Upload app.py

app.py

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+import streamlit as st
+import pandas as pd
+import joblib
+
+st.header('FTDS Model Deployment')
+
+st.write("""
+Created by Maria Melisa Gunawan
+
+Use the sidebar to select input features.
+""")
+
+@st.cache
+def fetch_data():
+    df = pd.read_csv('P1G5_Set_1_Melisa.csv')
+    return df
+
+df = fetch_data()
+st.write(df)
+
+st.sidebar.header('User Input Features')
+
+# Fungsi untuk mengambil input dari pengguna
+def user_input():
+    pay_0 = st.sidebar.number_input('Payment Status in September (pay_0)', value=80000)
+    pay_2 = st.sidebar.number_input('Payment Status in August (pay_2)', value=20000)
+    pay_3 = st.sidebar.number_input('Payment Status in July (pay_3)', value=3000)
+    pay_4 = st.sidebar.number_input('Payment Status in June (pay_4)', value=45000)
+    pay_5 = st.sidebar.number_input('Payment Status in May (pay_5)', value=500)
+    pay_6 = st.sidebar.number_input('Payment Status in April (pay_6)', value=2500)
+    limit_balance = st.sidebar.number_input('Credit Limit (limit_balance)', value=90000)
+    default_payment_next_month = st.sidebar.selectbox('Default Payment Next Month', ['No', 'Yes'])
+
+    # Mapping 'No' to 0 and 'Yes' to 1
+    default_payment_next_month = 1 if default_payment_next_month == 'Yes' else 0
+
+    data = {
+        'pay_0': pay_0,
+        'pay_2': pay_2,
+        'pay_3': pay_3,
+        'pay_4': pay_4,
+        'pay_5': pay_5,
+        'pay_6': pay_6,
+        'limit_balance': limit_balance,
+        'default_payment_next_month': default_payment_next_month
+    }
+    features = pd.DataFrame(data, index=[0])
+    return features
+
+# Memuat model yang telah di-train
+load_model = joblib.load("credit_card_default_model.pkl")
+
+# Menjalankan aplikasi Streamlit
+def main():
+    st.title('Default Payment Next Month')
+
+    # Mengambil input dari pengguna
+    input_features = user_input()
+
+    # Menampilkan input pengguna
+    st.subheader('User Input')
+    st.write(input_features)
+
+    # Melakukan prediksi menggunakan model
+    prediction = load_model.predict(input_features)
+
+    if prediction == 1:
+        prediction = 'Default'
+    else:
+        prediction = 'Not Default'
+
+    # Menampilkan hasil prediksi
+    st.subheader('Prediction')
+    st.write(f'Based on user input, the model predicts: {prediction}')
+
+if __name__ == '__main__':
+    main()