******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I have a picture 2350057.jpg, Can you determine what is on the fence in this picture? After recognizing the object, could you create a fictional backstory that includes the object on the fence in it, and finally, sum it up in a few sentences for a quick look?” 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 detect objects in an image and provide labels for them. This will help me identify what is on the fence in the picture. Once I have the object identified, I can search for a function that generates a fictional backstory including the object. Finally, I will need to find a function that can summarize the backstory into a few sentences. I will start by searching for the object detection function in the codebase. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: detecting objects) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: detecting 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=[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'}} """ ... } --- 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 } --- (35 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 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 image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def object_processing(file_path): ... # mnm/tool_api.py def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # 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 labels and bounding box coordinates. I will use this function to identify the object on the fence. After that, I will need to search for a function that can generate a fictional backstory and then another function to summarize the text. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import object_detection # Load the image image_path = '/root/input_data/2350057.jpg' with open(image_path, 'rb') as file: image_data = file.read() # Detect objects in the image detected_objects = object_detection(image_data) print(detected_objects['objects']) -----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/2350057.jpg, "file": <_io.BufferedReader name='/root/input_data/2350057.jpg'>, "image_data": b'\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x01\x00H\x00H\x00\x00\xff\xe2\x0cXICC_PROFILE\x00\x01\x01\x00\x00\x0cHLino\x02\x10\x00\x00mntrRGB XYZ \x07\xce\x00\x02\x00\t\x00\x06\x001\x00\x00acspMSFT\x00\x00\x00\x00IEC sRGB\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xf6\xd6\x00\x01\x00\x00\x00\x00\xd3-HP \x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x0...} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The error indicates that the `object_detection` function expects an image in a specific format (PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor, or jax.ndarray), but it received a NoneType, which means the image data was not loaded correctly. I need to load the image using an appropriate library that can provide the image in one of the expected formats. I will use the PIL library to load the image as a PIL.Image.Image object 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 using PIL image_path = '/root/input_data/2350057.jpg' image = Image.open(image_path) # Detect objects in the image detected_objects = object_detection(image) print(detected_objects['objects']) -----Response----- RESPONSE (Execution Result): STDOUT: [{'bbox': [63.3, 186.51, 211.28, 326.71], 'label': 'elephant'}, {'bbox': [62.97, 153.43, 236.54, 318.24], 'label': 'elephant'}, {'bbox': [87.23, 11.97, 338.99, 267.92], 'label': 'elephant'}, {'bbox': [78.13, 154.16, 235.45, 271.04], 'label': 'elephant'}] RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"Image": , "image": , "detected_objects": {'image': , 'objects': [{'bbox': [63.3, 186.51, 211.28, 326.71], 'label': 'elephant'}, {'bbox': [62.97, 153.43, 236.54, 318.24], 'label': 'elephant'}, {'bbox': [87.23, 11.97, 338.99, 267.92], 'label': 'elephant'}, {'bbox': [78.13, 154.16, 235.45, 271.04], 'label': 'elephant'}]}} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The object detection function has successfully identified multiple elephants in the image. Since the user asked about an object on the fence, and given that multiple detections are of elephants, it's reasonable to assume that the object on the fence is an elephant. Now, I need to find a function that can generate a fictional backstory involving an elephant. I will search for a text generation function in the codebase. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: text generation) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: text generation) CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[111, 141] type=FUNCTION content={ def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): """ Generates a continuation of the given input text using a specified language model. Args: text (str): The input text prompt to be continued. ckpt (str, optional): The identifier for the language model to be used. Defaults to the model specified in MODEL_SELECTION["text_generation"]. Returns: dict: A dictionary containing the generated text under the key 'text'. Raises: KeyError: If the OPENAI_API_KEY environment variable is not set. APIError: If the OpenAI API encounters an issue with the request. Examples: >>> generated_text = text_generation("Once upon a time") >>> print(generated_text) {'text': '...continued text from the model...'