Upload 5 files

app.py

@@ -1,3 +1,4 @@
+import numpy as np
 import streamlit as st
 import eda
 import models
@@ -75,4 +76,4 @@ Bagaimana model prediksi ini akan diuji, divalidasi, dan dioptimalkan untuk mema
 elif page == 'Exploration Data Analysis':
     eda.run()
 else:
-     models .run()
+     models.run()

eda.py

@@ -8,11 +8,14 @@ from PIL import Image
 #membuat function untuk nantinya dipanggil di app.py
 def run():
     st.title('Welcome to Explaration Data Analysis')
+    st.subheader ('Eda Mardi ML 2')
+
 #Memanggil data csv 
     df= pd.read_csv(r'heart_failure.csv')
 
 #menampilakn 5 data teratas
-    st.table(df.head(5))
+    st.table(df.head())
+    st.table(df.tail())
 
 
 #menampilakn phik matrix

models.py

@@ -1,46 +1,34 @@
 import streamlit as st
 import pandas as pd
 import pickle
+import numpy as np
 
 def run():
-# Load All Files
-
-    with open('best_linear_svm_model.pkl', 'rb') as file:
+    # Load All Files
+    with open('best_linear_svm_model.pkl', 'rb') as file: 
         full_process = pickle.load(file)
 
-        
-time = np.random.randint(1,300,100)
-serum_creatinine = np.random.choice(['0.1','2.9'],100)
-ejection_fraction = np.random.randint(10,70,100)    
-
-    time = st.number_input(label='Silakan Masukan Waktu', min_value=0.1,max_value=0.99)
-    serum_creatinine = st.number_input(label='Silakan Masukan serum_creatinine here', min_value=0.1,max_value=0.99)
-    ejection_fraction = st.number_input(label='Silakan Masukan ejection_fraction here', min_value=0.1,max_value=0.99)
-    
-    st.write('Prediksi atas data yang Anda sampaikan adalah : ')
-    
-    data_inf = pd.DataFrame({
-         'time' : time,
-        'serum_creatinine' : serum_creatinine,
-        'ejection_fraction' : ejection_fraction ,
-        }, index=[0])
-
-    st.table(data_inf)
-
-
-    if st.button(label='predict'):
-    
-        # Melakukan prediksi data dummy
-        y_pred_inf = full_process.predict(data_inf)
-
-        
-        st.metric(label="Prediksinya adalah : ", value = y_pred_inf[0])
-
-    # If your data is a classification, you can follow the example below 
-        # if y_pred_inf[0] == 0:
-        #     st.write('Pasien tidak terkena jantung')
-        #     st.markdown("[Cara Cegah Serangan Jantung](https://www.siloamhospitals.com/informasi-siloam/artikel/cara-cegah-serangan-jantung-di-usia-muda)")
-
-        # else:
-        #     st.write('Pasien kemungkinan terkena jantung')
-        #     st.markdown("[Cara Hidup Sehat Sehabis Terkena Serangan Jantung](https://lifestyle.kompas.com/read/2021/11/09/101744620/7-pola-hidup-sehat-setelah-mengalami-serangan-jantung?page=all)")
+time = np.random.choice(['0.0', '1'], 1000)
+serum_creatinine = np.random.choice(['0.0', '1'], 1000)
+ejection_fraction = np.random.choice(['0.0', '1'], 1000)   
+
+time_input = st.number_input(label='Silakan Masukan Waktu', min_value=0.1, max_value=0.99)
+serum_creatinine_input = st.number_input(label='Silakan Masukan serum_creatinine here', min_value=0.1, max_value=0.99)
+ejection_fraction_input = st.number_input(label='Silakan Masukan ejection_fraction here', min_value=0.1, max_value=0.99)
+
+st.write('Prediksi atas data yang Anda sampaikan adalah: ')
+
+data_inf = pd.DataFrame({
+    'time': time_input,
+    'serum_creatinine': serum_creatinine_input,
+    'ejection_fraction': ejection_fraction_input,
+}, index=[0])
+
+st.table(data_inf)
+
+if st.button(label='predict'):
+    # Melakukan prediksi data dummy
+    y_pred_inf = full_process.predict(data_inf)
+    st.metric(label="Prediksinya adalah:", value=y_pred_inf[0])
+
+run()  # Call the run function