******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I have this photo 2323530.jpg. Could you describe what's happening in the photo, create a new related image from the description, and finally, read any text or captions that appear in this newly generated 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 that can describe what's happening in the photo. This is likely an image captioning function. I will search for this function in the codebase. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: image captioning) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: image captioning) 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=[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=[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 } --- (36 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 36 classes/functions found in unexpanded results (search again with the same query to see details): def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py 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 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 object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py def image_processing(img): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `image_captioning` from `tool_api.py` can be used to describe what's happening in the photo. Next, I will use the `image_generation` function to create a new related image from the description. Finally, I will use the `optical_character_recognition` function to read any text or captions that appear in the newly generated image. I will now write the code to perform these steps sequentially. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_captioning, image_generation, optical_character_recognition # Step 1: Describe what's happening in the photo description = image_captioning(image='/root/input_data/2323530.jpg') # Step 2: Create a new related image from the description new_image = image_generation(text=description['text']) # Step 3: Read any text or captions that appear in the newly generated image text_from_new_image = optical_character_recognition(image=new_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_captioning": , "image_generation": , "optical_character_recognition": , "description": {'text': 'skiers are standing in the snow on a sunny day'}, "new_image": {'image': }, "text_from_new_image": {'text': ''}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The code executed without error, and we have the description of the original image, a new image generated from that description, and the result of attempting to read any text from the new image, which appears to be empty. This means there was no text detected in the new image. I will now summarize the solution. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import image_captioning, image_generation, optical_character_recognition description = image_captioning(image='/root/input_data/2323530.jpg') new_image = image_generation(text=description['text']) text_from_new_image = optical_character_recognition(image=new_image['image']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The user has acknowledged the receipt of the code summary. The task is complete. ACTION TYPE: done ACTION CONTENT: True -----Response----- None