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app.py
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app.py
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import streamlit as st
import pandas as pd
import numpy as np
import os
import pickle
import warnings
st.beta_set_page_config(page_title="Heart Failure Prediction", page_icon="💉", layout='centered', initial_sidebar_state='auto')
# Data columns
feature_names_best = ['age', 'sex', 'anaemia', 'creatinine_phosphokinase', 'diabetes', 'ejection_fraction', 'high_blood_pressure','platelets', 'serum_creatinine', 'serum_sodium', 'smoking', 'time']
gender_dict = {"Male":1,"Female":0}
feature_dict = {"Yes":1,"No":0}
def load_image(img):
im =Image.open(os.path.join(img))
return im
def load_model(modelfile):
loaded_model = pickle.load(open(modelfile, 'rb'))
return loaded_model
def get_value(val,my_dict):
for key,value in my_dict.items():
if val == key:
return value
def get_fvalue(val):
feature_dict = {"Yes":1,"No":0}
for key,value in feature_dict.items():
if val == key:
return value
# title
html_temp = """
<div>
<h1 style="color:crimson;text-align:left;">Prediction of Survival in Patients with Heart Failure</h1>
</div>
"""
st.markdown(html_temp, unsafe_allow_html=True)
if st.checkbox("Information"):
'''
Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide.
Heart failure is a common event caused by CVDs. Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.
People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.
'''
'''
## How does it work ❓
Complete all the questions and the machine learning model will predict the survival of patients with heart failure
'''
# Logo
st.sidebar.image('real-heart-icon-png-.png', width=120)
st.sidebar.title("Prediction Form📋")
# Age of the patient
age = st.sidebar.number_input("Age", 1,100)
# Male or Female
sex = st.sidebar.radio("Sex",tuple(gender_dict.keys()))
# Decrease of red blood cells or hemoglobin
anaemia = st.sidebar.radio("Anaemia ?",tuple(feature_dict.keys()))
# Level of the CPK enzyme in the blood
creatinine_phosphokinase = st.sidebar.number_input("Creatinine phosphokinase (mcg/L)",1,7861)
# If the patient has diabetes
diabetes = st.sidebar.selectbox("Diabetes",tuple(feature_dict.keys()))
# Percentage of blood leaving
ejection_fraction = st.sidebar.number_input("Ejection fraction %",1,100)
# If a patient has hypertension
high_blood_pressure = st.sidebar.radio("High blood pressure",tuple(feature_dict.keys()))
# Platelets in the blood
platelets = st.sidebar.number_input("Platelets (kiloplatelets/mL)",0.0,100000.0)
# Level of creatinine in the blood
serum_creatinine = st.sidebar.number_input("Serum creatinine (mg/dL)",0.0,100.0)
# Level of sodium in the blood
serum_sodium = st.sidebar.number_input("Serum sodium (mEq/L)",1,1000)
# If the patient smokes
smoking = st.sidebar.radio("Smoking",tuple(feature_dict.keys()))
# Follow-up period
time = st.sidebar.number_input("Time (follow-up-period)", 1,1000)
feature_list = [age,get_value(sex,gender_dict),get_fvalue(anaemia),creatinine_phosphokinase,get_fvalue(diabetes),ejection_fraction,get_fvalue(high_blood_pressure),platelets,serum_creatinine,serum_sodium,get_fvalue(smoking), time]
pretty_result = {"Age":age,"Sex":sex,"Anaemia":anaemia,"Creatinine phosphokinase (mcg/L)":creatinine_phosphokinase,"Diabetes":diabetes,"Ejection fraction %":ejection_fraction,"High blood pressure":high_blood_pressure,"Platelets (kiloplatelets/mL)":platelets,"Serum creatinine (mg/dL)":serum_creatinine,"Serum sodium (mEq/L)":serum_sodium,"Smoking":smoking,"Time (follow-up-period)":time}
'''
## These are the values you entered 🧑⚕
'''
st.json(pretty_result)
single_sample = np.array(feature_list).reshape(1,-1)
if st.button("Predict"):
'''
## Results 👁🗨
'''
loaded_model = load_model('model.pkl')
prediction = loaded_model.predict(single_sample)
pred_prob = loaded_model.predict_proba(single_sample)
if prediction == 1:
st.error("The patient will die")
else:
st.success("The patient will live")
for live, die in loaded_model.predict_proba(single_sample):
live = f"{live*100:.2f}%"
die = f"{die*100:.2f} %"
st.table(pd.DataFrame({'Live ':live,
'Die': die}, index=['probability']))
st.warning("Note: This A.I application is for educational/demo purposes only and cannot be relied upon")
st.sidebar.subheader("Source code")
st.sidebar.info('''
[](https://github.com/gabbygab1233/Heart-Failure-Predictor-Application)
**Github**
''')
hide_menu_style = """
<style>
#MainMenu {visibility: hidden;}
</style>
"""
st.markdown(hide_menu_style, unsafe_allow_html=True)