******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: I have an image 2336649.jpg. I need to understand the key points of this scene in a more condensed form first and then I need a continuation of the scene's narrative. Could you please analyze the image, provide a summary of what's happening, and then generate a follow-up scenario? 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 analyze the image and provide a summary of the scene. Once I have the summary, I can then look for a function that can generate a continuation of the scene's narrative based on the summary. I will start by searching for the image captioning function to get a summary of the image. ACTION TYPE: search ACTION CONTENT: (type: FUNCTION) AND (text: image captioning) -----Response----- RESPONSE (Retrieval Result): QUERY: (type: FUNCTION) AND (text: image captioning) CODE BLOCKS: --- 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=[269, 323] type=FUNCTION content={ def image_editing(image, prompt: str, ckpt=MODEL_SELECTION['image_editing']): """ Edits an image based on a given text prompt using a specified model checkpoint. Args: image: The input image to be edited. prompt (str): The text prompt describing the desired edits. ckpt: The model checkpoint to use for image editing. Defaults to MODEL_SELECTION["image_editing"]. Returns: A dictionary with the key 'image' containing the edited image. Raises: ImportError: If the necessary libraries for the chosen model are not installed. Examples: # Using the default checkpoint edited_image = image_editing(input_image, "A cat with a party hat.") # Using a specific checkpoint edited_image = image_editing(input_image, "A dog on a skateboard.", ckpt="timbrooks/instruct-pix2pix") """ ... } --- 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 } --- (36 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 optical_character_recognition(image, ckpt=MODEL_SELECTION['optical_character_recognition']): ... # mnm/tool_api.py def automatic_speech_recognition(audio: str, ckpt=MODEL_SELECTION['automatic_speech_recognition']): ... # 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 image_segmentation(image, ckpt=MODEL_SELECTION['image_segmentation']): ... # mnm/tool_api.py def object_detection(image, ckpt=MODEL_SELECTION['object_detection']): ... # mnm/tool_api.py def image_processing(img): ... # mnm/tool_api.py ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The `image_captioning` function from the `mnm` codebase is suitable for generating a summary of the image. I will write code to call this function with the image file provided by the user. After obtaining the summary, I will search for a function that can generate a continuation of the scene's narrative based on the summary. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_captioning # Load the image from the specified path image_path = '/root/input_data/2336649.jpg' with open(image_path, 'rb') as image_file: image_data = image_file.read() # Generate a caption for the image caption_result = image_captioning(image_data) print(caption_result) -----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 9, in File "/root/mnm/mnm/tool_api.py", line 261, in image_captioning result = pipe( File "/opt/conda/lib/python3.10/site-packages/transformers/pipelines/image_to_text.py", line 125, in __call__ return super().__call__(images, **kwargs) File "/opt/conda/lib/python3.10/site-packages/transformers/pipelines/base.py", line 1206, in __call__ return self.run_single(inputs, preprocess_params, forward_params, postprocess_params) File "/opt/conda/lib/python3.10/site-packages/transformers/pipelines/base.py", line 1212, in run_single model_inputs = self.preprocess(inputs, **preprocess_params) File "/opt/conda/lib/python3.10/site-packages/transformers/pipelines/image_to_text.py", line 128, in preprocess image = load_image(image, timeout=timeout) File "/opt/conda/lib/python3.10/site-packages/transformers/image_utils.py", line 332, in load_image raise ValueError( ValueError: Incorrect format used for image. Should be an url linking to an image, a base64 string, a local path, or a PIL image. RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_captioning": , "image_path": /root/input_data/2336649.jpg, "image_file": <_io.BufferedReader name='/root/input_data/2336649.jpg'>, "image_data": b'\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x01\x00H\x00H\x00\x00\xff\xdb\x00C\x00\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x01\x02\x02\x03\x02\x02\x02\x02\x02\x04\x03\x03\x02\x03\x05\x04\x05\x05\x05\x04\x04\x04\x05\x06\x07\x06\x05\x05\x07\x06\x04\x04\x06\t\x06\x07\x08\x08\x08\x08\x08\x05\x06\t\n\t\x08\n\x07\x08\x08\x08\xff\xdb\x00C\x01\x01\x01\x01\x02\x02\x02\x04\x02\x02\x04\x08\x05\x04\x05\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08\x08...} ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The execution error indicates that the image data format is incorrect for the `image_captioning` function. It seems that the function expects either a URL, a base64 string, a local path, or a PIL image object. Since I have the image as binary data, I need to convert it to a PIL image object before passing it to the function. I will modify the code to include this conversion step. