******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: I came across a term - 'Juneteenth' in a book. To better comprehend the context, can I have a summarized information about 'Juneteenth' along with a visual depiction of it? 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 that can retrieve summarized information about 'Juneteenth'. I will search for a function related to retrieving information or summarizing text. After that, I will look for a function that can provide a visual depiction related to 'Juneteenth'. ACTION TYPE: search ACTION CONTENT: text: Juneteenth AND (type: FUNCTION) AND (text: retrieve OR text: summary OR text: information) text: Juneteenth AND (type: FUNCTION) AND (text: visual OR text: image OR text: depiction) -----Response----- RESPONSE (Retrieval Result): QUERY: text: Juneteenth AND (type: FUNCTION) AND (text: retrieve OR text: summary OR text: information) CODE BLOCKS: --- file_path=mnm/tool_api.py lines=[143, 163] type=FUNCTION content={ def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): """ Summarizes a given text into a few sentences using a pre-trained model. Args: text (str): The text to be summarized. ckpt: The checkpoint or model identifier to use for the summarization pipeline. Defaults to a pre-selected model checkpoint. Returns: dict: A dictionary containing the summarized text under the key 'text'. Examples: summary = text_summarization("The quick brown fox jumps over the lazy dog.") print(summary) # Outputs a summarized version of the input text. """ ... } --- 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=[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'}, ...] """ ... } --- (19 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 19 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 image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def image_generation(text: str, ckpt=MODEL_SELECTION['image_generation']): ... # 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 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 QUERY: text: Juneteenth AND (type: FUNCTION) AND (text: visual OR text: image OR text: depiction) CODE BLOCKS: --- 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." """ ... } --- 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=[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 } --- (12 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 12 classes/functions found in unexpanded results (search again with the same query to see details): def image_processing(img): ... # mnm/tool_api.py def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # mnm/tool_api.py def image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `text_summarization` from `tool_api.py` can be used to get a summarized information about 'Juneteenth'. For the visual depiction, I can use the `image_generation` function to generate an image based on a text description of 'Juneteenth'. I will now write the code to use these functions. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import text_summarization, image_generation # Retrieve summarized information about 'Juneteenth' summary_result = text_summarization("Juneteenth") # Generate a visual depiction of 'Juneteenth' visual_result = image_generation("Juneteenth") # Output the results summary_text = summary_result.get('text', 'No summary available') visual_image = visual_result.get('image', None) summary_text, visual_image -----Response----- RESPONSE (Execution Result): EXECUTION ERROR: Traceback (most recent call last): File "/root/codenav/codenav/environments/code_env.py", line 88, in exec_with_informative_errors exec(code_str, global_vars) File "", line 7, in File "/root/mnm/mnm/tool_api.py", line 245, in image_generation result = generator(prompt=text).images[0] File "/opt/conda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context return func(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py", line 1174, in __call__ noise_pred = self.unet( File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/diffusers/models/unets/unet_2d_condition.py", line 1169, in forward sample = self.conv_in(sample) File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 463, in forward return self._conv_forward(input, self.weight, self.bias) File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/conv.py", line 459, in _conv_forward return F.conv2d(input, weight, bias, self.stride, File "/root/codenav/codenav/environments/code_env.py", line 78, in timeout_handler raise CodeEnvTimeoutError( codenav.environments.code_env.CodeEnvTimeoutError: Execution timed out after 180.0 seconds. Please ensure your code runs within the time limit. RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"text_summarization": , "image_generation": , "summary_result": {'text': ''}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The `image_generation` function has caused a timeout error, likely due to the complexity of the image generation process. However, the `text_summarization` function has executed successfully, but it seems that the summary result is empty. I will need to provide a more specific prompt for the text summarization to ensure it returns a meaningful summary. I will also remove the image generation part and focus on getting the text summary first. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import text_summarization # Retrieve summarized information about 'Juneteenth' with a more specific prompt summary_result = text_summarization("Juneteenth is a holiday celebrating the emancipation of those who had been enslaved in the United States. It is also known as Freedom Day or Emancipation Day. Please provide a summary of this event.") # Output the results summary_text = summary_result.get('text', 'No summary available') summary_text -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"summary_result": {'text': 'Juneteenth is a holiday celebrating the emancipation of those who had been enslaved in the United States. It is also known as Freedom Day or Emancipation Day.'}, "summary_text": Juneteenth is a holiday celebrating the emancipation of those who had been enslaved in the United States. It is also known as Freedom Day or Emancipation Day.} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The text summarization function has returned a summary, but it appears to be the same as the input text, indicating that the function may not have summarized the text but rather returned it as is. Since the image generation function caused a timeout, I will now attempt to use the `image_classification` function to provide a visual depiction related to 'Juneteenth'. This function should be faster as it does not generate an image but classifies an existing one. I will need to find an image related to 'Juneteenth' to use this function. ACTION TYPE: search ACTION CONTENT: text: Juneteenth AND (type: FUNCTION) AND (text: image OR text: classification) -----Response----- RESPONSE (Retrieval Result): QUERY: text: Juneteenth AND (type: FUNCTION) AND (text: image OR text: classification) 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=[731, 765] type=FUNCTION content={ def tag(image, objects, ckpt=None): """ Tags objects in an image with bounding boxes and class labels. Args: image: The image to be processed and tagged. objects: A list of dictionaries, each containing 'bbox' (bounding box) and 'label' (class) for an object. ckpt (optional): A checkpoint or model state to use for tagging, not used in the current implementation. Returns: A dictionary with a key 'image' containing the tagged image. Examples: # Assuming 'image' is an image object and 'objects' is a list of object dictionaries with 'bbox' and 'label' result = tag(image, objects) tagged_image = result['image'] """ ... } --- 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.'} """ ... } --- (9 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 9 classes/functions found in unexpanded results (search again with the same query to see details): def get_location(city: str, ckpt=None): ... # 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 image_crop(image, object, ckpt=None): ... # mnm/tool_api.py def text_generation(text: str, ckpt=MODEL_SELECTION['text_generation']): ... # mnm/tool_api.py def love_calculator(first_name: str, second_name: str, ckpt=None): ... # mnm/tool_api.py def save_output(task_idx, node_idx, output_dict, result_folder): ... # mnm/tool_api.py ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The search did not return a function that directly provides a visual depiction related to 'Juneteenth'. However, the `image_generation` function could be used for this purpose, but it previously caused a timeout error. I will attempt to use the `image_generation` function again, but this time with a more specific prompt to see if it can generate an image without timing out. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation # Generate a visual depiction of 'Juneteenth' with a more specific prompt visual_result = image_generation("A visual representation of Juneteenth, the holiday celebrating the emancipation of those who had been enslaved in the United States.") # Output the results visual_image = visual_result.get('image', None) visual_image -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"visual_result": {'image': }, "visual_image": } ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The image generation function has executed successfully and returned an image object. Since the user requested a visual depiction along with the summarized information, I will now provide a code summary of the steps taken to fulfill the user's request. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import text_summarization, image_generation summary_result = text_summarization("Juneteenth is a holiday celebrating the emancipation of those who had been enslaved in the United States. It is also known as Freedom Day or Emancipation Day. Please provide a summary of this event.") summary_text = summary_result.get('text', 'No summary available') visual_result = image_generation("A visual representation of Juneteenth, the holiday celebrating the emancipation of those who had been enslaved in the United States.") visual_image = visual_result.get('image', None) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 8 ******************************************************************************** ------Action------ THOUGHT: The user has acknowledged receipt of the summary. The task is now complete. ACTION TYPE: done ACTION CONTENT: True -----Response----- None