} """ ... } --- 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=[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 } --- (31 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 31 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 visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py def get_full_path_data(filename): ... # mnm/tool_api.py def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The `text_generation` function from the `tool_api.py` file is suitable for generating a fictional backstory involving the detected object, which is an elephant. After generating the backstory, I will need to summarize it. The search results also show a `text_summarization` function, which I can use for this purpose. I will now write code to generate the backstory using the `text_generation` function and then summarize it using the `text_summarization` function. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import text_generation, text_summarization # Generate a fictional backstory involving an elephant prompt = "Once upon a time, an elephant was sitting on a fence" backstory = text_generation(prompt) print("Generated Backstory:", backstory['text']) # Summarize the backstory summary = text_summarization(backstory['text']) print("Summary:", summary['text']) -----Response----- RESPONSE (Execution Result): STDOUT (STDOUT was too long. Showing only the start and end separated by ellipsis.): {'text': ' in a lush grassy field. The elephant was deep in thought, pondering the vastness of the world and his place in it. He was a wise old elephant, with wrinkles in his skin that told the stories of his long life.\n\nAs he sat there, a group of curious animals approached, intrigued by the sight of the elephant perched on the fence. The animals gathered around, watching in awe as the elephant sat there, deep in contemplation.\n\nOne brave little rabbit finally spoke up, "Excuse me, Mr. Elephant, why are you sitting on the fence? It seems like a strange place for an elephant to be."\n\nThe elephant smiled kindly at the rabbit and replied, "I am sitting on this fence to see the world from a different perspective. Sometimes, it is good to step out of our comfort zones and look at things from a new angle. It helps us to understand the world in a different way."\n\nThe other animals nodded in agreement, understanding the wisdom in the elephant\'s words. They thanked him for his insight ... elephant to be." The elephant smiled kindly at the rabbit and replied, "I am sitting on this fence to see the world from a different perspective. Sometimes, it is good to step out of our comfort zones and look at things from a new angle. It helps us to understand the world in a different way." The other animals nodded in agreement, understanding the wisdom in the elephant's words. They thanked him for his insight and went on their way, leaving the elephant to continue his contemplation. And so, the wise old elephant sat on the fence, watching the world go by with a newfound sense of wonder and understanding. Sometimes, it truly does take a different perspective to see things in a new light. Summary: The elephant was deep in thought, pondering the vastness of the world and his place in it. A group of curious animals approached, intrigued by the sight of the elephant perched on the fence. The elephant replied, "I am sitting on this fence to see the world from a different perspective" RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"text_generation": , "text_summarization": , "prompt": Once upon a time, an elephant was sitting on a fence, "backstory": {'text': ' in a lush grassy field. The elephant was deep in thought, pondering the vastness of the world and his place in it. He was a wise old elephant, with wrinkles in his skin that told the stories of his long life.\n\nAs he sat there, a group of curious animals approached, intrigued by the sight of the elephant perched on the fence. The animals gathered around, watching in awe as the elephant sat there, deep in contemplation.\n\nOne brave little rabbit finally spoke up, "Excuse me, Mr. Elep..., "summary": {'text': 'The elephant was deep in thought, pondering the vastness of the world and his place in it. A group of curious animals approached, intrigued by the sight of the elephant perched on the fence. The elephant replied, "I am sitting on this fence to see the world from a different perspective"'}} ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The fictional backstory has been successfully generated and summarized. The summary provides a concise version of the story involving the elephant on the fence. I will now provide a code summary of the solution. ACTION TYPE: code_summary ACTION CONTENT: from PIL import Image from mnm.tool_api import object_detection, text_generation, text_summarization image = Image.open('/root/input_data/2350057.jpg') detected_objects = object_detection(image) prompt = "Once upon a time, an elephant was sitting on a fence" backstory = text_generation(prompt) summary = text_summarization(backstory['text']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 8 ******************************************************************************** ------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