******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "Hey, I need to create an invitation card for my friend. It should feature an image of a woman playing with a video game controller. Once you've got that, could you please transcribe the text from the generated image to help me build the message for the invitation?" 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: Before writing any code, I need to find the appropriate functions in the `mnm` codebase that can generate an image based on a text description and transcribe text from an image. I will start by searching for the image generation function using a description and then look for the optical character recognition (OCR) function to transcribe text from images. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: generate image) (type: FUNCTION) AND (text: transcribe text) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: generate image) 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=[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=[348, 375] type=FUNCTION content={ def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): """ Processes an image and a question about the image, then generates an answer using a pre-trained model. Args: image: The image to be processed. question: The question related to the image. ckpt: The checkpoint or model identifier for the pre-trained model. Defaults to MODEL_SELECTION["visual_question_answering"]. Returns: A dictionary with a key 'text' containing the generated answer to the question. Examples: >>> answer = visual_question_answering(my_image, "What is in this image?") >>> print(answer['text']) "A cat sitting on a windowsill." """ ... } --- (27 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 27 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 get_full_path_data(filename): ... # mnm/tool_api.py def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py QUERY: (type: FUNCTION) AND (text: transcribe text) CODE BLOCKS: --- 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=[413, 461] type=FUNCTION content={ def image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): """ Segments an image into different parts using a pre-trained MaskFormer model and outputs segmentation masks along with additional information for each segment. Args: image: The input image to be segmented. ckpt: A string or a dictionary key that specifies the pre-trained model checkpoint to be used for segmentation. Defaults to a pre-selected model checkpoint from a MODEL_SELECTION dictionary. Returns: A dictionary containing the original image and a list of objects. Each object in the list is a dictionary with keys 'mask', 'label', 'bbox', and 'inst_id', representing the segmentation mask, label, bounding box, and instance ID, respectively. Raises: ImportError: If required modules (numpy, torch, transformers, PIL) are not installed. Examples: # Assuming `image` is a PIL image and `MODEL_SELECTION` is defined segmentation_result = image_segmentation(image) for obj in segmentation_result['objects']: print(obj['label'], obj['bbox']) """ ... } --- 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'} """ ... } --- (18 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 18 classes/functions found in unexpanded results (search again with the same query to see details): def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py def text_processing(file_path): ... # mnm/tool_api.py def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # 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 ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The search results returned the `image_generation` function which can be used to generate an image based on a text description. Additionally, the `optical_character_recognition` function was found in the unexpanded results, which can be used to transcribe text from an image. I will now write the code to use these functions to generate the invitation card image and then transcribe the text from it. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation, optical_character_recognition # Generate an image based on the description description = "a woman playing with a video game controller" generated_image_result = image_generation(text=description) # Assuming the image is saved in a variable `generated_image` generated_image = generated_image_result['image'] # Transcribe text from the generated image transcribed_text_result = optical_character_recognition(image=generated_image) transcribed_text = transcribed_text_result['text'] -----Response----- RESPONSE (Execution Result): STDOUT (STDOUT was too long. Showing only the start and end separated by ellipsis.): Progress: |--------------------------------------------------| 0.0% Complete Progress: |--------------------------------------------------| 0.0% Complete Progress: |--------------------------------------------------| 0.0% Complete Progress: |--------------------------------------------------| 0.0% Complete Progress: |--------------------------------------------------| 0.0% Complete Progress: |--------------------------------------------------| 0.1% Complete Progress: |--------------------------------------------------| 0.1% Complete Progress: |--------------------------------------------------| 0.1% Complete Progress: |--------------------------------------------------| 0.1% Complete Progress: |--------------------------------------------------| 0.1% Complete Progress: |--------------------------------------------------| 0.1% Complete Progress: |--------------------------------------------------| 0.1% Complete Progress: |--------------------------------------------------| 0.1% Complet ... ██████████████████████████████████████████████-| 99.3% Complete Progress: |█████████████████████████████████████████████████-| 99.4% Complete Progress: |█████████████████████████████████████████████████-| 99.4% Complete Progress: |█████████████████████████████████████████████████-| 99.5% Complete Progress: |█████████████████████████████████████████████████-| 99.5% Complete Progress: |█████████████████████████████████████████████████-| 99.6% Complete Progress: |█████████████████████████████████████████████████-| 99.7% Complete Progress: |█████████████████████████████████████████████████-| 99.7% Complete Progress: |█████████████████████████████████████████████████-| 99.8% Complete Progress: |█████████████████████████████████████████████████-| 99.8% Complete Progress: |█████████████████████████████████████████████████-| 99.9% Complete Progress: |█████████████████████████████████████████████████-| 99.9% Complete Progress: |██████████████████████████████████████████████████| 100.0% Complete RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_generation": , "optical_character_recognition": , "description": a woman playing with a video game controller, "generated_image_result": {'image': }, "generated_image": , "transcribed_text_result": {'text': ''}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The code execution was successful, and the image was generated. However, the transcription result is an empty string, which indicates that there may not be any text in the generated image or the OCR function could not detect it. Since the user's request was to transcribe text from the generated image to help build the message for the invitation, and considering that the generated image may not contain any text, I will proceed to the next step of providing a code summary for the user to finalize the invitation card. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import image_generation, optical_character_recognition output0 = image_generation(text="a woman playing with a video game controller") output1 = optical_character_recognition(image=output0['image']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The task has been completed successfully, and the user has acknowledged the receipt of the code summary. ACTION TYPE: done ACTION CONTENT: True -----Response----- None