******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "We're producing a short animation based on the description 'A trolley with people drives down a street by a building'. After generating this scene, we need to detect if there are any writings or signs in our visuals. Finally, please categorize the detected text according to whether it gives off positive or negative vibes." 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 the image based on the given description. I will search for a function in the `tool_api.py` file that can handle image generation from a text description. Once the image is generated, I will look for a function that can perform optical character recognition (OCR) to detect any writings or signs in the image. Finally, I will search for a function that can classify the detected text into positive or negative categories. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: generate image from text description) (type: FUNCTION) AND (text: optical character recognition) (type: FUNCTION) AND (text: classify text) AND (text: positive OR negative) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (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 } --- (35 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 35 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 QUERY: (type: FUNCTION) AND (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 } --- (32 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 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 QUERY: (type: FUNCTION) AND (text: classify text) AND (text: positive OR negative) CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[165, 176] type=FUNCTION content={ def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): """ Classifies a given text into a category based on the model's vocabulary. Args: text (str): The text to be classified. ckpt: The model checkpoint to use for classification. Defaults to MODEL_SELECTION["text_classification"]. Returns: dict: A dictionary with the key 'text' and the value as the classification label. Examples: >>> text_classification("I love sunny days.") {'text': 'POSITIVE'} """ ... } --- 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=[178, 193] type=FUNCTION content={ def question_answering( question: str, text: str, ckpt=MODEL_SELECTION["question_answering"] ): # alternative: "deepset/roberta-base-squad2" """ It takes a text and a question, and outputs an answer to that question based on the text. """ question = text_processing(question) text = text_processing(text) pipe = pipeline("question-answering", model=ckpt) result_raw = pipe( question=question, context=text ) # {'score': 0.01082150824368, 'start': 0, 'end': 10, 'answer': 'My name is'} result_formatted = {"text": result_raw["answer"]} return result_formatted } --- (23 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 23 classes/functions found in unexpanded results (search again with the same query to see details): def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py def visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py def wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # 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 text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The search results have provided the necessary functions to solve the user's query. The `image_generation` function can be used to generate the image based on the description. The `optical_character_recognition` function will detect any writings or signs in the generated image. Finally, the `text_classification` function can classify the detected text into positive or negative categories. I will now write the code to execute these functions in sequence. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation, optical_character_recognition, text_classification # Step 1: Generate the image based on the description description = "A trolley with people drives down a street by a building" generated_image = image_generation(description) # Step 2: Detect any writings or signs in the generated image if 'image' in generated_image: ocr_results = optical_character_recognition(generated_image['image']) # Step 3: Categorize the detected text according to positive or negative vibes if 'text' in ocr_results: detected_texts = ocr_results['text'].split(', ') text_vibes = [text_classification(text) for text in detected_texts] -----Response----- RESPONSE (Execution Result): STDOUT (STDOUT was too long. 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Since no text was detected, the text classification function was called with an empty string, which returned a default classification of 'POSITIVE'. This result may not be meaningful as there was no actual text to classify. The task is complete, but it's important to note that no text was detected in the generated image. ACTION TYPE: done ACTION CONTENT: True -----Response----- None ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): Please immediately take a code_summary action to generate a summary of your code produced so far. If you have not produced any code yet, make an educated guess based on what you have learned so far. ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: I will summarize the code that was produced to solve the user's query. The code involved generating an image from a text description, detecting any writings or signs in the image, and classifying the detected text for positive or negative sentiment. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import image_generation, optical_character_recognition, text_classification generated_image = image_generation(text='A trolley with people drives down a street by a building') ocr_results = optical_character_recognition(image=generated_image['image']) detected_texts = ocr_results['text'].split(', ') text_vibes = [text_classification(text=text) for text in detected_texts] -----Response----- None