******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: I have this file named '14021.jpg'. Could you take a look and determine what's inside? After identifying, could you create a fun story relating to whatever object or creature is shown in this image? And then, based on the story, answer the question What adventure did the main character of the story go on? 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 contents of the image '14021.jpg'. To do this, I will search for an image understanding function in the codebase that can classify the image. Once I have the classification, I can then look for a text generation function to create a fun story related to the identified object or creature. Finally, I will use a text understanding function to answer the question about the adventure the main character went on in the story. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: image classification) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: image classification) CODE BLOCKS: --- 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'} """ ... } --- file_path=mnm/tool_api.py lines=[165, 176] type=FUNCTION content={ def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): """ Classifies a given text into a category based on the model's vocabulary. Args: text (str): The text to be classified. ckpt: The model checkpoint to use for classification. Defaults to MODEL_SELECTION["text_classification"]. Returns: dict: A dictionary with the key 'text' and the value as the classification label. Examples: >>> text_classification("I love sunny days.") {'text': 'POSITIVE'} """ ... } --- file_path=mnm/tool_api.py lines=[731, 765] type=FUNCTION content={ def tag(image, objects, ckpt=None): """ Tags objects in an image with bounding boxes and class labels. Args: image: The image to be processed and tagged. objects: A list of dictionaries, each containing 'bbox' (bounding box) and 'label' (class) for an object. ckpt (optional): A checkpoint or model state to use for tagging, not used in the current implementation. Returns: A dictionary with a key 'image' containing the tagged image. Examples: # Assuming 'image' is an image object and 'objects' is a list of object dictionaries with 'bbox' and 'label' result = tag(image, objects) tagged_image = result['image'] """ ... } --- (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 object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py def image_generation(text: str, ckpt=MODEL_SELECTION['image_generation']): ... # mnm/tool_api.py def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # 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 image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # 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_classification` from `mnm/tool_api.py` seems suitable for classifying the image '14021.jpg'. I will write code to use this function to classify the image. After obtaining the classification, I will search for a text generation function to create a story based on the classified object or creature. Finally, I will search for a function that can answer questions based on a text context to respond to the query about the adventure the main character went on. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_classification # Load and classify the image image_path = '/root/input_data/14021.jpg' with open(image_path, 'rb') as file: image_data = file.read() classification_result = image_classification(image_data) print(classification_result) -----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 8, in File "/root/mnm/mnm/tool_api.py", line 338, in image_classification 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/vit/image_processing_vit.py", line 233, 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): {"image_classification": , "image_path": /root/input_data/14021.jpg, "file": <_io.BufferedReader name='/root/input_data/14021.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\x01\xf4\x01N\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 error indicates that the image data passed to the `image_classification` function is of type `NoneType`, which is not expected. The function likely expects an image in a specific format, such as a PIL image or a numpy array. I need to adjust the code to load the image correctly before passing it to the `image_classification` function. I will use the `Image` module from PIL to open the image file and then try classifying it again. ACTION TYPE: code ACTION CONTENT: from PIL import Image from mnm.tool_api import image_classification # Load the image using PIL image_path = '/root/input_data/14021.jpg' image = Image.open(image_path) # Classify the image classification_result = image_classification(image) print(classification_result) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': 'gasmask, respirator, gas helmet'} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"Image": , "image": , "classification_result": {'text': 'gasmask, respirator, gas helmet'}} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The image has been classified as 'gasmask, respirator, gas helmet'. Now, I will search for a text generation function to create a fun story related to this object. After generating the story, I will search for a function that can answer questions based on a text context to find out what adventure the main character of the story went on. 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_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The function `text_generation` from `mnm/tool_api.py` can be used to generate a continuation of a given input text. I will use this function to create a fun story related to the classified object 'gasmask, respirator, gas helmet'. After generating the story, I will search for the `question_answering` function to answer the question about the adventure the main character of the story went on. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import text_generation # Generate a fun story related to the classified object 'gasmask, respirator, gas helmet' prompt = "In a post-apocalyptic world, a lone survivor finds an old gasmask. This gasmask isn't ordinary; it has a story to tell. The survivor puts it on and suddenly, " story_result = text_generation(prompt) print(story_result) -----Response----- RESPONSE (Execution Result): STDOUT (STDOUT was too long. Showing only the start and end separated by ellipsis.): {'text': "their vision is filled with memories of the previous owner. They see flashes of a bustling city before it was destroyed, a loving family that was torn apart, and the desperate struggle for survival in a world ravaged by chaos.\n\nThe survivor is overwhelmed by the intensity of the memories, feeling as though they are living through the experiences of the previous owner. They see the horrors of the apocalypse through their eyes, feel the pain of loss, and the relentless fight for survival.