Spaces:
Sleeping
Sleeping
| 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) |