Delete deployment.py

deployment.py

@@ -1,77 +0,0 @@
-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()