import re import gradio as gr from PIL import Image, ImageDraw import math import torch import html from transformers import DonutProcessor, VisionEncoderDecoderModel pretrained_repo_name = 'ivelin/donut-refexp-combined-v1' pretrained_revision = 'main' # revision: '348ddad8e958d370b7e341acd6050330faa0500f' # Iou = 0.47 # revision: '41210d7c42a22e77711711ec45508a6b63ec380f' # : IoU=0.42 # use 'main' for latest revision print(f"Loading model checkpoint: {pretrained_repo_name}") processor = DonutProcessor.from_pretrained(pretrained_repo_name, revision=pretrained_revision) model = VisionEncoderDecoderModel.from_pretrained(pretrained_repo_name, revision=pretrained_revision) device = "cuda" if torch.cuda.is_available() else "cpu" model.to(device) def process_refexp(image: Image, prompt: str): print(f"(image, prompt): {image}, {prompt}") # trim prompt to 80 characters and normalize to lowercase prompt = prompt[:80].lower() # prepare encoder inputs pixel_values = processor(image, return_tensors="pt").pixel_values # prepare decoder inputs task_prompt = "{user_input}" prompt = task_prompt.replace("{user_input}", prompt) decoder_input_ids = processor.tokenizer( prompt, add_special_tokens=False, return_tensors="pt").input_ids # generate answer outputs = model.generate( pixel_values.to(device), decoder_input_ids=decoder_input_ids.to(device), max_length=model.decoder.config.max_position_embeddings, early_stopping=True, pad_token_id=processor.tokenizer.pad_token_id, eos_token_id=processor.tokenizer.eos_token_id, use_cache=True, num_beams=1, bad_words_ids=[[processor.tokenizer.unk_token_id]], return_dict_in_generate=True, ) # postprocess sequence = processor.batch_decode(outputs.sequences)[0] print(fr"predicted decoder sequence: {html.escape(sequence)}") sequence = sequence.replace(processor.tokenizer.eos_token, "").replace( processor.tokenizer.pad_token, "") # remove first task start token sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() print( fr"predicted decoder sequence before token2json: {html.escape(sequence)}") seqjson = processor.token2json(sequence) # safeguard in case predicted sequence does not include a target_bounding_box token bbox = seqjson.get('target_bounding_box') if bbox is None: print( f"token2bbox seq has no predicted target_bounding_box, seq:{seq}") bbox = {"xmin": 0, "ymin": 0, "xmax": 0, "ymax": 0} return bbox print(f"predicted bounding box with text coordinates: {bbox}") # safeguard in case text prediction is missing some bounding box coordinates # or coordinates are not valid numeric values try: xmin = float(bbox.get("xmin", 0)) except ValueError: xmin = 0 try: ymin = float(bbox.get("ymin", 0)) except ValueError: ymin = 0 try: xmax = float(bbox.get("xmax", 1)) except ValueError: xmax = 1 try: ymax = float(bbox.get("ymax", 1)) except ValueError: ymax = 1 # replace str with float coords bbox = {"xmin": xmin, "ymin": ymin, "xmax": xmax, "ymax": ymax, "decoder output sequence": sequence} print(f"predicted bounding box with float coordinates: {bbox}") print(f"image object: {image}") print(f"image size: {image.size}") width, height = image.size print(f"image width, height: {width, height}") print(f"processed prompt: {prompt}") # safeguard in case text prediction is missing some bounding box coordinates xmin = math.floor(width*bbox["xmin"]) ymin = math.floor(height*bbox["ymin"]) xmax = math.floor(width*bbox["xmax"]) ymax = math.floor(height*bbox["ymax"]) print( f"to image pixel values: xmin, ymin, xmax, ymax: {xmin, ymin, xmax, ymax}") shape = [(xmin, ymin), (xmax, ymax)] # deaw bbox rectangle img1 = ImageDraw.Draw(image) img1.rectangle(shape, outline="green", width=5) img1.rectangle(shape, outline="white", width=2) return image, bbox title = "Demo: Donut 🍩 for UI RefExp (by GuardianUI)" description = "Gradio Demo for Donut RefExp task, an instance of `VisionEncoderDecoderModel` fine-tuned on [UIBert RefExp](https://huggingface.co/datasets/ivelin/ui_refexp_saved) Dataset (UI Referring Expression). To use it, simply upload your image and type a prompt and click 'submit', or click one of the examples to load them. See the model training Colab Notebook for this space. Read more at the links below." article = "

Donut: OCR-free Document Understanding Transformer | Github Repo

" examples = [["example_1.jpg", "select the setting icon from top right corner"], ["example_1.jpg", "click on down arrow beside the entertainment"], ["example_1.jpg", "select the down arrow button beside lifestyle"], ["example_1.jpg", "click on the image beside the option traffic"], ["example_2.jpg", "enter the text field next to the name"], ["example_3.jpg", "select the third row first image"], ["example_3.jpg", "click the tick mark on the first image"], ["example_3.jpg", "select the ninth image"], ["example_3.jpg", "select the add icon"], ["example_3.jpg", "click the first image"], ["val-image-4.jpg", 'select 4153365454'], ['val-image-4.jpg', 'go to cell'] ['val-image-4.jpg', 'select number above cell'] ["val-image-1.jpg", "select calendar option"], ["val-image-1.jpg", "select photos&videos option"], ["val-image-2.jpg", "click on change store"], ["example_2.jpg", "click on green color button"], ["example_2.jpg", "click on text which is beside call now"], ["example_2.jpg", "click on more button"], ["val-image-2.jpg", "click on shop menu at the bottom"], ["val-image-3.jpg", "click on image above short meow"], ["val-image-3.jpg", "go to cat sounds"], ] demo = gr.Interface(fn=process_refexp, inputs=[gr.Image(type="pil"), "text"], outputs=[gr.Image(type="pil"), "json"], title=title, description=description, article=article, examples=examples, # caching examples inference takes too long to start space after app change commit cache_examples=False ) demo.launch()