******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "From the '11561.jpg', please identify the main subject, make a new image that represents the same concept, and provide a written summary of what the newly created image depicts." 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 identify the main subject in the image '11561.jpg'. For this, I will search for a function in the codebase that can perform object detection to get the labels and bounding boxes of objects in the image. Once I have identified the main subject, I will then search for a function that can generate a new image based on a text description. Finally, I will need to find a function that can provide a written summary of an image. I will start by searching for the object detection function. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: detect objects) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: detect objects) CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[377, 411] type=FUNCTION content={ def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): """ Performs object detection on an image using a DETR (DEtection TRansformer) model. Args: image: The image on which to perform object detection. ckpt: A string representing the model checkpoint to use for object detection. Defaults to a value from MODEL_SELECTION["object_detection"]. Returns: A dictionary with two keys: - 'image': The processed image. - 'objects': A list of dictionaries, each containing a 'bbox' key with the bounding box coordinates and a 'label' key with the object's label. Raises: ImportError: If the required modules 'torch' or 'transformers' are not installed. Examples: >>> result = object_detection(my_image) >>> print(result['objects']) [{'bbox': [x1, y1, x2, y2], 'label': 'object_label'}, ...] """ ... } --- file_path=mnm/tool_api.py lines=[721, 729] type=FUNCTION content={ def count(objects, ckpt=None): """ It takes a list of objects and returns the count of the objects. """ objs = objects result_formatted = {"number": len(objs)} return result_formatted } --- file_path=mnm/tool_api.py lines=[810, 838] type=FUNCTION content={ def select_object(objects, object_name, ckpt=None): """ Selects an object from a list of objects based on the input object name. Args: objects (list): A list of dictionaries, where each dictionary represents an object with a "label" key. object_name (str): The name of the object to be selected. ckpt (optional): A checkpoint or state to resume from, not used in the current implementation. Returns: dict: A dictionary with a single key "object" that maps to the selected object dictionary. Examples: >>> objects_list = [{'label': 'apple'}, {'label': 'banana'}] >>> select_object(objects_list, 'apple') {'object': {'label': 'apple'}} """ ... } --- (32 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 32 classes/functions found in unexpanded results (search again with the same query to see details): def tag(image, objects, ckpt=None): ... # mnm/tool_api.py def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py def object_processing(file_path): ... # mnm/tool_api.py def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The `object_detection` function from the `tool_api.py` file seems to be the right choice for detecting objects in the image. It returns a list of objects with their bounding boxes and labels. I will use this function to identify the main subject in the image '11561.jpg'. After identifying the main subject, I will need to search for a function that can generate a new image based on a text description. I will proceed with the object detection step first. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import object_detection # Load the image '11561.jpg' and perform object detection image_path = '/root/input_data/11561.jpg' with open(image_path, 'rb') as file: image_data = file.read() # Perform object detection detection_result = object_detection(image_data) main_subject = detection_result['objects'][0]['label'] # Assuming the first object is the main subject main_subject -----Response----- RESPONSE (Execution Result): EXECUTION ERROR: Traceback (most recent call last): File "/root/codenav/codenav/environments/code_env.py", line 88, in exec_with_informative_errors exec(code_str, global_vars) File "", line 9, in File "/root/mnm/mnm/tool_api.py", line 392, in object_detection inputs = processor(images=image, return_tensors="pt") File "/opt/conda/lib/python3.10/site-packages/transformers/image_processing_utils.py", line 551, in __call__ return self.preprocess(images, **kwargs) File "/opt/conda/lib/python3.10/site-packages/transformers/models/detr/image_processing_detr.py", line 1286, in preprocess images = make_list_of_images(images) File "/opt/conda/lib/python3.10/site-packages/transformers/image_utils.py", line 162, in make_list_of_images raise ValueError( ValueError: Invalid image type. Expected either PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray, but got . RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"object_detection": , "image_path": /root/input_data/11561.jpg, "file": <_io.BufferedReader name='/root/input_data/11561.jpg'>, "image_data": b'\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x00\x00\x01\x00\x01\x00\x00\xff\xdb\x00C\x00\x08\x06\x06\x07\x06\x05\x08\x07\x07\x07\t\t\x08\n\x0c\x14\r\x0c\x0b\x0b\x0c\x19\x12\x13\x0f\x14\x1d\x1a\x1f\x1e\x1d\x1a\x1c\x1c $.\' ",#\x1c\x1c(7),01444\x1f\'9=82<.342\xff\xdb\x00C\x01\t\t\t\x0c\x0b\x0c\x18\r\r\x182!\x1c!22222222222222222222222222222222222222222222222222\xff\xc0\x00\x11\x08\x01w\x01\xf4\x03\x01"\x00\x02\x11\x01\x03\x11\x01\xff\xc4\x00\x1f\x00\x00\x01\x05\x01\x01\x01\x01\x01\x01\x00\x00\x00\x...} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The execution error indicates that the `object_detection` function expects an image in a specific format, such as a PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor, or jax.ndarray, but it received a NoneType, which means the image data was not correctly loaded or passed to the function. I need to ensure that the image is loaded as a PIL image before passing it to the `object_detection` function. I will correct the code to load the image using PIL and then retry the object detection. ACTION TYPE: code ACTION CONTENT: from PIL import Image from mnm.tool_api import object_detection # Load the image '11561.jpg' using PIL and perform object detection image_path = '/root/input_data/11561.jpg' image = Image.open(image_path) # Perform object detection detection_result = object_detection(image) main_subject = detection_result['objects'][0]['label'] # Assuming the first object is the main subject main_subject -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"Image": , "image": , "detection_result": {'image': , 'objects': [{'bbox': [0.01, 87.25, 251.72, 300.89], 'label': 'boat'}]}, "main_subject": boat} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The main subject of the image has been identified as 'boat'. Now, I need to search for a function that can generate a new image based on a text description. This will likely be an image generation function that takes a text prompt as input. I will search for such a function in the codebase. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: generate image) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: generate image) CODE BLOCKS: --- 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=[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=[348, 375] type=FUNCTION content={ def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): """ Processes an image and a question about the image, then generates an answer using a pre-trained model. Args: image: The image to be processed. question: The question related to the image. ckpt: The checkpoint or model identifier for the pre-trained model. Defaults to MODEL_SELECTION["visual_question_answering"]. Returns: A dictionary with a key 'text' containing the generated answer to the question. Examples: >>> answer = visual_question_answering(my_image, "What is in this image?") >>> print(answer['text']) "A cat sitting on a windowsill." """ ... } --- (26 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 26 classes/functions found in unexpanded results (search again with the same query to see details): def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py def get_full_path_data(filename): ... # mnm/tool_api.py def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The `image_generation` function from the `tool_api.py` file is suitable for generating a new image based on a text description. I will use this function with the text prompt 'boat' to generate a new image that represents the same concept as the main subject identified in the original image. After generating the new image, I will need to search for a function that can provide a written summary of the image. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation # Generate a new image based on the text description of the main subject text_description = 'boat' generated_image_result = image_generation(text_description) generated_image_result -----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": , "text_description": boat, "generated_image_result": {'image': }} ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The new image has been generated successfully. Now, I need to search for a function that can provide a written summary of the newly created image. This is likely to be an image captioning function. I will search for such a function in the codebase. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: image captioning) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: image captioning) 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=[463, 489] type=FUNCTION content={ def optical_character_recognition( image, ckpt=MODEL_SELECTION["optical_character_recognition"] ): """ It takes an image and outputs recognized texts in the image. """ import easyocr if isinstance(image, str): image_path = image if os.path.exists(image) else get_full_path_data(image) else: image = image_processing(image) image_path = os.path.join(MNM_BASE_PATH_DATA, "ocr_temp.jpg") image.save(image_path) assert ckpt == "easyOCR" # only support easyocr for now reader = easyocr.Reader( ["en"] ) # this needs to run only once to load the model into memory result = reader.readtext(image_path) result_text = [ text for _, text, _ in result ] # Extract only the text from the result result_formatted = {"text": ", ".join(result_text)} 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.'} """ ... } --- (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 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 image_processing(img): ... # 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_crop_top(image, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 8 ******************************************************************************** ------Action------ THOUGHT: The search did not return the image captioning function directly, but from the previous search results, I know that there is an `image_captioning` function available in the `tool_api.py` file. I will use this function to generate a written summary of the newly created image. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_captioning # Generate a written summary of the newly created image caption_result = image_captioning(generated_image_result['image']) caption_result -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_captioning": , "caption_result": {'text': 'painting of a boat in the ocean with a rope on the front'}} ******************************************************************************** Step 9 ******************************************************************************** ------Action------ THOUGHT: The written summary of the newly created image has been successfully generated. The task is now complete. I will provide a code summary of the solution. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import object_detection, image_generation, image_captioning image_path = '/root/input_data/11561.jpg' image = Image.open(image_path) detection_result = object_detection(image) main_subject = detection_result['objects'][0]['label'] text_description = main_subject generated_image_result = image_generation(text_description) caption_result = image_captioning(generated_image_result['image']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 10 ******************************************************************************** ------Action------ THOUGHT: The user has acknowledged receipt of the code summary. The task is complete. ACTION TYPE: done ACTION CONTENT: True -----Response----- None