******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: I'm working on a project based on a 'School programme aimed at creating a generation of high-tech entrepreneurs.' Need to create a brochure cover for it. Can we generate a write-up that captures the spirit of the program, then create a vibrant image to represent it? After that, can we edit the image - add a group of diverse young people working together on a high-tech project please, to personify the target group? 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 generate a write-up that encapsulates the essence of the 'School programme aimed at creating a generation of high-tech entrepreneurs.' For this, I will search for a text generation function in the `mnm` codebase that can help me create the write-up. Once I have the write-up, I will proceed to generate a vibrant image to represent it and then edit the image to add a group of diverse young people working on a high-tech project. 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 } --- (34 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 34 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_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The `text_generation` function from the `mnm` codebase can be used to generate the write-up for the brochure cover. I will use this function to create a text prompt that reflects the spirit of the 'School programme aimed at creating a generation of high-tech entrepreneurs.' After generating the text, I will use the `image_generation` function to create a vibrant image based on the generated text. Once the image is generated, I will search for a function that can edit the image to add a group of diverse young people working together on a high-tech project. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import text_generation, image_generation # Generate a write-up for the brochure cover prompt = "Create a generation of high-tech entrepreneurs through a dedicated school programme that fosters innovation, collaboration, and technological expertise among young students." generated_text = text_generation(text=prompt) # Generate a vibrant image to represent the write-up generated_image = image_generation(text=generated_text['text']) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': "Our dedicated school programme, TechGenius Academy, aims to cultivate the next generation of high-tech entrepreneurs by providing students with the skills, resources, and opportunities they need to succeed in the rapidly evolving world of technology.\n\nThrough a curriculum that integrates STEM education with entrepreneurship principles, students will learn how to develop and implement innovative solutions to real-world problems. They will have access to state-of-the-art technology and tools, and will be encouraged to think creatively and work collaboratively with their peers.\n\nAt TechGenius Academy, students will also have the opportunity to participate in internships, mentorship programs, and networking events with industry professionals, giving them valuable hands-on experience and exposure to the world of entrepreneurship.\n\nBy fostering a supportive and empowering environment for young innovators, we aim to inspire and equip students to become the leaders and pioneers of tomorrow's technology-driven economy. Join us at TechGenius Academy and become part of a community that is shaping the future of innovation and entrepreneurship."} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"text_generation": , "image_generation": , "prompt": Create a generation of high-tech entrepreneurs through a dedicated school programme that fosters innovation, collaboration, and technological expertise among young students., "generated_text": {'text': "Our dedicated school programme, TechGenius Academy, aims to cultivate the next generation of high-tech entrepreneurs by providing students with the skills, resources, and opportunities they need to succeed in the rapidly evolving world of technology.\n\nThrough a curriculum that integrates STEM education with entrepreneurship principles, students will learn how to develop and implement innovative solutions to real-world problems. They will have access to state-of-the-art technology and..., "generated_image": {'image': }} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The write-up has been successfully generated and captures the spirit of the 'School programme aimed at creating a generation of high-tech entrepreneurs.' Now, I will proceed to search for a function that can edit the generated image to add a group of diverse young people working together on a high-tech project, as requested by the user. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: edit image) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: edit image) 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=[463, 489] type=FUNCTION content={ def optical_character_recognition( image, ckpt=MODEL_SELECTION["optical_character_recognition"] ): """ It takes an image and outputs recognized texts in the image. """ import easyocr if isinstance(image, str): image_path = image if os.path.exists(image) else get_full_path_data(image) else: image = image_processing(image) image_path = os.path.join(MNM_BASE_PATH_DATA, "ocr_temp.jpg") image.save(image_path) assert ckpt == "easyOCR" # only support easyocr for now reader = easyocr.Reader( ["en"] ) # this needs to run only once to load the model into memory result = reader.readtext(image_path) result_text = [ text for _, text, _ in result ] # Extract only the text from the result result_formatted = {"text": ", ".join(result_text)} return result_formatted } --- (20 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 20 classes/functions found in unexpanded results (search again with the same query to see details): 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 def image_crop_right(image, ckpt=None): ... # mnm/tool_api.py def image_processing(img): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The `image_editing` function from the `mnm` codebase can be used to edit the generated image to add a group of diverse young people working together on a high-tech project. I will use this function with an appropriate prompt to achieve the desired edit. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_editing # Edit the generated image to add a group of diverse young people working together on a high-tech project edit_prompt = "Add a group of diverse young people working together on a high-tech project to the image." edited_image = image_editing(image=generated_image['image'], prompt=edit_prompt) -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_editing": , "edit_prompt": Add a group of diverse young people working together on a high-tech project to the image., "edited_image": {'image': }} ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The image has been successfully edited to include a group of diverse young people working together on a high-tech project. The task is now complete, and I will provide a summary of the code used to solve the user's query. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import text_generation, image_generation, image_editing output0 = text_generation(text="Create a generation of high-tech entrepreneurs through a dedicated school programme that fosters innovation, collaboration, and technological expertise among young students.") output1 = image_generation(text=output0['text']) output2 = image_editing(image=output1['image'], prompt="Add a group of diverse young people working together on a high-tech project to the 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 complete. ACTION TYPE: done ACTION CONTENT: True -----Response----- None