'mask': array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.]], dtype=float32), 'label': 'airplane', 'bbox': [0, 5, 1023, 1023], 'inst_id': 4}, {'mask': array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]], dtype=float32), 'label': 'person', 'bbox': [471, 317, 531, 378], 'inst_id': 5}]} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_segmentation": , "segmentation_result": {'image': , 'objects': [{'mask': array([[1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], [1., 1., 1., ..., 1., 1., 1.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]], dtype=float32), 'label': 'sky-other-merged', 'bbox': [0, 0, 1023, 363], 'inst_id': 1}, {'mask': array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0....} ******************************************************************************** Step 8 ******************************************************************************** ------Action------ THOUGHT: The image has been successfully segmented into its constituent parts, and the segmentation result contains the original image along with a list of objects, each with its own mask, label, bounding box, and instance ID. The task is now complete. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import get_year_fact, image_generation, image_segmentation fact = get_year_fact("2022") generated_image = image_generation(fact['text']) segmentation_result = image_segmentation(generated_image['image']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 9 ******************************************************************************** ------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