Update src/streamlit_app.py

src/streamlit_app.py

@@ -3,100 +3,65 @@ import pandas as pd
 import joblib
 import os
 
-# ======================
-# LOAD MODEL
-# ======================
 BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 model = joblib.load(os.path.join(BASE_DIR, "model.pkl"))
 
-# ======================
-# PAGE CONFIG
-# ======================
-st.set_page_config(
-    page_title="Porto Seguro Safe Driver Prediction",
-    page_icon="🚗",
-    layout="centered"
-)
+st.set_page_config(page_title="Porto Seguro Prediction", page_icon="🚗")
 
 st.title("🚗 Porto Seguro – Safe Driver Prediction")
-st.write("Predict the probability that a driver will file an insurance claim.")
 
-# ======================
-# FRIENDLY NAMES
-# ======================
-friendly_names = {
-    "ps_ind_01": "Driver Age",
-    "ps_ind_02_cat": "Gender",
-    "ps_ind_03": "Driving Experience",
-    "ps_ind_04_cat": "Education Level",
-    "ps_ind_05_cat": "Employment Type",
-    "ps_ind_06_bin": "Has Previous Claims",
-    "ps_ind_07_bin": "Owns a Car",
-    "ps_ind_08_bin": "Has Driving License",
-    "ps_ind_09_bin": "Has Children",
-    "ps_ind_10_bin": "Has Insurance History"
-}
+feature_names = model.feature_names_in_
 
 # ======================
-# INPUT FEATURES
+# USER INPUTS (BELANGRIJKSTE)
 # ======================
-st.sidebar.header("Driver Information")
 
-input_data = {}
+st.sidebar.header("Driver Profile")
 
-if hasattr(model, "feature_names_in_"):
+ps_ind_01 = st.sidebar.slider("Driver Age", 18, 80, 35)
+ps_ind_03 = st.sidebar.slider("Driving Experience", 0, 20, 5)
 
-    feature_names = model.feature_names_in_
+st.sidebar.header("Insurance History")
 
-    for feature in feature_names:
+ps_ind_06_bin = st.sidebar.selectbox("Has Previous Claims", [0, 1])
+ps_ind_10_bin = st.sidebar.selectbox("Has Insurance History", [0, 1])
 
-        label = friendly_names.get(feature, feature)
+st.sidebar.header("Vehicle & License")
 
-        # Binary → dropdown
-        if "_bin" in feature:
-            input_data[feature] = st.sidebar.selectbox(label, [0, 1])
+ps_ind_07_bin = st.sidebar.selectbox("Owns a Car", [0, 1])
+ps_ind_08_bin = st.sidebar.selectbox("Has Driving License", [0, 1])
 
-        # Categorical → integer input
-        elif "_cat" in feature:
-            input_data[feature] = st.sidebar.number_input(label, value=0)
+# ======================
+# AUTO FILL REST
+# ======================
 
-        # Numeric
-        else:
-            input_data[feature] = st.sidebar.number_input(label, value=0.0)
+input_data = dict.fromkeys(feature_names, 0)
 
-    input_df = pd.DataFrame([input_data])
+input_data["ps_ind_01"] = ps_ind_01
+input_data["ps_ind_03"] = ps_ind_03
+input_data["ps_ind_06_bin"] = ps_ind_06_bin
+input_data["ps_ind_10_bin"] = ps_ind_10_bin
+input_data["ps_ind_07_bin"] = ps_ind_07_bin
+input_data["ps_ind_08_bin"] = ps_ind_08_bin
 
-else:
-    st.error("Model does not contain feature names.")
-    st.stop()
+input_df = pd.DataFrame([input_data])
 
-# ======================
-# SHOW INPUT
-# ======================
 st.subheader("Input Data")
 st.write(input_df)
 
 # ======================
-# PREDICTION
+# PREDICT
 # ======================
-if st.button("Predict"):
-
-    prediction = model.predict(input_df)[0]
 
-    st.subheader("Result")
-
-    if hasattr(model, "predict_proba"):
-
-        probability = model.predict_proba(input_df)[0][1]
+if st.button("Predict"):
 
-        st.metric("Claim Probability", f"{probability:.2%}")
+    prob = model.predict_proba(input_df)[0][1]
 
-        if probability > 0.7:
-            st.error("🔴 High Risk Driver")
-        elif probability > 0.4:
-            st.warning("🟡 Medium Risk Driver")
-        else:
-            st.success("🟢 Low Risk Driver")
+    st.metric("Claim Probability", f"{prob:.2%}")
 
+    if prob > 0.7:
+        st.error("🔴 High Risk Driver")
+    elif prob > 0.4:
+        st.warning("🟡 Medium Risk Driver")
     else:
-        st.success(f"Prediction: {prediction}")
+        st.success("🟢 Low Risk Driver")