******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: My cousin shared a funny idea with me the other day. She has this image 270650-input.png of her car and jokingly asked me to make it look like the car is ready to go skiing. Could you help by putting skis on the wheels of the car in the image? Once done, could you depict the outcome amusingly and then present it to me in a short and concise phrase to make my cousin laugh? 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 in the `mnm` codebase that can edit images based on a description. This function should be able to understand the request to add skis to the wheels of the car. After editing the image, I will need to find a function that can generate a short and amusing phrase to describe the edited image. I will start by searching for the image editing function. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: edit images) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: edit images) 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=[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=[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 } --- (23 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 23 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_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 image_crop_left(image, ckpt=None): ... # mnm/tool_api.py def image_crop_top(image, ckpt=None): ... # mnm/tool_api.py def image_crop_bottom(image, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The `image_editing` function from the `mnm` codebase seems to be the right choice for editing the image based on a text prompt. I will use this function to add skis to the wheels of the car. After editing the image, I will need to find a function that can generate a short and amusing phrase. I will search for a text generation function next. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: generate text) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: generate text) 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=[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...'} """ ... } --- (21 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 21 classes/functions found in unexpanded results (search again with the same query to see details): def get_full_path_data(filename): ... # mnm/tool_api.py def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # 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 wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The `text_generation` function from the `mnm` codebase can be used to generate a continuation of the given input text. This function seems suitable for creating a short and amusing phrase to describe the edited image. Now, I will write the code to first edit the image by adding skis to the wheels of the car using the `image_editing` function and then generate a funny phrase using the `text_generation` function. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_editing, text_generation # Path to the input image image_path = '/root/input_data/270650-input.png' # Edit the image to add skis to the wheels of the car edited_image_result = image_editing(image_path, "Add skis to the wheels of the car.") # Check if the image was edited successfully if 'image' in edited_image_result: edited_image = edited_image_result['image'] # Generate a short and amusing phrase to describe the edited image funny_phrase_result = text_generation("A car with skis ready to hit the snowy slopes.") if 'text' in funny_phrase_result: funny_phrase = funny_phrase_result['text'] print(funny_phrase) else: print("Failed to generate a funny phrase.") else: print("Failed to edit the image.") -----Response----- RESPONSE (Execution Result): STDOUT (STDOUT was too long. Showing only the start and end separated by ellipsis.): {'text': "The sleek black car was equipped with a custom ski rack on the roof, holding two pairs of shiny red skis. The driver, clad in a warm winter jacket and matching ski pants, could hardly contain their excitement as they approached the snowy slopes. With the car's four-wheel drive and all-weather tires, they were confident they could tackle even the most treacherous terrain.\n\nAs they reached the base of the mountain, the driver quickly unloaded the skis from the rack and clicked into their bindings. With a quick glance at the map of the trails, they set off, navigating the twists and turns with ease. The car may have brought them to the mountain, but it was the skis that would take them on a thrilling ride through the powder-covered slopes.\n\nThe car stood empty at the base of the mountain, patiently waiting for its owner to return, its engine rumbling softly in the cold winter air. And as the skier raced down the slopes, their laughter echoing through the snowy landscape, the ... f shiny red skis. The driver, clad in a warm winter jacket and matching ski pants, could hardly contain their excitement as they approached the snowy slopes. With the car's four-wheel drive and all-weather tires, they were confident they could tackle even the most treacherous terrain. As they reached the base of the mountain, the driver quickly unloaded the skis from the rack and clicked into their bindings. With a quick glance at the map of the trails, they set off, navigating the twists and turns with ease. The car may have brought them to the mountain, but it was the skis that would take them on a thrilling ride through the powder-covered slopes. The car stood empty at the base of the mountain, patiently waiting for its owner to return, its engine rumbling softly in the cold winter air. And as the skier raced down the slopes, their laughter echoing through the snowy landscape, the car sat silently, a silent witness to the joy and freedom that only a day on the slopes could bring. RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_editing": , "text_generation": , "image_path": /root/input_data/270650-input.png, "edited_image_result": {'image': }, "edited_image": , "funny_phrase_result": {'text': "The sleek black car was equipped with a custom ski rack on the roof, holding two pairs of shiny red skis. The driver, clad in a warm winter jacket and matching ski pants, could hardly contain their excitement as they approached the snowy slopes. With the car's four-wheel drive and all-weather tires, they were confident they could tackle even the most treacherous terrain.\n\nAs they reached the base of the mountain, the driver quickly unloaded the skis from the rack and clicked into the..., "funny_phrase": The sleek black car was equipped with a custom ski rack on the roof, holding two pairs of shiny red skis. The driver, clad in a warm winter jacket and matching ski pants, could hardly contain their excitement as they approached the snowy slopes. With the car's four-wheel drive and all-weather tires, they were confident they could tackle even the most treacherous terrain. As they reached the base of the mountain, the driver quickly unloaded the skis from the rack and clicked into their bindings....} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The image has been successfully edited, and a lengthy text has been generated. However, the generated text is too long for the purpose of creating a short and amusing phrase. I will need to refine the prompt for the `text_generation` function to ensure that the output is a concise phrase suitable for making the user's cousin laugh. I will adjust the prompt and call the `text_generation` function again. ACTION TYPE: code ACTION CONTENT: # Generate a shorter and more amusing phrase to describe the edited image funny_phrase_result = text_generation("Look at this car, ready to ski down the highway!") # Extract the funny phrase if generation was successful if 'text' in funny_phrase_result: funny_phrase = funny_phrase_result['text'] print(funny_phrase) else: print("Failed to generate a funny phrase.") -----Response----- RESPONSE (Execution Result): STDOUT: {'text': 'Sorry, I am unable to view or analyze images. Is there anything else I can help you with?'} Sorry, I am unable to view or analyze images. Is there anything else I can help you with? RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"funny_phrase_result": {'text': 'Sorry, I am unable to view or analyze images. Is there anything else I can help you with?'}, "funny_phrase": Sorry, I am unable to view or analyze images. Is there anything else I can help you with?} ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The text generation function did not produce the desired amusing phrase. It seems to have returned a default response. I will attempt to refine the prompt further to generate a concise and humorous phrase that fits the context of the edited image. ACTION TYPE: code ACTION CONTENT: # Attempt to generate a concise and humorous phrase again with a more specific prompt funny_phrase_result = text_generation("Caption for a car with skis instead of wheels.") # Extract the funny phrase if generation was successful if 'text' in funny_phrase_result: funny_phrase = funny_phrase_result['text'] print(funny_phrase) else: print("Failed to generate a funny phrase.") -----Response----- RESPONSE (Execution Result): STDOUT: {'text': '"Ready to hit the slopes... on the road!"'} "Ready to hit the slopes... on the road!" RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"funny_phrase_result": {'text': '"Ready to hit the slopes... on the road!"'}, "funny_phrase": "Ready to hit the slopes... on the road!"} ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The text generation function has successfully produced a concise and humorous phrase: "Ready to hit the slopes... on the road!" This phrase fits the context of the edited image and should make the user's cousin laugh. The task is now complete. ACTION TYPE: done ACTION CONTENT: True -----Response----- None ******************************************************************************** Step 8 ******************************************************************************** ------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 9 ******************************************************************************** ------Action------ THOUGHT: The user has requested a code summary of the actions taken to edit the image and generate a humorous phrase. I will summarize the code that was used to edit the image with skis and to generate the amusing caption. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import image_editing, text_generation output0 = image_editing(image='/root/input_data/270650-input.png', prompt="Add skis to the wheels of the car.") output1 = text_generation(text="Caption for a car with skis instead of wheels.") -----Response----- None