Upload 4 files

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+
+with open('model_rf_model.pkl', 'rb') as file_1:
+  model_rf_model= joblib.load(file_1)
+
+with open('model_scaler.pkl', 'rb') as file_2:
+  model_scaler=joblib.load(file_2)
+
+with open('list_num_cols.txt', 'rb') as file_4:
+  num_cols= joblib.load(file_4)
+
+with open('list_cat_cols.txt', 'rb') as file_5:
+  cat_cols= joblib.load(file_5)
+
+
+age = st.slider('Masukan Umur:',0, 42, step=1)
+creatinine_phosphokinase = st.number_input('Masukan total creatinine phosphokinase :')
+anaemia = st.radio('Apakah anda mengidap Anaemia? 1=ya, 0=tidak',(0, 1))
+serum_creatinine = st.slider('Masukan Serum Creatinine:',0.0, 10.0)
+high_blood_pressure = st.radio('Apakah anda mengidap Hipertensi? 1=ya, 0=tidak',(0, 1))
+smoking = st.radio('Apakah anda merokok? 1=ya, 0=tidak',(0, 1))
+
+
+if st.button('Predict'):
+
+    data_inf = pd.DataFrame({
+    'age': age,
+    'creatinine_phosphokinase': creatinine_phosphokinase,
+    'anaemia': anaemia,
+    'serum_creatinine':serum_creatinine,
+    'high_blood_pressure': high_blood_pressure,
+    'smoking':smoking },index=0)
+    data_inf
+    # data_inf_scaled = model_scaler.transform(data_inf[num_cols])
+   
+
+    # data_inf_fix = np.concatenate([data_inf_scaled, data_inf[cat_cols]], axis=1)
+    # hasil = model_rf_model.predict(data_inf_fix)
+    # st.header(f'Death Event= ${hasil}')
+
+
+

model_scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9ef218bba754492ee0865ee6b28f800a21430cc55f437a018c7053df98f351bb
+size 999