******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: I'd like to better visualize the steps for baking something under the conditions of 'Bake 375 for 10 minutes then turn down to 350 degrees for 45 minutes.' Can you generate a step-by-step guide or tips for doing this to ensure it's done correctly? Based on that guide, can you answer this question: What is the total baking time? Finally, could you also provide a visual representation of the procedure based on the generated guide? 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 search for a function that can help generate a step-by-step guide or tips for a baking procedure. This might be related to text generation or summarization functions in the tool_api.py file. I will search for functions that could be used for generating text instructions. After generating the guide, I will calculate the total baking time by summing the individual times mentioned in the steps. Lastly, I will look for a function that can provide a visual representation of the procedure, which might be related to image editing functions. ACTION TYPE: search ACTION CONTENT: text: generate AND (text: guide OR text: steps OR text: tips) text: visual AND (text: representation OR text: image) -----Response----- RESPONSE (Retrieval Result): QUERY: text: generate AND (text: guide OR text: steps OR text: tips) CODE BLOCKS: --- 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=[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=[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") """ ... } --- (5 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 3 out of 3 classes/functions found in unexpanded results (search again with the same query to see details): def get_date_fact(date: str, ckpt=None): ... # mnm/tool_api.py def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py QUERY: text: visual AND (text: representation OR text: image) CODE BLOCKS: --- 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=[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/config.py lines=[7, 22] type=ASSIGNMENT content={ MODEL_SELECTION = {'text_generation': 'gpt-3.5-turbo-0125', 'text_summarization': 'facebook/bart-large-cnn', 'text_classification': 'distilbert-base-uncased-finetuned-sst-2-english', 'question_answering': 'deepset/roberta-base-squad2', 'automatic_speech_recognition': 'openai/whisper-large-v2', 'image_generation': 'stabilityai/stable-diffusion-xl-base-1.0', 'image_captioning': 'Salesforce/blip-image-captioning-large', 'image_editing': 'stabilityai/stable-diffusion-xl-refiner-1.0', 'image_classification': 'google/vit-base-patch16-224', 'visual_question_answering': 'Salesforce/blip-vqa-base', 'object_detection': 'facebook/detr-resnet-101', 'image_segmentation': 'facebook/maskformer-swin-base-coco', 'optical_character_recognition': 'easyOCR'} } --- (All code blocks matching the query were returned.) ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The search results returned a function `text_generation` that can be used to generate a step-by-step guide based on the input text. I will use this function to generate the guide for the baking procedure. After generating the guide, I will manually calculate the total baking time based on the steps provided. For the visual representation, the `image_editing` function seems suitable to create an image based on a text prompt. I will proceed with generating the guide first and then calculate the total baking time. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import text_generation # Generate a step-by-step guide for the baking procedure baking_instructions = "Bake 375 for 10 minutes then turn down to 350 degrees for 45 minutes." guide = text_generation(baking_instructions) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': 'Preheat your oven to 375 degrees Fahrenheit. Place your baked goods on a baking sheet or in a baking dish and bake at 375 degrees for 10 minutes. After the initial 10 minutes, reduce the oven temperature to 350 degrees Fahrenheit and continue baking for an additional 45 minutes, or until the baked goods are cooked through and golden brown on top. Enjoy your delicious treats!'} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"text_generation": , "baking_instructions": Bake 375 for 10 minutes then turn down to 350 degrees for 45 minutes., "guide": {'text': 'Preheat your oven to 375 degrees Fahrenheit. Place your baked goods on a baking sheet or in a baking dish and bake at 375 degrees for 10 minutes. After the initial 10 minutes, reduce the oven temperature to 350 degrees Fahrenheit and continue baking for an additional 45 minutes, or until the baked goods are cooked through and golden brown on top. Enjoy your delicious treats!'