import streamlit as st

st.set_page_config(
    page_title="predict_pm_tavi App",
    page_icon="👋",
)

st.title("Documentation!")
st.sidebar.success("Select a model above.")

st.markdown("This tool is derived from a single-center study of consecutive patients who underwent TAVI from January 2019 to December 2021 at University Hospitals of Brest. In this retrospective study, we presented the contribution of ML methods in predicting PMI after TAVI. Thanks to this practical, easy-to-use tool, clinicians will be able to estimate the post-operative risk of PMI and, as a result, optimize patient management. 3 models are avaible depending on the patient stage management (i.e., pre-, per- or post-TAVI). By simply entering the variables required for each model, a percentage risk of PMI would be given by this tool")