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app.py
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app.py
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import streamlit as st
import numpy as np
import pickle
#st.set_option('deprecation.showfileUploaderEncoding',False)
model = pickle.load(open('finalized_model.sav','rb'))
st.title("")
st.markdown("<h1 style='text-align: center; color: White;background-color:#e84343'>The Place of AI in Tackling the Challenge of Malaria in Africa</h1>", unsafe_allow_html=True)
st.header("")
region = st.selectbox('Region', ('Central Africa', 'East Africa', 'North Africa', 'Southern Africa', 'West Africa'))
region = 0 if region == 'Central Africa' else 1 if region == 'East Africa' else 2 if region == 'North Africa' else 3 if region == 'Southern Africa' else 4
st.text('')
st.markdown("<h4 style='text-align: center; color: Black;'>Use the slider to select optimal variables</h4>", unsafe_allow_html=True)
st.text('')
col1, col2 = st.columns(2)
with col1:
rural_pop = st.slider("Rural population (%)", 0.0, 100.0, 5.0)
itns = st.slider('Use of insecticide-treated bed nets (% of under age 5 population)', 0.0, 100.0, 5.0)
ipt = st.slider('Intermittent preventive treatment (IPT) of malaria in pregnancy (% of pregnant women)', 0.0, 100.0, 5.0)
malaria_case = st.slider("Malaria cases", 0.0, 100.0, 5.0)
with col2:
urban_pop = st.slider("Urban population (%)", 0.0, 100.0, 5.0)
child_fever = st.slider('Children with fever receiving antimalarial drugs (% of children under age 5 with fever)', 0.0, 100.0, 5.0)
dw_all = st.slider("Drinking Water (%)", 0.0, 100.0, 5.0)
san_all = st.slider("Sanitation (%)", 0.0, 100.0, 5.0)
st.text('')
if st.button("Predict incidence of malaria (per 1,000 population at risk)"):
result = model.predict(np.array([[region, rural_pop, itns, ipt, malaria_case, urban_pop, child_fever, dw_all, san_all]]))
st.text(round(result[0],2))
st.text('')
st.text('')
st.markdown('`Code:` [GitHub](https://github.com/yusufokunlola/TeamFlask_notebook)')
# Reference: Santiago Víquez (2023). How to Deploy Machine Learning Models with Python & Streamlit.
# Accessed on 29/4/2023. https://365datascience.com/tutorials/machine-learning-tutorials/how-to-deploy-machine-learning-models-with-python-and-streamlit/