app.py

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-click'
pretrained_revision = 'main'
# revision can be git commit hash, branch or tag
# use 'main' for latest revision
print(f"Loading model checkpoint: {pretrained_repo_name}")

processor = DonutProcessor.from_pretrained(pretrained_repo_name, revision=pretrained_revision, use_auth_token="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")
model = VisionEncoderDecoderModel.from_pretrained(pretrained_repo_name, use_auth_token="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", revision=pretrained_revision)

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

def translate_point_coords_from_out_to_in(point=None, input_image_size=None, output_image_size=None):
    """
    Convert relative prediction coordinates from resized encoder tensor image
    to original input image size.
    Args:
        original_point: x, y coordinates of the point coordinates in [0..1] range in the original image 
        input_image_size: (width, height) tuple
        output_image_size: (width, height) tuple
    """    
    assert point is not None
    assert input_image_size is not None
    assert output_image_size is not None
    # print(f"point={point}, input_image_size={input_image_size}, output_image_size={output_image_size}")
    input_width, input_height = input_image_size
    output_width, output_height = output_image_size
    
    ratio = min(output_width/input_width, output_height/input_height)
    
    resized_height = int(input_height*ratio)
    # print(f'>>> resized_height={resized_height}')
    resized_width = int(input_width*ratio)
    # print(f'>>> resized_width={resized_width}')

    if resized_height == input_height and resized_width == input_width:
        return

    # translation of the relative positioning is only needed for dimentions that have padding
    if resized_width < output_width:
        # adjust for padding pixels
        point['x'] *= (output_width / resized_width)
    if resized_height < output_height:
        # adjust for padding pixels
        point['y'] *= (output_height / resized_height)
    # print(f"translated point={point}, resized_image_size: {resized_width, resized_height}")
        

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 = "<s_refexp><s_prompt>{user_input}</s_prompt><s_target_center>"
    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_center token
    center_point = seqjson.get('target_center')
    if center_point is None:
        print(
            f"predicted sequence has no target_center, seq:{sequence}")
        center_point = {"x": 0, "y": 0}
        return center_point

    print(f"predicted center_point with text coordinates: {center_point}")
    # safeguard in case text prediction is missing some center point coordinates
    # or coordinates are not valid numeric values
    try:
        x = float(center_point.get("x", 0))
    except ValueError:
        x = 0
    try:
        y = float(center_point.get("y", 0))
    except ValueError:
        y = 0
    # replace str with float coords
    center_point = {"x": x, "y": y, "decoder output sequence": sequence}
    print(f"predicted center_point with float coordinates: {center_point}")

    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}")

    # convert coordinates from tensor image size to input image size
    out_size = (processor.image_processor.size[1], processor.image_processor.size[0])
    translate_point_coords_from_out_to_in(point=center_point, input_image_size=image.size, output_image_size=out_size)
    
    x = math.floor(width*center_point["x"])
    y = math.floor(height*center_point["y"])

    print(
        f"to image pixel values: x, y: {x, y}")

    # draw center point circle
    img1 = ImageDraw.Draw(image)

    r = 30
    shape = [(x-r, y-r), (x+r, y+r)]
    img1.ellipse(shape, outline="green", width=20)
    img1.ellipse(shape, outline="white", width=10)

    return image, center_point


title = "Demo: Donut 🍩 for UI RefExp - Center Point (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 <a href='https://colab.research.google.com/github/ivelin/donut_ui_refexp/blob/main/Fine_tune_Donut_on_UI_RefExp.ipynb' target='_parent'>Colab Notebook</a> for this space. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.15664' target='_blank'>Donut: OCR-free Document Understanding Transformer</a> | <a href='https://github.com/clovaai/donut' target='_blank'>Github Repo</a></p>"
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_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"],
            ["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"],
            ["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"],
            ["example_2.jpg", "enter the text field next to the name"],
            ]

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()