******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I want to know something unique about the number 9. After that, please generate an image that could illustrate this trivia fact. Subsequently, I'd like to add a sun in the top right corner of the image. Can you use the input Add a sun in the top right corner for adjusting the image?" 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 retrieve a unique fact about the number 9. I will search for a function related to retrieving facts about numbers. ACTION TYPE: search ACTION CONTENT: text: (unique OR trivia OR fact) AND (text: number) -----Response----- RESPONSE (Retrieval Result): QUERY: text: (unique OR trivia OR fact) AND (text: number) CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[895, 909] type=FUNCTION content={ def get_trivia_fact(number: str, ckpt=None): """ It provides interesting trivia facts about number. """ url = f"https://numbersapi.p.rapidapi.com/{number}/trivia" 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 } --- 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 } --- file_path=mnm/tool_api.py lines=[863, 877] type=FUNCTION content={ def get_year_fact(year: str, ckpt=None): """ It provides interesting facts about years. """ url = f"https://numbersapi.p.rapidapi.com/{year}/year" 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 } --- (7 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 5 out of 5 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 image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `get_trivia_fact` from `tool_api.py` seems suitable for retrieving a unique fact about the number 9. I will write code to call this function with the number 9 as an argument. After obtaining the trivia fact, I will proceed to generate an image illustrating this fact using the appropriate function from the `mnm` codebase. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import get_trivia_fact # Retrieve a unique fact about the number 9 trivia_fact = get_trivia_fact('9') print(trivia_fact) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': 'the number of innings in a regulation, non-tied game of baseball', 'number': 9, 'found': True, 'type': 'trivia'} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"get_trivia_fact": , "trivia_fact": {'text': 'the number of innings in a regulation, non-tied game of baseball', 'number': 9, 'found': True, 'type': 'trivia'}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: Now that we have the trivia fact about the number 9, the next step is to generate an image that illustrates this fact. I will search for a function in the `mnm` codebase that can generate images based on a text description. ACTION TYPE: search ACTION CONTENT: text: generate AND (text: image OR text: illustration) AND (text: text OR text: description) -----Response----- RESPONSE (Retrieval Result): QUERY: text: generate AND (text: image OR text: illustration) AND (text: text 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=[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=[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 } --- (17 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 15 classes/functions found in unexpanded results (search again with the same query to see details): def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The function `image_generation` from `tool_api.py` is suitable for generating an image based on a text description. I will write code to call this function with the trivia fact about the number 9 as an argument to generate an image illustrating the fact. After generating the image, I will use the `image_editing` function to add a sun in the top right corner of the image, as requested by the user. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation, image_editing # Generate an image that matches the trivia fact about the number 9 description = trivia_fact['text'] generated_image = image_generation(description) # Edit the generated image to add a sun in the top right corner edit_prompt = "Add a sun in the top right corner" edited_image = image_editing(generated_image['image'], edit_prompt) # Output the edited image print(edited_image) -----Response----- RESPONSE (Execution Result): STDOUT: {'image': } RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_generation": , "image_editing": , "description": the number of innings in a regulation, non-tied game of baseball, "generated_image": {'image': }, "edit_prompt": Add a sun in the top right corner, "edited_image": {'image': }} ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The image has been successfully generated and edited with a sun in the top right corner. The task is now complete. ACTION TYPE: done ACTION CONTENT: True -----Response----- None ******************************************************************************** Step 7 ******************************************************************************** ------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 8 ******************************************************************************** ------Action------ THOUGHT: I will summarize the code that was used to retrieve a trivia fact about the number 9, generate an image based on that fact, and then edit the image to add a sun in the top right corner. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import get_trivia_fact, image_generation, image_editing output0 = get_trivia_fact('9') output1 = image_generation(output0['text']) output2 = image_editing(output1['image'], "Add a sun in the top right corner") -----Response----- None