******************************************************************************** Step 1 ******************************************************************************** ------Action------ THOUGHT: None ACTION TYPE: None ACTION CONTENT: None -----Response----- RESPONSE (User Message): USER QUERY: "I have a specific layout in my mind for a room interior - 'A hotel room with a bed and a chair and white walls'. Could you help me visualize just the left portion of this image, so I could get a detailed understanding? Also, a textual picture of the same would be greatly helpful." 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 generate an image based on a text description. This function will be used to create the initial image of the hotel room. Then, I will need to find a function that can crop the image to show only the left portion of it. Lastly, I will need to find a function that can provide a textual description of the image. I will start by searching for the image generation function. ACTION TYPE: search ACTION CONTENT: text: "generate images give a text description" -----Response----- RESPONSE (Retrieval Result): QUERY: text: "generate images give a text description" CODE BLOCKS: No code blocks found. ******************************************************************************** Step 3 ******************************************************************************** ------Action------ THOUGHT: The search for an image generation function using the provided query did not yield any results. I will attempt a broader search for image generation functions in the `mnm` codebase without specifying the text description part, which might help in finding the relevant function. ACTION TYPE: search ACTION CONTENT: text: "generate images" -----Response----- RESPONSE (Retrieval Result): QUERY: text: "generate images" CODE BLOCKS: No code blocks found. ******************************************************************************** Step 4 ******************************************************************************** ------Action------ THOUGHT: The search for image generation functions has not returned any results. I will now attempt to search for image editing functions, as these might include the capability to generate images based on descriptions. ACTION TYPE: search ACTION CONTENT: text: "image editing functions" -----Response----- RESPONSE (Retrieval Result): QUERY: text: "image editing functions" CODE BLOCKS: No code blocks found. ******************************************************************************** Step 5 ******************************************************************************** ------Action------ THOUGHT: It seems that my search queries are not yielding the expected results. I will now try a different approach by searching for a function that can edit images given a description, which might implicitly include the capability to generate an image from a text description. ACTION TYPE: search ACTION CONTENT: text: "edit images given the original image and a description" -----Response----- RESPONSE (Retrieval Result): QUERY: text: "edit images given the original image and a description" CODE BLOCKS: No code blocks found. ******************************************************************************** Step 6 ******************************************************************************** ------Action------ THOUGHT: The search for an image editing function based on a description has also failed to return results. I will now try to search for functions related to image captioning, as this might be a way to get a textual picture of the image. Additionally, I will search for functions that can crop images, as this is needed to visualize just the left portion of the image. ACTION TYPE: search ACTION CONTENT: text: "image captioning" text: "image cropping" -----Response----- RESPONSE (Retrieval Result): QUERY: text: "image captioning" CODE BLOCKS: No code blocks found. QUERY: text: "image cropping" CODE BLOCKS: No code blocks found. ******************************************************************************** Step 7 ******************************************************************************** ------Action------ THOUGHT: The searches for image captioning and cropping functions have not returned any results. I will now attempt to search for any functions related to image generation or editing without specifying the action, to see if there are any relevant functions in the codebase that I can use. ACTION TYPE: search ACTION CONTENT: text: "image" -----Response----- RESPONSE (Retrieval Result): QUERY: text: "image" 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=[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=[536, 556] type=FUNCTION content={ def image_crop_left(image, ckpt=None): """ Crops the left half of the provided image and returns it in a formatted dictionary. Args: image: The image to be cropped. ckpt: Optional checkpoint data (unused in the current implementation). Returns: A dictionary containing the cropped image with the key 'image'. Examples: Assuming `image` is a PIL Image object: >>> result = image_crop_left(image) >>> result['image'] # This will contain the left half of the original image. """ ... } --- (19 additional code blocks not shown. Search again with the same query to see additional results.) Prototypes for the next 7 out of 16 classes/functions found in unexpanded results (search again with the same query to see details): def image_crop_top(image, ckpt=None): ... # mnm/tool_api.py def image_crop_bottom(image, ckpt=None): ... # mnm/tool_api.py def image_classification(image, ckpt=MODEL_SELECTION['image_classification']): ... # mnm/tool_api.py def image_crop_right(image, 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 tag(image, objects, ckpt=None): ... # mnm/tool_api.py ******************************************************************************** Step 8 ******************************************************************************** ------Action------ THOUGHT: I have found the functions needed to generate the image and crop it. The `image_editing` function can be used to generate an image based on a text description, and the `image_crop_left` function can be used to crop the left portion of the image. Additionally, the `image_captioning` function can provide a textual description of the image. I will now provide the code to use these functions. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_editing, image_crop_left, image_captioning # Generate an image based on the description description = "A hotel room with a bed and a chair and white walls" generated_image = image_editing(image=None, prompt=description) # Crop the left portion of the generated image cropped_image = image_crop_left(generated_image['image']) # Get a textual description of the cropped image textual_description = image_captioning(cropped_image['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 5, in File "/root/mnm/mnm/tool_api.py", line 293, in image_editing result = pipe(prompt, image=init_image).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_img2img.py", line 1221, in __call__ image = self.image_processor.preprocess(image) File "/opt/conda/lib/python3.10/site-packages/diffusers/image_processor.py", line 499, in preprocess f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support {', '.join(supported_formats)}" TypeError: 'NoneType' object is not iterable RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_editing": , "image_crop_left": , "image_captioning": , "description": A hotel room with a bed and a chair and white walls} ******************************************************************************** Step 9 ******************************************************************************** ------Action------ THOUGHT: The execution error indicates that the `image_editing` function does not accept `None` for the image parameter. I need to adjust the code to pass an appropriate argument for the image parameter or find a way to generate an image from a description without an initial image. I will search for the `image_editing` function in the codebase to understand its parameters and usage better. ACTION TYPE: search ACTION CONTENT: text: "image_editing" -----Response----- RESPONSE (Retrieval Result): QUERY: text: "image_editing" CODE BLOCKS: --- 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/config.py lines=[7, 22] type=ASSIGNMENT content={ MODEL_SELECTION = {'text_generation': 'gpt-3.5-turbo-0125', 'text_summarization': 'facebook/bart-large-cnn', 'text_classification': 'distilbert-base-uncased-finetuned-sst-2-english', 'question_answering': 'deepset/roberta-base-squad2', 'automatic_speech_recognition': 'openai/whisper-large-v2', 'image_generation': 'stabilityai/stable-diffusion-xl-base-1.0', 'image_captioning': 'Salesforce/blip-image-captioning-large', 'image_editing': 'stabilityai/stable-diffusion-xl-refiner-1.0', 'image_classification': 'google/vit-base-patch16-224', 'visual_question_answering': 'Salesforce/blip-vqa-base', 'object_detection': 'facebook/detr-resnet-101', 'image_segmentation': 'facebook/maskformer-swin-base-coco', 'optical_character_recognition': 'easyOCR'} } --- (All code blocks matching the query were returned.) ******************************************************************************** Step 10 ******************************************************************************** ------Action------ THOUGHT: The `image_editing` function requires an image as input, and it cannot be `None`. The error occurred because I passed `None` for the image parameter. However, I noticed in the `MODEL_SELECTION` that there is a key for 'image_generation'. This suggests that there might be a separate function for generating images from text descriptions. I will search for the 'image_generation' function to see if it can be used to generate an image from the description without needing an initial image. ACTION TYPE: search ACTION CONTENT: text: "image_generation" -----Response----- RESPONSE (Retrieval Result): QUERY: text: "image_generation" 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 } --- (All code blocks matching the query were returned.) ******************************************************************************** Step 11 ******************************************************************************** ------Action------ THOUGHT: I have found the `image_generation` function which can generate an image from a text prompt. I will use this function to generate the initial image based on the description provided by the user. Then, I will use the `image_crop_left` function to crop the left portion of the generated image. Finally, I will use the `image_captioning` function to get a textual description of the cropped image. I will now provide the updated code to perform these steps. ACTION TYPE: code ACTION CONTENT: from mnm.tool_api import image_generation, image_crop_left, image_captioning # Generate an image based on the description description = "A hotel room with a bed and a chair and white walls" generated_image = image_generation(text=description) # Crop the left portion of the generated image cropped_image = image_crop_left(generated_image['image']) # Get a textual description of the cropped image textual_description = image_captioning(cropped_image['image']) -----Response----- RESPONSE (Execution Result): CODE EXECUTED WITHOUT ERROR, STDOUT WAS EMPTY RELEVANT VARIABLES (only shown if string rep. has changed after code exec): {"image_generation": , "generated_image": {'image': }, "cropped_image": {'image': }, "textual_description": {'text': 'there is a large bed and a chair in a room'}} ******************************************************************************** Step 12 ******************************************************************************** ------Action------ THOUGHT: The code executed successfully, and we have obtained the generated image, the cropped image of the left portion, and a textual description of the cropped image. I will now provide a summary of the solution. ACTION TYPE: code_summary ACTION CONTENT: from mnm.tool_api import image_generation, image_crop_left, image_captioning output0 = image_generation(text="A hotel room with a bed and a chair and white walls") output1 = image_crop_left(output0['image']) output2 = image_captioning(output1['image']) -----Response----- RESPONSE (User Message): Summary received and stored. ******************************************************************************** Step 13 ******************************************************************************** ------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