******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I'm working on a special project for 2023. Can you provide an interesting fact about that year and create an illustrative picture based on it? Once done, also help me with extracting the text from the 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 retrieve an interesting fact about the year 2023 using the information retrieval functions from the codebase. After that, I will generate an illustrative picture based on the fact and finally extract text from the generated image. I will start by searching for a function that can retrieve interesting facts about a specific year. ACTION TYPE: search ACTION CONTENT: text: interesting fact about year -----Response----- RESPONSE (Retrieval Result): QUERY: text: interesting fact about year CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[863, 877] type=FUNCTION content={ def get_year_fact(year: str, ckpt=None): """ It provides interesting facts about years. """ url = f"https://numbersapi.p.rapidapi.com/{year}/year" 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=[840, 861] type=FUNCTION content={ def get_date_fact(date: str, ckpt=None): """ It provides interesting facts about dates. """ from dateutil import parser date_str = date dt = parser.parse(date_str) formatted_date = dt.strftime("%m/%d") url = f"https://numbersapi.p.rapidapi.com/{formatted_date}/date" 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=[879, 893] type=FUNCTION content={ def get_math_fact(number: str, ckpt=None): """ It provides interesting math facts about numbers. """ url = f"https://numbersapi.p.rapidapi.com/{number}/math" 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 } --- (40 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 get_trivia_fact(number: str, ckpt=None): ... # mnm/tool_api.py def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py def select_object(objects, object_name, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `get_year_fact` from the `tool_api.py` file seems to be the right choice for retrieving an interesting fact about the year 2023. I will write code to call this function with "2023" as the argument. Once I have the fact, I will proceed to generate an illustrative picture based on it. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import get_year_fact # Retrieve an interesting fact about the year 2023 year_fact = get_year_fact("2023") print(year_fact) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': 'Istanbul Canal will be finished', 'number': 2023, 'found': True, 'type': 'year'} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"get_year_fact": , "year_fact": {'text': 'Istanbul Canal will be finished', 'number': 2023, 'found': True, 'type': 'year'}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: Now that I have the interesting fact about the year 2023, which is that the Istanbul Canal will be finished, I will search for a function that can generate an image based on a text description. This will help me create an illustrative picture based on the fact. ACTION TYPE: search ACTION CONTENT: text: generate image from text description -----Response----- RESPONSE (Retrieval Result): QUERY: text: generate image from 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 } --- (39 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 32 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 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 get_full_path_data(filename): ... # 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 5 ******************************************************************************** ------Action------ THOUGHT: The function `image_generation` from the `tool_api.py` file is suitable for generating an image based on a text description. I will write code to call this function with the text description "Istanbul Canal will be finished" to create an illustrative picture. After generating the image, I will proceed to extract text from it. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation # Generate an image based on the interesting fact about the year 2023 fact_description = "Istanbul Canal will be finished" generated_image = image_generation(fact_description) print(generated_image) -----Response----- RESPONSE (Execution Result): STDOUT: {'image': } RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_generation": , "fact_description": Istanbul Canal will be finished, "generated_image": {'image': }} ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: Now that the image has been generated, I need to search for a function that can perform optical character recognition (OCR) to extract text from the 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 } --- (33 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 29 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 wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py def search_movie(movie_title: str, movie_year: str, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The function `optical_character_recognition` from the `tool_api.py` file is the correct choice for extracting text from the generated image. I will write code to call this function with the generated image as the argument. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import optical_character_recognition # Extract text from the generated image extracted_text = optical_character_recognition(generated_image['image']) print(extracted_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{'text': ''} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"optical_character_recognition": , "extracted_text": {'text': ''}} ******************************************************************************** Step 8 ******************************************************************************** ------Action------ THOUGHT: The optical character recognition function did not return any text from the generated image, which likely means that there was no recognizable text in the image. This could be because the image is purely illustrative and does not contain any alphanumeric characters, or the OCR was unable to detect the text due to the image's complexity. Since the task is complete, I will now provide a summary of the solution. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import get_year_fact, image_generation, optical_character_recognition output0 = get_year_fact("2023") output1 = image_generation("Istanbul Canal will be finished") output2 = optical_character_recognition(output1['image']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 9 ******************************************************************************** ------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