}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The guide has been successfully generated. Now, I will calculate the total baking time by adding the initial 10 minutes at 375 degrees Fahrenheit to the additional 45 minutes at 350 degrees Fahrenheit. After that, I will use the `image_editing` function to create a visual representation of the procedure based on the generated guide. ACTION TYPE: code ACTION CONTENT: # Calculate the total baking time initial_baking_time = 10 # 10 minutes at 375 degrees additional_baking_time = 45 # 45 minutes at 350 degrees total_baking_time = initial_baking_time + additional_baking_time # Generate a visual representation of the baking procedure from mnm.tool_api import image_editing # Use the generated guide text to create a visual representation visual_guide = image_editing(None, guide['text']) -----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 10, in File "/root/mnm/mnm/tool_api.py", line 293, in image_editing result = pipe(prompt, image=init_image).images[0] File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py", line 1221, in __call__ image = self.image_processor.preprocess(image) File "/opt/conda/lib/python3.10/site-packages/diffusers/image_processor.py", line 499, in preprocess f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support {', '.join(supported_formats)}" TypeError: 'NoneType' object is not iterable RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"initial_baking_time": 10, "additional_baking_time": 45, "total_baking_time": 55, "image_editing": } ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The code execution resulted in an error because the `image_editing` function expects an image as the first argument, and I passed `None`. To resolve this, I will search for a function that can generate an image from text without requiring an initial image. This function is likely to be related to image generation rather than image editing. ACTION TYPE: search ACTION CONTENT: text: generate AND (text: image OR text: visual) AND NOT (text: editing) -----Response----- RESPONSE (Retrieval Result): QUERY: text: generate AND (text: image OR text: visual) AND NOT (text: editing) CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[911, 929] type=FUNCTION content={ def love_calculator(first_name: str, second_name: str, ckpt=None): """ Enter your name and the name of your partner/lover/crush to find Love compatibility & chances of successful love relationship. """ url = "https://love-calculator.p.rapidapi.com/getPercentage" params = {"fname": first_name, "sname": second_name} headers = { "X-RapidAPI-Key": RAPID_API_KEY, "X-RapidAPI-Host": "love-calculator.p.rapidapi.com", } response = requests.get(url, headers=headers, params=params) result_formatted = response.json() result_formatted = { "number": result_formatted["percentage"], "message": result_formatted["result"], } return result_formatted } --- (All code blocks matching the query were returned.) ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The search did not return a function for generating an image from text. It seems I may have used the wrong keywords in my search query. I will attempt another search with a broader scope, possibly including keywords like 'image generation' or 'text description', to find the appropriate function for generating an image based on text description. ACTION TYPE: search ACTION CONTENT: text: image AND (text: generation OR text: description) -----Response----- RESPONSE (Retrieval Result): QUERY: text: image AND (text: generation OR text: 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=[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'} """ ... } --- (10 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 9 classes/functions found in unexpanded results (search again with the same query to see details): 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_processing(img): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py def get_location(city: str, ckpt=None): ... # mnm/tool_api.py def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The search results returned a function `image_generation` that can be used to generate an image from a text description. I will use this function to create a visual representation of the baking procedure based on the guide text that was generated earlier. I will also provide the total baking time calculated previously. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation # Use the guide text to generate a visual representation of the baking procedure visual_representation = image_generation(guide['text']) -----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": , "visual_representation": {'image': }} ******************************************************************************** Step 8 ******************************************************************************** ------Action------ THOUGHT: The visual representation of the baking procedure has been successfully generated. I will now summarize the solution, including the total baking time and the steps to generate the visual guide. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import text_generation, image_generation guide = text_generation("Bake 375 for 10 minutes then turn down to 350 degrees for 45 minutes.") total_baking_time = 10 + 45 # Total baking time in minutes visual_representation = image_generation(guide['text']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 9 ******************************************************************************** ------Action------ THOUGHT: The user has acknowledged the receipt of the summary. The task is complete. ACTION TYPE: done ACTION CONTENT: True -----Response----- None