app.py

# -*- coding: utf-8 -*-
"""
Created on Fri Mar 31 17:45:36 2023

@author: Gaspar Avit Ferrero
"""
import os
import streamlit as st

from streamlit import session_state as session
from htbuilder import HtmlElement, div, hr, a, p, styles
from htbuilder.units import percent, px
from catboost import CatBoostClassifier


###############################
## ------- FUNCTIONS ------- ##
###############################

def link(link, text, **style):
    return a(_href=link, _target="_blank", style=styles(**style))(text)


def layout(*args):

    style = """
    <style>
      # MainMenu {visibility: hidden;}
      footer {visibility: hidden;}
     .stApp { bottom: 105px; }
    </style>
    """

    style_div = styles(
        position="fixed",
        left=0,
        bottom=0,
        margin=px(0, 0, 0, 0),
        width=percent(100),
        height=px(10),
        color="black",
        text_align="center",
        # height="auto",
        opacity=1
    )

    style_hr = styles(
        display="block",
        margin=px(8, 8, "auto", "auto"),
        border_style="inset",
        border_width=px(0)
    )

    body = p()
    foot = div(
        style=style_div
    )(
        body
    )

    st.markdown(style, unsafe_allow_html=True)

    for arg in args:
        if isinstance(arg, str):
            body(arg)

        elif isinstance(arg, HtmlElement):
            body(arg)

    st.markdown(str(foot), unsafe_allow_html=True)


def footer():
    myargs = [
        "Made by ",
        link("https://www.linkedin.com/in/gaspar-avit/", "Gaspar Avit"),
    ]  # with ❤️
    layout(*myargs)


def update_prediction(input_data):
    """Callback to automatically update prediction if button has already been
    clicked"""
    if is_clicked:
        launch_prediction(input_data)


def get_input_data():
    """
    Generate input layout and get input values.
        
    -return: DataFrame with input data.
    """
    session.input_data = pd.DataFrame()

    input_expander = st.expander('Input parameters', True)
    with input_expander:
        # Row 1
        col_age, col_sex = st.columns(2)
        with col_age:
            session.input_data['age'] = st.slider(
                'Age', 18, 75, on_change=update_prediction(session.input_data))
        with col_sex:
            session.input_data['sex'] = st.radio(
                'Sex', ['Female', 'Male'],
                on_change=update_prediction(session.input_data))

        # Row 2
        col_height, col_weight = st.columns(2)
        with col_height:
            session.input_data['height'] = st.slider(
                'Height', 140, 200,
                on_change=update_prediction(session.input_data))
        with col_weight:
            session.input_data['weight'] = st.slider(
                'Weight', 40, 140,
                on_change=update_prediction(session.input_data))

        # Row 3
        col_ap_hi, col_ap_lo = st.columns(2)
        with col_ap_hi:
            session.input_data['ap_hi'] = st.slider(
                'Systolic blood pressure', 90, 200,
                on_change=update_prediction(session.input_data))
        with col_ap_lo:
            session.input_data['ap_lo'] = st.slider(
                'Diastolic blood pressure', 50, 120,
                on_change=update_prediction(session.input_data))

        st.write("")

    return session.input_data


def generate_prediction(input_data):
    """
    Generate prediction of cardiovascular disease probability based on input
    data.
    
    -param input_data: DataFrame with input data
    
    -return: prediction of cardiovascular disease probability
    """
    return MODEL.predict(input_data)


###############################
## --------- MAIN ---------- ##
###############################


if __name__ == "__main__":

    ## --- Page config ------------ ##
    # Set page title
    st.title("""
    Cardiovascular Disease predictor
    #### This app aims to give a scoring of how probable is that an individual \
    would suffer from a cardiovascular disease given its physical \
         characteristics
    #### Just enter your info and get a prediction.
    """)

    # Set page footer
    # footer()

    # Initialize clicking flag
    is_clicked = False

    ## --------------------------- ##

    # Load classification model
    MODEL = CatBoostClassifier()
    MODEL.load_model('./model.cbm')

    # Get inputs
    session.input_data = get_input_data()

    # Create button to trigger poster generation
    buffer1, col1, buffer2 = st.columns([1.3, 1, 1])
    is_clicked = col1.button(label="Generate predictions")

    st.text("")
    st.text("")

    # Generate poster
    if is_clicked:
        prediction = generate_prediction(session.input_data)
        st.write(prediction)

    st.text("")
    st.text("")