******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: Based on the auditory content of "6829-68769-0002.flac", I am curious about a few things. First, can we extrapolate what the lawyer's conversation could have been about before or after that line? Also, I want to understand, what could be the possible reasons or situations that caused the old lawyer to say "I can't see it in that light"? 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 transcribe the audio content of "6829-68769-0002.flac" to understand what is being said. Once I have the transcription, I can use the text understanding functions to generate text that could extrapolate the conversation before or after the line mentioned. Additionally, I can use the same functions to generate possible reasons or situations for the statement "I can't see it in that light". I will start by searching for the speech recognition function in the codebase. ACTION TYPE: search ACTION CONTENT: type: FUNCTION AND text: speech recognition -----Response----- RESPONSE (Retrieval Result): QUERY: type: FUNCTION AND text: speech recognition CODE BLOCKS: --- 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=[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=[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'}, ...] """ ... } --- (All code blocks matching the query were returned.) ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The function `automatic_speech_recognition` from `tool_api.py` is what I need to transcribe the audio content. I will write code to call this function with the audio file "6829-68769-0002.flac" to get the transcription. After obtaining the transcription, I can proceed to generate text that could extrapolate the conversation and explore possible reasons for the statement "I can't see it in that light". ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import automatic_speech_recognition # Transcribe the audio content audio_file_path = "/root/input_data/6829-68769-0002.flac" transcription = automatic_speech_recognition(audio_file_path) print(transcription) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': ' I can\'t see it in that light," said the old lawyer.'} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"automatic_speech_recognition": , "audio_file_path": /root/input_data/6829-68769-0002.flac, "transcription": {'text': ' I can\'t see it in that light," said the old lawyer.'}} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: Now that I have the transcription, I need to search for text generation functions that can extrapolate the conversation before or after the given line. Additionally, I will look for functions that can generate possible reasons or situations for the statement "I can't see it in that light". I will start by searching for text generation functions in the codebase. ACTION TYPE: search ACTION CONTENT: type: FUNCTION AND text: text generation -----Response----- RESPONSE (Retrieval Result): QUERY: type: FUNCTION AND text: text generation CODE BLOCKS: --- 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...'} """ ... } --- 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 } --- (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 wikipedia_simple_search(text: str, ckpt=None): ... # mnm/tool_api.py def text_classification(text: str, ckpt=MODEL_SELECTION['text_classification']): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def text_summarization(text: str, ckpt=MODEL_SELECTION['text_summarization']): ... # mnm/tool_api.py def question_answering(question: str, text: str, ckpt=MODEL_SELECTION['question_answering']): ... # mnm/tool_api.py def tag(image, objects, ckpt=None): ... # mnm/tool_api.py def optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The function `text_generation` from `tool_api.py` can be used to generate a continuation of the given input text. I will use this function to generate text that could extrapolate the conversation before and after the given line. Additionally, I will use the same function to generate possible reasons or situations for the statement "I can't see it in that light". I will write code to call this function with the appropriate prompts. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import text_generation # The transcribed text from the audio transcribed_text = " I can't see it in that light," said the old lawyer." # Generate text that could extrapolate the conversation before the given line prompt_before = "What led to the conversation where the old lawyer said: " + transcribed_text generated_text_before = text_generation(prompt_before) print("Before the line:", generated_text_before) # Generate text that could extrapolate the conversation after the given line prompt_after = transcribed_text + " What did the old lawyer say next?" generated_text_after = text_generation(prompt_after) print("After the line:", generated_text_after) # Generate possible reasons or situations for the statement prompt_reasons = "Why would an old lawyer say: " + transcribed_text generated_reasons = text_generation(prompt_reasons) print("Possible reasons:", generated_reasons) -----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 4 transcribed_text = " I can't see it in that light," said the old lawyer." ^ SyntaxError: unterminated string literal (detected at line 4) STATIC ANALYSIS ERRORS: TypeCheckingError(code_ref="transcribed_text = " I can't see it in that light," said the old lawyer."", line_num=4, error="unterminated string literal (detected at line 12) [syntax]") ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: There is a syntax error in the code due to incorrect string literal handling. The double quotes within the string are not escaped, causing the string to terminate prematurely. I need to fix the string by escaping the internal double quotes and then re-run the code. