******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "Looking at image 2326465.jpg, can you identify the fruit on the table? Then, using the fruit you identified, can you generate a new image of a supermarket aisle dedicated to this fruit? Lastly, extract any text that is discernible within the newly generated image for me." 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 detect objects in an image to identify the fruit on the table. Once the fruit is identified, I will search for a function that can generate a new image based on a text description, which will be a supermarket aisle dedicated to the identified fruit. Finally, I will look for a function that can perform optical character recognition (OCR) to extract any text from the generated image. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: detecting objects) (type: FUNCTION) AND (text: generating images) (type: FUNCTION) AND (text: transcribing alphanumeric characters) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: detecting objects) CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[377, 411] type=FUNCTION content={ def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): """ Performs object detection on an image using a DETR (DEtection TRansformer) model. Args: image: The image on which to perform object detection. ckpt: A string representing the model checkpoint to use for object detection. Defaults to a value from MODEL_SELECTION["object_detection"]. Returns: A dictionary with two keys: - 'image': The processed image. - 'objects': A list of dictionaries, each containing a 'bbox' key with the bounding box coordinates and a 'label' key with the object's label. Raises: ImportError: If the required modules 'torch' or 'transformers' are not installed. Examples: >>> result = object_detection(my_image) >>> print(result['objects']) [{'bbox': [x1, y1, x2, y2], 'label': 'object_label'}, ...] """ ... } --- file_path=mnm/tool_api.py lines=[810, 838] type=FUNCTION content={ def select_object(objects, object_name, ckpt=None): """ Selects an object from a list of objects based on the input object name. Args: objects (list): A list of dictionaries, where each dictionary represents an object with a "label" key. object_name (str): The name of the object to be selected. ckpt (optional): A checkpoint or state to resume from, not used in the current implementation. Returns: dict: A dictionary with a single key "object" that maps to the selected object dictionary. Examples: >>> objects_list = [{'label': 'apple'}, {'label': 'banana'}] >>> select_object(objects_list, 'apple') {'object': {'label': 'apple'}} """ ... } --- file_path=mnm/tool_api.py lines=[721, 729] type=FUNCTION content={ def count(objects, ckpt=None): """ It takes a list of objects and returns the count of the objects. """ objs = objects result_formatted = {"number": len(objs)} 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 tag(image, objects, ckpt=None): ... # mnm/tool_api.py def color_pop(image, object, ckpt=None): ... # mnm/tool_api.py def emoji(image, object, emoji, ckpt=None): ... # mnm/tool_api.py def background_blur(image, object, ckpt=None): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def object_processing(file_path): ... # mnm/tool_api.py def image_captioning(image, ckpt=MODEL_SELECTION['image_captioning']): ... # mnm/tool_api.py QUERY: (type: FUNCTION) AND (text: generating images) 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=[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=[111, 141] type=FUNCTION content={ def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): """ Generates a continuation of the given input text using a specified language model. Args: text (str): The input text prompt to be continued. ckpt (str, optional): The identifier for the language model to be used. Defaults to the model specified in MODEL_SELECTION["text_generation"]. Returns: dict: A dictionary containing the generated text under the key 'text'. Raises: KeyError: If the OPENAI_API_KEY environment variable is not set. APIError: If the OpenAI API encounters an issue with the request. Examples: >>> generated_text = text_generation("Once upon a time") >>> print(generated_text) {'text': '...continued text from the model...'} """ ... } --- (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 visual_question_answering(image, question, ckpt=MODEL_SELECTION['visual_question_answering']): ... # mnm/tool_api.py 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 image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py QUERY: (type: FUNCTION) AND (text: transcribing alphanumeric characters) 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=[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']) """ ... } --- (30 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 30 classes/functions found in unexpanded results (search again with the same query to see details): def text_processing(file_path): ... # 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 love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py def get_math_fact(number: str, ckpt=None): ... # mnm/tool_api.py def get_weather(lon: str, lat: str, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The search results have provided the necessary functions to solve the user's query. I will use `object_detection` to identify the fruit on the table, `image_generation` to generate a new image of a supermarket aisle dedicated to the identified fruit, and `optical_character_recognition` to extract any discernible text within the newly generated image. I will now write the code to execute these steps sequentially, waiting for the user to execute each step and provide the results before moving on to the next. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import object_detection, image_generation, optical_character_recognition # Step 1: Identify the fruit on the table using object detection image_path = '/root/input_data/2326465.jpg' detected_objects = object_detection(image_path) -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"object_detection": , "image_generation": , "optical_character_recognition": , "image_path": /root/input_data/2326465.jpg, "detected_objects": {'image': , 'objects': [{'bbox': [165.05, 149.08, 262.39, 255.57], 'label': 'apple'}, {'bbox': [17.09, 46.35, 197.84, 205.87], 'label': 'banana'}, {'bbox': [0.1, 1.99, 499.58, 329.44], 'label': 'dining table'}, {'bbox': [334.44, 7.25, 432.99, 107.58], 'label': 'apple'}, {'bbox': [253.95, 120.47, 355.64, 218.21], 'label': 'orange'}, {'bbox': [181.15, 47.73, 266.52, 135.19], 'label': 'orange'}, {'bbox': [264.06, 62.88, 349.8, 134.32], ...} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The object detection function has successfully identified multiple fruits on the table, including apples, bananas, and oranges. I will now proceed to generate a new image of a supermarket aisle dedicated to one of these fruits. I will choose the first fruit detected, which is an apple, for the image generation step. After generating the image, I will extract any discernible text from it using optical character recognition. ACTION TYPE: code ACTION CONTENT: # Step 2: Generate a new image of a supermarket aisle dedicated to the identified fruit (apple) description = "a supermarket aisle dedicated to apples" generated_image = image_generation(description) -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"description": a supermarket aisle dedicated to apples, "generated_image": {'image': }} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The image generation step has been completed successfully, and we now have an image of a supermarket aisle dedicated to apples. The next step is to extract any discernible text from the generated image using the optical character recognition function. ACTION TYPE: code ACTION CONTENT: # Step 3: Extract any discernible text from the generated image using optical character recognition extracted_text = optical_character_recognition(generated_image['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): {"extracted_text": {'text': ''}} ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The optical character recognition function has completed its execution, but it seems that no text was detected in the generated image. This could be because the image does not contain any discernible text, or the text may not be clear enough for the OCR to detect. I will now conclude the task as all steps have been executed. ACTION TYPE: done ACTION CONTENT: True -----Response----- None ******************************************************************************** Step 7 ******************************************************************************** ------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 8 ******************************************************************************** ------Action------ THOUGHT: The user has requested a code summary of the actions taken to solve the query. I will summarize the code that was used to identify the fruit on the table, generate a new image of a supermarket aisle dedicated to the identified fruit, and extract any text from the generated image. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import object_detection, image_generation, optical_character_recognition output0 = object_detection(image='/root/input_data/2326465.jpg') output1 = image_generation(text="a supermarket aisle dedicated to apples") output2 = optical_character_recognition(image=output1['image']) -----Response----- None