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import streamlit as st
from streamlit import session_state as session
from PIL import Image
class TeethApp:
def __init__(self):
# Font
with open("utils/style.css") as css:
st.markdown(f"<style>{css.read()}</style>", unsafe_allow_html=True)
# Logo
self.image_path = "utils/teeth-295404_1280.png"
self.image = Image.open(self.image_path)
width, height = self.image.size
scale = 12
new_width, new_height = width / scale, height / scale
self.image = self.image.resize((int(new_width), int(new_height)))
# Streamlit side navigation bar
st.sidebar.markdown("# AI ToothSeg")
st.sidebar.markdown("Automatic teeth segmentation with Deep Learning")
st.sidebar.markdown(" ")
st.sidebar.image(self.image, use_column_width=False)
st.markdown(
"""
<style>
.css-1bxukto {
background-color: rgb(255, 255, 255) ;""",
unsafe_allow_html=True,
)
# Configure Streamlit page
st.set_page_config(page_title="Teeth Segmentation", page_icon="ⓘ")
class Guide(TeethApp):
def __init__(self):
TeethApp.__init__(self)
self.build_app()
def build_app(self):
st.title("More Coming Soon")
st.markdown("Made by [Huayuan Song](https://www.linkedin.com/in/huayuansong/) for the 10 ECTS [02830 Advanced Project in Digital Media Technology](https://kurser.dtu.dk/course/02830) project course at the [Technical University of Denmark (DTU)](https://dtu.dk/english).")
st.markdown("ML backend is based on MeshSegNet architecture by [Lian et al.](https://ieeexplore.ieee.org/abstract/document/8984309)")
st.markdown("The model has been trained on intra-oral scans of both upper and lower jaws annotated, validated by professionals in the 3DTeethSeg'22 Challenge by [Ben-Hamadou et al.](https://arxiv.org/abs/2305.18277)")
st.markdown("**Thanks for trying the app out!**")
st.image("illustration.png")
if __name__ == "__main__":
app = Guide() |