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_captioning from PIL import Image import io # Load the image from the specified path image_path = '/root/input_data/2336649.jpg' with open(image_path, 'rb') as image_file: image_data = image_file.read() # Convert the binary data to a PIL image image = Image.open(io.BytesIO(image_data)) # Generate a caption for the image caption_result = image_captioning(image) print(caption_result) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': 'several people sitting at a table with plates of food and drinks'} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"Image": , "io": , "image": , "caption_result": {'text': 'several people sitting at a table with plates of food and drinks'}} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The image captioning was successful, and we have obtained a summary of the scene: 'several people sitting at a table with plates of food and drinks'. Now, I need to find a function in the `mnm` codebase that can generate a continuation of the scene's narrative based on this summary. I will search for a text generation function that can take this summary as input and produce a narrative continuation. 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=[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." """ ... } --- (31 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 31 classes/functions found in unexpanded results (search again with the same query to see details): def get_full_path_data(filename): ... # mnm/tool_api.py 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 ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The `text_generation` function from the `mnm` codebase is suitable for generating a continuation of the scene's narrative based on the summary obtained from the image captioning. I will write code to call this function with the summary as input to produce a narrative continuation. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import text_generation # Use the summary from the image captioning as the prompt for text generation summary_text = 'several people sitting at a table with plates of food and drinks' # Generate a continuation of the scene's narrative narrative_continuation = text_generation(summary_text) print(narrative_continuation) -----Response----- RESPONSE (Execution Result): STDOUT: {'text': ", engaging in conversation and enjoying each other's company. The atmosphere is lively and festive, with laughter filling the air. The table is filled with a variety of dishes, from appetizers to main courses, and everyone is happily digging in and sampling the delicious spread. Glasses of wine and cocktails are also present, adding to the convivial mood of the gathering. Overall, it's a scene of good food, good drinks, and good company, making for a memorable and enjoyable meal together."} {'text': ", engaging in conversation and enjoying each other's company. The atmosphere is lively and festive, with laughter filling the air. The table is filled with a variety of dishes, from appetizers to main courses, and everyone is happily digging in and sampling the delicious spread. Glasses of wine and cocktails are also present, adding to the convivial mood of the gathering. Overall, it's a scene of good food, good drinks, and good company, making for a memorable and enjoyable meal together."} RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"text_generation": , "summary_text": several people sitting at a table with plates of food and drinks, "narrative_continuation": {'text': ", engaging in conversation and enjoying each other's company. The atmosphere is lively and festive, with laughter filling the air. The table is filled with a variety of dishes, from appetizers to main courses, and everyone is happily digging in and sampling the delicious spread. Glasses of wine and cocktails are also present, adding to the convivial mood of the gathering. Overall, it's a scene of good food, good drinks, and good company, making for a memorable and enjoyable meal togeth...} ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The text generation was successful, and we have obtained a continuation of the scene's narrative. 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 image_captioning, text_generation from PIL import Image import io # Load the image and convert it to a PIL image image_path = '/root/input_data/2336649.jpg' with open(image_path, 'rb') as image_file: image_data = image_file.read() image = Image.open(io.BytesIO(image_data)) # Generate a caption for the image output0 = image_captioning(image) # Use the summary from the image captioning as the prompt for text generation summary_text = output0['text'] # Generate a continuation of the scene's narrative output1 = text_generation(summary_text) -----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