******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I am writing content for a pizzeria's website and I need a vivid, generated image displaying a chef pulling a pizza out of a pizza oven. However, I particularly need the left portion of the image that includes the bustling activities of the chef and the oven. Plus, I also need individual components of the image like the pizza, oven, and chef's gear separated out into different segments to give a detailed perspective, possibly for infographics or detailed visual descriptions." 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 generate an image based on a text description. This will be used to create the initial image of a chef pulling a pizza out of a pizza oven. After generating the image, I will need to find functions that can crop the image to focus on the left portion and segment the image into individual components like the pizza, oven, and chef's gear. ACTION TYPE: search ACTION CONTENT: text: generate image from text description text: crop image text: segment image into components -----Response----- RESPONSE (Retrieval Result): QUERY: text: generate image from 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 } --- (42 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 35 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 search_movie(movie_title: str, movie_year: str, ckpt=None): ... # 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 get_full_path_data(filename): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py QUERY: text: crop image CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[536, 556] type=FUNCTION content={ def image_crop_left(image, ckpt=None): """ Crops the left half of the provided image and returns it in a formatted dictionary. Args: image: The image to be cropped. ckpt: Optional checkpoint data (unused in the current implementation). Returns: A dictionary containing the cropped image with the key 'image'. Examples: Assuming `image` is a PIL Image object: >>> result = image_crop_left(image) >>> result['image'] # This will contain the left half of the original image. """ ... } --- file_path=mnm/tool_api.py lines=[581, 602] type=FUNCTION content={ def image_crop_top(image, ckpt=None): """ Crops the top half of an image and returns it in a dictionary. Args: image: The image to be cropped. ckpt: Optional checkpoint data (unused in the current implementation). Returns: A dictionary containing the cropped image with the key 'image'. Examples: >>> from PIL import Image >>> original_image = Image.open('path/to/image.jpg') >>> cropped_image_info = image_crop_top(original_image) >>> cropped_image = cropped_image_info['image'] """ ... } --- file_path=mnm/tool_api.py lines=[604, 625] type=FUNCTION content={ def image_crop_bottom(image, ckpt=None): """ Crops the bottom half of an image and returns it in a dictionary. Args: image: The image to be cropped. ckpt: Optional checkpoint data (unused in the current implementation). Returns: A dictionary containing the cropped image with the key 'image'. Examples: Assuming `image` is a PIL Image object: >>> cropped_image_result = image_crop_bottom(image) >>> cropped_image = cropped_image_result['image'] """ ... } --- (23 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 20 classes/functions found in unexpanded results (search again with the same query to see details): def image_crop_right(image, ckpt=None): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # 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_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def image_processing(img): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py QUERY: text: segment image into components 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=[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") } --- 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 } --- (43 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 29 classes/functions found in unexpanded results (search again with the same query to see details): 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 text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The `image_generation` function from `tool_api.py` can be used to generate an image based on a text description. After generating the image, the `image_crop_left` function can be used to crop the left portion of the image. Finally, the `image_segmentation` function can be used to segment the image into individual components like the pizza, oven, and chef's gear. I will now write the code to generate the image and then proceed with cropping and segmentation in subsequent steps. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation, image_crop_left, image_segmentation # Generate an image based on the text description description = "a chef pulling a pizza out of a pizza oven" generated_image_info = image_generation(text=description) # Assuming the image generation is successful, we proceed to crop the left portion of the image if 'image' in generated_image_info: cropped_image_info = image_crop_left(image=generated_image_info['image']) # Assuming the cropping is successful, we proceed to segment the image into individual components if 'image' in cropped_image_info: segmented_image_info = image_segmentation(image=cropped_image_info['image']) -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_generation": , "image_crop_left": , "image_segmentation": , "description": a chef pulling a pizza out of a pizza oven, "generated_image_info": {'image': }, "cropped_image_info": {'image': }, "segmented_image_info": {'image': , 'objects': [{'mask': array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [1., 1., 1., ..., 0., 0., 0.], [1., 1., 1., ..., 0., 0., 0.], [1., 1., 1., ..., 0., 0., 0.]], dtype=float32), 'label': 'person', 'bbox': [0, 37, 438, 1022], 'inst_id': 1}, {'mask': array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.],...} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The code executed successfully, and we have the generated image, the cropped image focusing on the left portion, and the segmented image information with individual components. The next step is to provide a summary of the solution using the code_summary action. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import image_generation, image_crop_left, image_segmentation output0 = image_generation(text="a chef pulling a pizza out of a pizza oven") output1 = image_crop_left(image=output0['image']) output2 = image_segmentation(image=output1['image']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The task has been completed successfully, and the user has acknowledged receipt of the code summary. ACTION TYPE: done ACTION CONTENT: True -----Response----- None