Update app.py

app.py

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+import streamlit as st
+import numpy as np
+import pickle
+
+# Load trained model
+model = pickle.load(open("life_expectancy_model.pkl", "rb"))
+
+# Streamlit UI
+st.title("🌍 Life Expectancy Prediction App")
+
+st.markdown("Enter the values below to predict the Life Expectancy.")
+
+# Input Fields with Proper Ranges
+year = st.number_input("Year", min_value=2000, max_value=2015, value=2008)
+status = st.selectbox("Status (0: Developing, 1: Developed)", [0, 1])
+adult_mortality = st.number_input("Adult Mortality Rate", min_value=1.0, max_value=723.0, value=144.0)
+infant_deaths = st.number_input("Infant Deaths", min_value=0, max_value=1800, value=3)
+alcohol = st.number_input("Alcohol Consumption", min_value=0.01, max_value=17.87, value=4.55)
+percentage_expenditure = st.number_input("Percentage Expenditure", min_value=0.0, max_value=19479.91, value=738.25)
+hepatitis_b = st.number_input("Hepatitis B Immunization (%)", min_value=1, max_value=99, value=83)
+measles = st.number_input("Measles Cases", min_value=0, max_value=212183, value=2419)
+bmi = st.number_input("BMI", min_value=1.0, max_value=87.3, value=38.3)
+under_five_deaths = st.number_input("Under-Five Deaths", min_value=0, max_value=2500, value=4)
+polio = st.number_input("Polio Immunization (%)", min_value=3, max_value=99, value=82)
+total_expenditure = st.number_input("Total Healthcare Expenditure (%)", min_value=0.37, max_value=17.6, value=5.92)
+diphtheria = st.number_input("Diphtheria Immunization (%)", min_value=2, max_value=99, value=82)
+hiv_aids = st.number_input("HIV/AIDS Prevalence Rate", min_value=0.1, max_value=50.6, value=1.74)
+gdp = st.number_input("GDP per Capita", min_value=1.68, max_value=119172.7, value=6611.52)
+population = st.number_input("Population", min_value=34, max_value=1293859000, value=10230850)
+thinness_1_19 = st.number_input("Thinness 1-19 years (%)", min_value=0.1, max_value=27.7, value=4.83)
+thinness_5_9 = st.number_input("Thinness 5-9 years (%)", min_value=0.1, max_value=28.6, value=4.86)
+income_composition = st.number_input("Income Composition of Resources", min_value=0.0, max_value=0.948, value=0.63)
+schooling = st.number_input("Schooling (Years)", min_value=0.0, max_value=20.7, value=11.99)
+
+# Predict button
+if st.button("Predict Life Expectancy"):
+    features = np.array([[year, status, adult_mortality, infant_deaths, alcohol, percentage_expenditure,
+                          hepatitis_b, measles, bmi, under_five_deaths, polio, total_expenditure,
+                          diphtheria, hiv_aids, gdp, population, thinness_1_19, thinness_5_9,
+                          income_composition, schooling]])
+    
+    prediction = model.predict(features)[0]
+    
+    st.success(f"📌 **Predicted Life Expectancy: {prediction:.2f} years**")