Upload app.py

app.py

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+import streamlit as st
+import requests
+from PIL import Image
+
+image = Image.open('dataset-cover.jpg')
+st.title("Aplikasi Car Insurance")
+st.image(image)
+education_format = {0:"None",1:"High School",2:"University"}
+def ed_format(option):
+        return education_format[option]
+        
+income_format = {0:"Poverty",1:"Working Class",2:"Middle Class",3:"Upper Class"}
+def in_format(option):
+        return income_format[option]
+EDUCATION = st.selectbox("EDUCATION", options=list(education_format.keys()),format_func=ed_format)
+INCOME = st.selectbox("INCOME", options=list(income_format.keys()),format_func=in_format)
+CREDIT_SCORE = st.number_input("CREDIT_SCORE", min_value=0.00,max_value=0.99)
+ANNUAL_MILEAGE = st.number_input("ANNUAL_MILEAGE", min_value=2000,max_value=22000)
+SPEEDING_VIOLATIONS = st.number_input("SPEEDING_VIOLATIONS",min_value=0,max_value=22)
+PAST_ACCIDENTS = st.number_input("PAST_ACCIDENTS",min_value=0,max_value=15)
+DRIVING_EXPERIENCE = st.selectbox("DRIVING_EXPERIENCE", ['Newbie', 'Amateur', 'Advanced', 'Expert'])
+VEHICLE_OWNERSHIP = st.selectbox("VEHICLE_OWNERSHIP", ['Yes', 'No'])
+MARRIED = st.selectbox("MARRIED", ['Yes', 'No'])
+CHILDREN = st.selectbox("CHILDREN", ['Yes', 'No'])
+# inference
+data = {'EDUCATION':EDUCATION,
+        'INCOME':INCOME,
+        'CREDIT_SCORE': CREDIT_SCORE,
+        'ANNUAL_MILEAGE':ANNUAL_MILEAGE,
+        'SPEEDING_VIOLATIONS':SPEEDING_VIOLATIONS,
+        'PAST_ACCIDENTS':PAST_ACCIDENTS,
+        'DRIVING_EXPERIENCE' : DRIVING_EXPERIENCE,
+        'VEHICLE_OWNERSHIP' : VEHICLE_OWNERSHIP,
+        'MARRIED' : MARRIED,
+        'CHILDREN' : CHILDREN}
+
+URL = "https://andreean-backend-car-insurance.hf.space/"
+
+# komunikasi
+if st.button('Predict'):
+        r = requests.post(URL, json=data)
+        res = r.json()
+        if res['code'] == 200:
+                st.title(res['result']['classes'])