Spaces:
Sleeping
Sleeping
File size: 2,483 Bytes
63a679d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | import streamlit as st
import pandas as pd
import pickle
from PIL import Image
def run():
# Load All Files
with open('final_model.pkl', 'rb') as file:
final_model = pickle.load(file)
Gender= st.selectbox("Gender", ["Male", "Female"])
Occupation = st.selectbox("Occupation", ["Software Engineer", "Doctor", "Sales Representative", "Teacher", "Nurse",
"Engineer", "Accountant", "Scientist", "Lawyer", "Salesperson", "Manager"])
Sleep_Duration = st.selectbox("Sleep_Duration", [6.1, 6.2, 5.9, 6.3, 7.8, 6. , 6.5, 7.6, 7.7, 7.9, 6.4, 7.5, 7.2, 5.8,
6.7, 7.3, 7.4, 7.1, 6.6, 6.9, 8. , 6.8, 8.1, 8.3, 8.5, 8.4, 8.2])
Physical_Activity_Level = st.selectbox("Physical_Activity_Level", [42, 60, 30, 40, 75, 35, 45, 50, 32, 70,
80, 55, 90, 47, 65, 85])
Stress_Level = st.selectbox("Stress_Level", [6, 8, 7, 4, 3, 5])
BMI_Category = st.selectbox("BMI_Category", ["Overweight", "Normal", "Obese", "Normal Weight"])
Heart_Rate = st.selectbox("Heart_Rate", [77, 75, 85, 82, 70, 80, 78, 69, 72, 68, 76, 81, 65, 84, 74, 67, 73, 83, 86])
st.write('In the following is the result of the data you have input : ')
data_inf = pd.DataFrame({
"Gender" : Gender,
"Occupation" : Occupation,
"Sleep Duration" : Sleep_Duration,
"Physical Activity Level" : Physical_Activity_Level,
"Stress Level" : Stress_Level,
"BMI Category" : BMI_Category,
"Heart Rate" : Heart_Rate,
}, index=[0])
st.table(data_inf)
if st.button(label='predict'):
# Melakukan prediksi data dummy
scaler = scaler.transform(input)
y_pred_inf = final_model.predict(data_inf)
st.write("Here is a prediction of the People Who Have Sleep Disorder: ")
if y_pred_inf[0] == 1:
st.subheader("The Diagnosis is:")
prediction = 'Sleep Apnea'
if y_pred_inf[0] == 2:
st.subheader("This People Diagnosed Insomnia")
prediction = 'Insomnia'
else:
st.subheader("This People Normal")
prediction = 'Normal'
st.subheader('Based on user input, the model predicted: ')
st.header(y_pred_inf) |