Create app.py

app.py

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+import streamlit as st
+import joblib
+import numpy as np
+
+# Load the saved model and scaler
+model = joblib.load("medical_model.pkl")
+scaler = joblib.load("scaler.pkl")
+
+st.title("Medical Insurance Cost Predictor")
+st.write("Enter your details to estimate insurance charges.")
+
+# User Inputs
+age = st.number_input("Age", min_value=18, max_value=100, value=25)
+bmi = st.number_input("BMI", min_value=10.0, max_value=60.0, value=22.0)
+children = st.slider("Number of Children", 0, 5, 0)
+smoker = st.selectbox("Do you smoke?", ["Yes", "No"])
+sex = st.selectbox("Gender", ["Male", "Female"])
+
+# Map inputs to match the model's training format
+smoker_val = 1 if smoker == "Yes" else 0
+sex_val = 1 if sex == "Male" else 0
+
+# (Note: For simplicity, we assume region 'Southwest' as default here)
+# For full accuracy, you'd add region radio buttons matching your get_dummies columns.
+features = np.array([[age, sex_val, bmi, children, smoker_val, 0, 0, 0]])
+features_scaled = scaler.transform(features)
+
+if st.button("Predict Charges"):
+    prediction = model.predict(features_scaled)
+    st.success(f"Estimated Insurance Cost: ${prediction[0]:,.2f}")