app.py

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}")