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eda.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+from PIL import Image
+
+#membuat function untuk nantinya dipanggil di app.py
+def run():
+    st.title('Welcome to Explaration Data Analysis')
+
+#menampilkan eda 
+    st.title('Sleep Disorder Distribution')
+    image = Image.open('1.png')
+    st.image(image, caption='figure 1')
+#menampilkan penjelasan 
+    with st.expander('Explanation'):
+        st.caption('''Berdasarkan bar plot di atas, dapat diketahui bahwa lebih banyak orang yang tidak mengalami gangguan tidur(normal),
+                      perbandingan orang yang mengalami sleep apnea maupun insomnia tidak sangat tipis.
+                   ''')
+
+#menampilkan eda 
+    st.title('BMI Category Distribution')
+    image = Image.open('2.png')
+    st.image(image, caption='figure 2')
+    image = Image.open('3.png')
+    st.image(image, caption='figure 3')
+#menampilkan penjelasan 
+    with st.expander('Explanation'):
+        st.caption('''
+                    Berdasarkan plot di atas:
+                    - lebih banyak pasien yang memiliki berat badan normal
+                    - namun tidak sedikit pula pasien yang memiliki masalah berat badan overweight
+                    - kebanyakan pasien yang memiliki berat badan overweight yaitu wanita
+                    - sedangkan pasien yang memiliki berat badan normal di dominasi oleh pria
+                   ''')
+
+#menampilkan eda 
+    st.title('Daily Step by Occupation')
+    image = Image.open('4.png')
+    st.image(image, caption='figure 4')
+#menampilkan penjelasan 
+    with st.expander('Explanation'):
+        st.caption('''Berdasarkan bar diatas dapat kita lihat bahwa pekerja yang miliki jumlah langkah harian paling banyak ialah seorang perawat,
+                   namun yang memiliki jumlah langkah harian paling sedikit ialah sales representative''')

model.py

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+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)