Create app.py

app.py

@@ -0,0 +1,59 @@
+import streamlit as st
+import pandas as pd
+import joblib
+
+st.header('FTDS Model Deployment')
+st.write("""
+Created by FTDS Curriculum Team
+Use the sidebar to select input features.
+""")
+
+@st.cache
+def fetch_data():
+    df = pd.read_csv('P1G5_Set_1_Muhammad-Insani.csv')
+    return df
+
+df = fetch_data()
+st.write(df)
+
+st.sidebar.header('User Input Features')
+
+def user_input():
+    limit_balance = st.sidebar.selectbox('limit_balance', df['limit_balance'].unique())
+    pay_1 = st.slider('pay_1', min_value=-12, max_value=12)
+    pay_2 = st.slider('pay_2', min_value=-12, max_value=12)
+    pay_3 = st.slider('pay_3', min_value=-12, max_value=12)
+    pay_4 = st.slider('pay_4', min_value=-12, max_value=12)
+    pay_5 = st.slider('pay_5', min_value=-12, max_value=12)
+    pay_6 = st.slider('pay_6', min_value=-12, max_value=12)
+
+    data = {
+        'limit_balance': limit_balance,
+        'pay_1': pay_1,
+        'pay_2': pay_2,
+        'pay_3': pay_3,
+        'pay_4': pay_4,
+        'pay_5': pay_5,
+        'pay_6': pay_6
+    }
+    features = pd.DataFrame(data, index=[0])
+    return features
+
+
+input = user_input()
+
+st.subheader('User Input')
+st.write(input)
+
+load_model = joblib.load("my_model.pkl")
+
+if st.button("Predict"):
+    prediction = load_model.predict(input)
+
+    if prediction == 1:
+        prediction = 'Defaulted Payment'
+    else:
+        prediction = 'Not Defaulted'
+
+    st.subheader('Based on user input, the placement model predicted: ')
+    st.header(prediction)