\n\nAs the memories fade, the survivor is left with a newfound understanding of the world they now inhabit. They realize that despite the chaos and destruction, there is still hope for a better future. Armed with the knowledge and experiences of the gasmask's previous owner, the survivor sets out on a journey to rebuild and restore what was lost.\n\nThe gasmask becomes a symbol of resilience and determination, a reminder of the strength and endurance of the human spirit in the face of adversi ... t was torn apart, and the desperate struggle for survival in a world ravaged by chaos.\n\nThe survivor is overwhelmed by the intensity of the memories, feeling as though they are living through the experiences of the previous owner. They see the horrors of the apocalypse through their eyes, feel the pain of loss, and the relentless fight for survival.\n\nAs the memories fade, the survivor is left with a newfound understanding of the world they now inhabit. They realize that despite the chaos and destruction, there is still hope for a better future. Armed with the knowledge and experiences of the gasmask's previous owner, the survivor sets out on a journey to rebuild and restore what was lost.\n\nThe gasmask becomes a symbol of resilience and determination, a reminder of the strength and endurance of the human spirit in the face of adversity. And as the survivor continues their journey, they carry the memories of the past with them, using them to pave the way for a brighter tomorrow."} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"text_generation": , "prompt": In a post-apocalyptic world, a lone survivor finds an old gasmask. This gasmask isn't ordinary; it has a story to tell. The survivor puts it on and suddenly, , "story_result": {'text': "their vision is filled with memories of the previous owner. They see flashes of a bustling city before it was destroyed, a loving family that was torn apart, and the desperate struggle for survival in a world ravaged by chaos.\n\nThe survivor is overwhelmed by the intensity of the memories, feeling as though they are living through the experiences of the previous owner. They see the horrors of the apocalypse through their eyes, feel the pain of loss, and the relentless fight for surviv...} ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The story has been successfully generated. Now, I will search for the `question_answering` function to answer the question about the adventure the main character of the story went on. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: question answering) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: question answering) CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[178, 193] type=FUNCTION content={ def question_answering( question: str, text: str, ckpt=MODEL_SELECTION["question_answering"] ): # alternative: "deepset/roberta-base-squad2" """ It takes a text and a question, and outputs an answer to that question based on the text. """ question = text_processing(question) text = text_processing(text) pipe = pipeline("question-answering", model=ckpt) result_raw = pipe( question=question, context=text ) # {'score': 0.01082150824368, 'start': 0, 'end': 10, 'answer': 'My name is'} result_formatted = {"text": result_raw["answer"]} 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." """ ... } --- file_path=mnm/tool_api.py lines=[879, 893] type=FUNCTION content={ def get_math_fact(number: str, ckpt=None): """ It provides interesting math facts about numbers. """ url = f"https://numbersapi.p.rapidapi.com/{number}/math" 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 } --- (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 get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py def get_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py def get_year_fact(year: str, ckpt=None): ... # mnm/tool_api.py def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py def text_processing(file_path): ... # mnm/tool_api.py def get_location(city: str, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 8 ******************************************************************************** ------Action------ THOUGHT: The function `question_answering` from `mnm/tool_api.py` is suitable for answering the question based on the generated story text. I will write code to use this function to answer the question "What adventure did the main character of the story go on?" using the story text as context. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import question_answering # The generated story text story_text = story_result['text'] # The question about the main character's adventure question = "What adventure did the main character of the story go on?" # Answer the question based on the story text adventure_result = question_answering(question, story_text) print(adventure_result) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': 'journey to rebuild and restore what was lost'} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"question_answering": , "story_text": their vision is filled with memories of the previous owner. They see flashes of a bustling city before it was destroyed, a loving family that was torn apart, and the desperate struggle for survival in a world ravaged by chaos. The survivor is overwhelmed by the intensity of the memories, feeling as though they are living through the experiences of the previous owner. They see the horrors of the apocalypse through their eyes, feel the pain of loss, and the relentless fight for survival. As the ..., "question": What adventure did the main character of the story go on?, "adventure_result": {'text': 'journey to rebuild and restore what was lost'}} ******************************************************************************** Step 9 ******************************************************************************** ------Action------ THOUGHT: The question has been answered successfully. The main character of the story went on a "journey to rebuild and restore what was lost." 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 image_classification, text_generation, question_answering # Load the image using PIL image_path = '/root/input_data/14021.jpg' image = Image.open(image_path) # Classify the image classification_result = image_classification(image) # Generate a fun story related to the classified object 'gasmask, respirator, gas helmet' prompt = "In a post-apocalyptic world, a lone survivor finds an old gasmask. This gasmask isn't ordinary; it has a story to tell. The survivor puts it on and suddenly, " story_result = text_generation(prompt) # The question about the main character's adventure question = "What adventure did the main character of the story go on?" # Answer the question based on the story text adventure_result = question_answering(question, story_result['text']) -----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