******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I'm trying to create a dynamic new photo for an agricultural-themed event poster. Start with '111376-input.png', and based on the phrase 'Have the cow wear a hat.', do your magic. Once that's done, for conciseness and rhythm, would you keep only the left part of the image?" 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 the function that can edit images based on a description, which should handle the request to "Have the cow wear a hat." After that, I will look for a function that can crop the image to keep only the left part. I will start by searching for the image editing function that can modify an image based on a description. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: edit images given the original image and a description of how to modify the image) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: edit images given the original image and a description of how to modify the image) 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=[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'] """ ... } --- (36 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 36 classes/functions found in unexpanded results (search again with the same query to see details): def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def image_crop_left(image, ckpt=None): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # 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 def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `image_editing` from the `tool_api.py` file seems to be the right function to edit the image based on the description "Have the cow wear a hat." I will use this function to edit the image. Additionally, the prototype for `image_crop_left` is mentioned, which should be the function to keep only the left part of the image. I will search for the full implementation of `image_editing` and `image_crop_left` to understand how to use them properly. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: image_editing) (type: FUNCTION) AND (text: image_crop_left) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: image_editing) CODE BLOCKS: --- 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") """ ... } --- 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 } --- 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'} """ ... } --- (30 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 30 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_crop(image, object, ckpt=None): ... # mnm/tool_api.py def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py def image_crop_left(image, ckpt=None): ... # mnm/tool_api.py def image_crop_bottom(image, ckpt=None): ... # mnm/tool_api.py def image_crop_right(image, ckpt=None): ... # mnm/tool_api.py def image_processing(img): ... # mnm/tool_api.py QUERY: (type: FUNCTION) AND (text: image_crop_left) 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=[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'] """ ... } --- file_path=mnm/tool_api.py lines=[558, 579] type=FUNCTION content={ def image_crop_right(image, ckpt=None): """ Crops the right half of the provided image. Args: image: The image to be cropped. ckpt: Optional; a checkpoint or reference point for the cropping process, not used in the current implementation. Returns: A dictionary containing the cropped image under the key 'image'. Examples: Assuming `img` is a PIL Image object: >>> cropped_result = image_crop_right(img) >>> cropped_image = cropped_result['image'] """ ... } --- (16 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 16 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 image_processing(img): ... # mnm/tool_api.py def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: I have found the implementations for both `image_editing` and `image_crop_left` functions. I will now write the code to first edit the image with the description "Have the cow wear a hat" using `image_editing` and then crop the left part of the image using `image_crop_left`. The image file is located at `/root/input_data/111376-input.png`. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_editing, image_crop_left from PIL import Image # Load the original image original_image_path = '/root/input_data/111376-input.png' original_image = Image.open(original_image_path) # Edit the image based on the description edited_image_info = image_editing(image=original_image, prompt="Have the cow wear a hat.") # Crop the left part of the edited image cropped_image_info = image_crop_left(image=edited_image_info['image']) # Save the final image final_image_path = '/root/playground/edited_and_cropped_image.png' cropped_image_info['image'].save(final_image_path) # Return the path of the saved image final_image_path -----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_left": , "Image": , "original_image_path": /root/input_data/111376-input.png, "original_image": , "edited_image_info": {'image': }, "cropped_image_info": {'image': }, "final_image_path": /root/playground/edited_and_cropped_image.png} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The code executed without error, and the relevant variables indicate that the image has been edited and cropped as requested. The final image has been saved to '/root/playground/edited_and_cropped_image.png'. 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: The user has requested a code summary of the actions taken to edit and crop the image. I will provide a summary of the Python code that was executed to perform the task. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import image_editing, image_crop_left from PIL import Image original_image_path = '/root/input_data/111376-input.png' original_image = Image.open(original_image_path) edited_image_info = image_editing(image=original_image, prompt="Have the cow wear a hat.") cropped_image_info = image_crop_left(image=edited_image_info['image']) final_image_path = '/root/playground/edited_and_cropped_image.png' cropped_image_info['image'].save(final_image_path) -----Response----- None