******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: I'm working on a graphic novel project and I have a picture referenced as 2395343.jpg. I need a condensed and intense description of what's going on in the picture to maintain the storyline's fast pace. Once the description is ready, could you create an image from your narrative? I want to see if the new image would better fit the novel's art style and mood. 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 provide a description of an image. Once I have the description, I will then look for a function that can generate an image from a text description. I will start by searching for the image captioning function to get the description 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 function `image_captioning` from `tool_api.py` can be used to generate a text caption of the image. After obtaining the description, I will use the `image_generation` function to create a new image based on the narrative description. I will now write the code to use these functions, starting with `image_captioning`. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_captioning, image_generation # Load the image image_path = '/root/input_data/2395343.jpg' with open(image_path, 'rb') as file: image_data = file.read() # Generate a caption for the image caption_result = image_captioning(image_data) # Check the caption result print("Caption:", caption_result['text']) # Generate a new image based on the caption generated_image_result = image_generation(caption_result['text']) # Save the generated image to a file output_image_path = '/root/playground/generated_image.jpg' with open(output_image_path, 'wb') as file: file.write(generated_image_result['image']) print("Generated image saved to:", output_image_path) -----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_generation": , "image_path": /root/input_data/2395343.jpg, "file": <_io.BufferedReader name='/root/input_data/2395343.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 a local path or a PIL image, not the raw image bytes. I will modify the code to pass the image path directly to the function instead of reading the file and passing the bytes. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_captioning, image_generation from PIL import Image # Load the image using PIL as the function expects a PIL image or a local path image_path = '/root/input_data/2395343.jpg' image = Image.open(image_path) # Generate a caption for the image caption_result = image_captioning(image) # Check the caption result print("Caption:", caption_result['text']) # Generate a new image based on the caption generated_image_result = image_generation(caption_result['text']) # Save the generated image to a file output_image_path = '/root/playground/generated_image.jpg' with open(output_image_path, 'wb') as file: file.write(generated_image_result['image'].tobytes()) print("Generated image saved to:", output_image_path) -----Response----- RESPONSE (Execution Result): STDOUT: Caption: surfer riding a wave in the water on his surfboard Generated image saved to: /root/playground/generated_image.jpg RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"file": <_io.BufferedWriter name='/root/playground/generated_image.jpg'>, "Image": , "image": , "caption_result": {'text': 'surfer riding a wave in the water on his surfboard'}, "generated_image_result": {'image': }, "output_image_path": /root/playground/generated_image.jpg} ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: The code has executed successfully. The image caption has been generated, and a new image has been created based on the caption. The generated image has been saved to the specified path. The task is now complete. ACTION TYPE: done ACTION CONTENT: True -----Response----- None ******************************************************************************** Step 6 ******************************************************************************** ------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 7 ******************************************************************************** ------Action------ THOUGHT: The user has requested a code summary of the actions taken to generate a caption for an image and then create a new image based on that caption. I will provide a summary of the code that was executed successfully. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import image_captioning, image_generation from PIL import Image image_path = '/root/input_data/2395343.jpg' image = Image.open(image_path) caption_result = image_captioning(image) generated_image_result = image_generation(caption_result['text']) output_image_path = '/root/playground/generated_image.jpg' with open(output_image_path, 'wb') as file: file.write(generated_image_result['image'].tobytes()) -----Response----- None