******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I am preparing a kids-friendly learning material. Today's lesson involves the number 6. Could you find a captivating trivia fact about the number 6, produce a vibrant, engaging image representing that fact and write an easy-to-understand caption that quickly explains 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: The task involves three steps: retrieving a trivia fact about the number 6, generating an image representing that fact, and writing a caption for the image. I will start by searching for a function in the `mnm` codebase that can retrieve trivia facts about numbers. ACTION TYPE: search ACTION CONTENT: text: trivia AND (text: number OR text: fact) -----Response----- RESPONSE (Retrieval Result): QUERY: text: trivia AND (text: number OR text: fact) 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=[51, 77] type=FUNCTION content={ def text_processing(file_path): """ preprocess the text file Args: - the path of .doc .docx .txt file Returns: - text with string format Notes: - This function is designed for text input. """ # Check the file extension if file_path.endswith(".txt"): with open(file_path, "r") as file: content = file.read() elif file_path.endswith(".doc") or file_path.endswith(".docx"): # Use textract to extract text from doc and docx files content = textract.process(file_path).decode("utf-8") else: # if the file is not .txt .doc .docx, then it is a string, directly return the stirng return file_path return content } --- file_path=mnm/tool_api.py lines=[985, 1011] type=FUNCTION content={ def wikipedia_simple_search(text: str, ckpt=None): """ Performs a basic search on Wikipedia to retrieve a summary of the most relevant page. Args: text (str): The search query text. ckpt: Unused parameter, can be anything (default is None). Returns: dict: A dictionary containing the formatted text summary of the most relevant Wikipedia page. Raises: This function may raise exceptions related to network issues or JSON decoding if the Wikipedia API changes or is unreachable. Examples: result = wikipedia_simple_search("Python programming language") # result might be something like: # {'text': 'Python is a high-level, interpreted, general-purpose programming language. Its design philosophy emphasizes code readability with its use of significant indentation.'} """ ... } --- (All code blocks matching the query were returned.) ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `get_trivia_fact` from the `mnm` codebase is suitable for retrieving a trivia fact about the number 6. I will use this function to get the trivia fact. After obtaining the fact, I will proceed to generate an image and caption related to the fact. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import get_trivia_fact # Retrieve a trivia fact about the number 6 trivia_fact = get_trivia_fact("6") -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"get_trivia_fact": , "trivia_fact": {'text': 'the number of points on a Star of David', 'number': 6, 'found': True, 'type': 'trivia'}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: We have successfully retrieved a trivia fact about the number 6, which is "the number of points on a Star of David". Next, I will search for a function that can generate an image based on a text description to create a vibrant and engaging image representing this fact. ACTION TYPE: search ACTION CONTENT: text: generate AND text: image AND text: description -----Response----- RESPONSE (Retrieval Result): QUERY: text: generate AND text: image AND 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 } --- (12 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 10 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 image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # 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 5 ******************************************************************************** ------Action------ THOUGHT: The function `image_generation` from the `mnm` codebase can generate an image based on a text description. I will use this function to create an image representing the trivia fact about the number 6. After generating the image, I will use the `image_captioning` function to create a caption for the image. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation, image_captioning # Generate an image based on the trivia fact image_description = "the number of points on a Star of David" generated_image = image_generation(image_description) # Generate a caption for the image caption_result = image_captioning(generated_image['image']) -----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": , "image_captioning": , "image_description": the number of points on a Star of David, "generated_image": {'image': }, "caption_result": {'text': 'there is a star of david on a wall with a black background'}} ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: We have successfully generated an image and a caption for the trivia fact about the number 6. The caption generated is "there is a star of david on a wall with a black background". This completes the user's request. I will now provide a summary of the code used to solve the task. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import get_trivia_fact, image_generation, image_captioning output0 = get_trivia_fact("6") output1 = image_generation("the number of points on a Star of David") output2 = image_captioning(output1['image']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The user has acknowledged the receipt of the code summary. The task is now complete. ACTION TYPE: done ACTION CONTENT: True -----Response----- None