******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I have a photo stored as '270650-input.png'. I need to create a picture where there are skis on the wheel. Once you make that, just keep the bottom part of the photo, where the wheel is located." 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 edit the image by adding skis to the wheel. After that, I will need to find a function that can crop the image to keep only the bottom part where the wheel is located. I will start by searching for a function that can edit images based on a description. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: edit images description) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: edit images 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=[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") """ ... } --- (33 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 33 classes/functions found in unexpanded results (search again with the same query to see details): def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # 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 `image_editing` from the `tool_api.py` file seems to be the right choice for editing the image based on a text description. I will use this function to add skis to the wheel in the image. After that, I will need to search for a function that can crop the image to keep only the bottom part where the wheel is located. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: crop image) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (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'] """ ... } --- (20 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_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py def image_processing(img): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The function `image_crop_bottom` from the `tool_api.py` file is suitable for cropping the bottom half of the image. I will use this function after editing the image to keep only the bottom part where the wheel is located. Now, I will write the code to first edit the image by adding skis to the wheel using `image_editing` and then crop the bottom half of the image using `image_crop_bottom`. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_editing, image_crop_bottom # Path to the input image input_image_path = '/root/input_data/270650-input.png' # Edit the image to add skis on the wheel edit_prompt = "Add skis on the wheel." edited_image_result = image_editing(image=input_image_path, prompt=edit_prompt) # Assuming the edited image is returned as a PIL Image object, we can directly pass it to the crop function # Crop the bottom half of the edited image to keep only the part where the wheel is located cropped_image_result = image_crop_bottom(image=edited_image_result['image']) # The result is a dictionary with the key 'image' containing the cropped image cropped_image = cropped_image_result['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_editing": , "image_crop_bottom": , "input_image_path": /root/input_data/270650-input.png, "edit_prompt": Add skis on the wheel., "edited_image_result": {'image': }, "cropped_image_result": {'image': }, "cropped_image": } ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The code has been executed without errors, and the relevant variables indicate that the image has been edited and cropped as requested. The final cropped image is stored in the variable `cropped_image`. The task is complete. ACTION TYPE: done ACTION CONTENT: True -----Response----- None ******************************************************************************** Step 6 ******************************************************************************** ------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 7 ******************************************************************************** ------Action------ THOUGHT: I will now provide a summary of the code that was used to edit the image by adding skis to the wheel and then cropping the bottom half of the image where the wheel is located. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import image_editing, image_crop_bottom edited_image_result = image_editing(image='/root/input_data/270650-input.png', prompt="Add skis on the wheel.") cropped_image_result = image_crop_bottom(image=edited_image_result['image']) -----Response----- None