import os

# build detectron2 from source
# we can't build detectron2 in requirements.txt because it needs PyTorch installed first,
# but requirements.txt will try to build wheels before installing any packages.
os.system("pip install git+https://github.com/facebookresearch/detectron2.git")

import gradio as gr
import numpy as np
from datasets import load_dataset
from PIL import Image, ImageDraw, ImageFont
from transformers import LayoutLMv2ForTokenClassification, LayoutLMv2Processor

processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
model = LayoutLMv2ForTokenClassification.from_pretrained("nielsr/layoutlmv2-finetuned-funsd")

# load image example
dataset = load_dataset("nielsr/funsd", split="test")
image = Image.open(dataset[0]["image_path"]).convert("RGB")
image = Image.open("./invoice.png")
image.save("document.png")
# define id2label, label2color
labels = dataset.features["ner_tags"].feature.names
id2label = {v: k for v, k in enumerate(labels)}
label2color = {"question": "blue", "answer": "green", "header": "orange", "other": "violet"}


def unnormalize_box(bbox, width, height):
    return [
        width * (bbox[0] / 1000),
        height * (bbox[1] / 1000),
        width * (bbox[2] / 1000),
        height * (bbox[3] / 1000),
    ]


def iob_to_label(label):
    label = label[2:]
    if not label:
        return "other"
    return label


def process_image(image):
    width, height = image.size

    # encode
    encoding = processor(image, truncation=True, return_offsets_mapping=True, return_tensors="pt")
    offset_mapping = encoding.pop("offset_mapping")

    # forward pass
    outputs = model(**encoding)

    # get predictions
    predictions = outputs.logits.argmax(-1).squeeze().tolist()
    token_boxes = encoding.bbox.squeeze().tolist()

    # only keep non-subword predictions
    is_subword = np.array(offset_mapping.squeeze().tolist())[:, 0] != 0
    true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
    true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]

    # draw predictions over the image
    draw = ImageDraw.Draw(image)
    font = ImageFont.load_default()
    for prediction, box in zip(true_predictions, true_boxes):
        predicted_label = iob_to_label(prediction).lower()
        draw.rectangle(box, outline=label2color[predicted_label])
        draw.text((box[0] + 10, box[1] - 10), text=predicted_label, fill=label2color[predicted_label], font=font)

    return image


title = "Interactive demo: LayoutLMv2"
description = "Demo for Microsoft's LayoutLMv2, a Transformer for state-of-the-art document image understanding tasks. This particular model is fine-tuned on FUNSD, a dataset of manually annotated forms. It annotates the words appearing in the image as QUESTION/ANSWER/HEADER/OTHER. To use it, simply upload an image or use the example image below and click 'Submit'. Results will show up in a few seconds. If you want to make the output bigger, right-click on it and select 'Open image in new tab'."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2012.14740' target='_blank'>LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding</a> | <a href='https://github.com/microsoft/unilm' target='_blank'>Github Repo</a></p>"
examples = [["document.png"]]

css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}"
# css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }"
# css = ".output_image, .input_image {height: 600px !important}"

css = ".image-preview {height: auto !important;}"

gr.Interface(
    fn=process_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Image(type="pil", label="annotated image"),
    title=title,
    description=description,
    article=article,
    examples=examples,
    css=css,
).launch()