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import text_generation # The transcribed text from the audio, with escaped quotes transcribed_text = " I can't see it in that light,\" said the old lawyer." # Generate text that could extrapolate the conversation before the given line prompt_before = "What led to the conversation where the old lawyer said: " + transcribed_text generated_text_before = text_generation(prompt_before) print("Before the line:", generated_text_before) # Generate text that could extrapolate the conversation after the given line prompt_after = transcribed_text + " What did the old lawyer say next?" generated_text_after = text_generation(prompt_after) print("After the line:", generated_text_after) # Generate possible reasons or situations for the statement prompt_reasons = "Why would an old lawyer say: " + transcribed_text generated_reasons = text_generation(prompt_reasons) print("Possible reasons:", generated_reasons) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': 'It is not specified in the prompt what led to the conversation where the old lawyer made that statement. It could be a discussion or argument about a legal case, a moral dilemma, a personal matter, or any other situation where the old lawyer\'s perspective differs from that of someone else in the conversation. The statement "I can\'t see it in that light" suggests that the old lawyer has a different interpretation or viewpoint on the topic being discussed.'} Before the line: {'text': 'It is not specified in the prompt what led to the conversation where the old lawyer made that statement. It could be a discussion or argument about a legal case, a moral dilemma, a personal matter, or any other situation where the old lawyer\'s perspective differs from that of someone else in the conversation. The statement "I can\'t see it in that light" suggests that the old lawyer has a different interpretation or viewpoint on the topic being discussed.'} {'text': 'Without further context, it is unclear what the old lawyer said next.'} After the line: {'text': 'Without further context, it is unclear what the old lawyer said next.'} {'text': "The old lawyer may be saying this to indicate that they cannot view a situation or argument from a different perspective or interpret it in the same way as the other person. This can imply that the old lawyer may have a different understanding or opinion on the matter, or simply that they disagree with the other person's viewpoint."} Possible reasons: {'text': "The old lawyer may be saying this to indicate that they cannot view a situation or argument from a different perspective or interpret it in the same way as the other person. This can imply that the old lawyer may have a different understanding or opinion on the matter, or simply that they disagree with the other person's viewpoint."} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"text_generation": , "transcribed_text": I can't see it in that light," said the old lawyer., "prompt_before": What led to the conversation where the old lawyer said: I can't see it in that light," said the old lawyer., "generated_text_before": {'text': 'It is not specified in the prompt what led to the conversation where the old lawyer made that statement. It could be a discussion or argument about a legal case, a moral dilemma, a personal matter, or any other situation where the old lawyer\'s perspective differs from that of someone else in the conversation. The statement "I can\'t see it in that light" suggests that the old lawyer has a different interpretation or viewpoint on the topic being discussed.'}, "prompt_after": I can't see it in that light," said the old lawyer. What did the old lawyer say next?, "generated_text_after": {'text': 'Without further context, it is unclear what the old lawyer said next.'}, "prompt_reasons": Why would an old lawyer say: I can't see it in that light," said the old lawyer., "generated_reasons": {'text': "The old lawyer may be saying this to indicate that they cannot view a situation or argument from a different perspective or interpret it in the same way as the other person. This can imply that the old lawyer may have a different understanding or opinion on the matter, or simply that they disagree with the other person's viewpoint."}} ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The code executed successfully and generated text for the extrapolation of the conversation before and after the given line, as well as possible reasons for the old lawyer's statement. I will now provide a summary of the solution using the code_summary action. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import automatic_speech_recognition, text_generation transcription = automatic_speech_recognition(audio="/root/input_data/6829-68769-0002.flac") prompt_before = "What led to the conversation where the old lawyer said: " + transcription['text'] output0 = text_generation(prompt_before) prompt_after = transcription['text'] + " What did the old lawyer say next?" output1 = text_generation(prompt_after) prompt_reasons = "Why would an old lawyer say: " + transcription['text'] output2 = text_generation(prompt_reasons) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 8 ******************************************************************************** ------Action------ THOUGHT: The task has been completed successfully, and the user has acknowledged receipt of the summary. ACTION TYPE: done ACTION CONTENT: True -----Response----- None