******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: I am designing a game and want to create an environment based on 'a bathroom with a sink and television in it'. I need to generate this scene and specifically focus on the left side of the image, to extract any text possibly appearing there - such as signboards, mirrors with text imprint, tv screen text etc, for further translation into different languages in my game. 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 generate an image based on a text description. Once the image is generated, I will need to find a function that can crop the image to focus on the left side. After cropping, I will search for a function that can perform optical character recognition (OCR) on the cropped image to extract any text. I will start by searching for the image generation function. ACTION TYPE: search ACTION CONTENT: text: generate image description -----Response----- RESPONSE (Retrieval Result): QUERY: text: generate image 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 } --- (38 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 visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py def get_full_path_data(filename): ... # mnm/tool_api.py def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # 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 ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The `image_generation` function from the `mnm` codebase can be used to generate an image based on a text description. Next, I will search for a function that can crop the image to focus on the left side. After that, I will look for an OCR function to extract text from the cropped image. ACTION TYPE: search ACTION CONTENT: text: crop image -----Response----- RESPONSE (Retrieval Result): QUERY: text: crop image CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[536, 556] type=FUNCTION content={ def image_crop_left(image, ckpt=None): """ Crops the left half of the provided image and returns it in a formatted dictionary. Args: image: The image to be cropped. ckpt: Optional checkpoint data (unused in the current implementation). Returns: A dictionary containing the cropped image with the key 'image'. Examples: Assuming `image` is a PIL Image object: >>> result = image_crop_left(image) >>> result['image'] # This will contain the left half of the original image. """ ... } --- file_path=mnm/tool_api.py lines=[581, 602] type=FUNCTION content={ def image_crop_top(image, ckpt=None): """ Crops the top half of an image and returns it in a dictionary. Args: image: The image to be cropped. ckpt: Optional checkpoint data (unused in the current implementation). Returns: A dictionary containing the cropped image with the key 'image'. Examples: >>> from PIL import Image >>> original_image = Image.open('path/to/image.jpg') >>> cropped_image_info = image_crop_top(original_image) >>> cropped_image = cropped_image_info['image'] """ ... } --- file_path=mnm/tool_api.py lines=[604, 625] type=FUNCTION content={ def image_crop_bottom(image, ckpt=None): """ Crops the bottom half of an image and returns it in a dictionary. Args: image: The image to be cropped. ckpt: Optional checkpoint data (unused in the current implementation). Returns: A dictionary containing the cropped image with the key 'image'. Examples: Assuming `image` is a PIL Image object: >>> cropped_image_result = image_crop_bottom(image) >>> cropped_image = cropped_image_result['image'] """ ... } --- (23 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_right(image, ckpt=None): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def image_processing(img): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The `image_crop_left` function can be used to crop the left side of the image. Now, I will search for the OCR function mentioned in the prototypes, `optical_character_recognition`, to extract text from the cropped image. ACTION TYPE: search ACTION CONTENT: text: optical_character_recognition -----Response----- RESPONSE (Retrieval Result): QUERY: text: optical_character_recognition CODE BLOCKS: --- 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 } --- 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=[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 } --- (28 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 24 classes/functions found in unexpanded results (search again with the same query to see details): def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py def get_location(city: str, ckpt=None): ... # mnm/tool_api.py def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: I have found the necessary functions to generate the image, crop the left side, and perform OCR to extract text. I will now write the code to use these functions in sequence. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation, image_crop_left, optical_character_recognition # Generate an image based on the description generated_image_info = image_generation("a bathroom with a sink and television in it") # Crop the left side of the generated image cropped_image_info = image_crop_left(generated_image_info['image']) # Perform OCR to extract text from the cropped image extracted_text_info = optical_character_recognition(cropped_image_info['image']) -----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": , "image_crop_left": , "optical_character_recognition": , "generated_image_info": {'image': }, "cropped_image_info": {'image': }, "extracted_text_info": {'text': ''}} ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The code executed successfully, and the OCR function did not find any text in the cropped image. This could mean that there was no text on the left side of the generated image, or the text was not recognizable by the OCR. I will now summarize the code that was used to generate the image, crop the left side, and attempt to extract text. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import image_generation, image_crop_left, optical_character_recognition output0 = image_generation("a bathroom with a sink and television in it") output1 = image_crop_left(output0['image']) output2 = optical_character_recognition(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