Update app.py

app.py

@@ -2,10 +2,8 @@ import streamlit as st
 import numpy as np
 import pickle
 
-# Load trained model
 model = pickle.load(open("life_expectancy_model.pkl", "rb"))
 
-# Custom Styling for Clean UI
 st.markdown("""
     <style>
         .stApp {
@@ -22,42 +20,50 @@ st.markdown("""
             text-align: center;
             font-size: 22px;
             font-weight: bold;
+            margin-top: 20px;
         }
     </style>
 """, unsafe_allow_html=True)
 
-# App Title
-st.markdown("<h1 class='title-container'>🌍 Life Expectancy Prediction</h1>", unsafe_allow_html=True)
-st.markdown("#### Predict the life expectancy based on health and economic factors.")
+st.markdown("<h1 class='title-container'>Life Expectancy Prediction</h1>", unsafe_allow_html=True)
+st.markdown("### Enter the required details to get the predicted life expectancy.")
 
-# Two-column Layout for Better Readability
+# Two-column Layout
 col1, col2 = st.columns(2)
 
 with col1:
-    year = st.number_input("📅 Year", min_value=2000, max_value=2015, value=2008)
-    status = st.radio("🌍 Status", ["Developing", "Developed"], horizontal=True)
+    year = st.number_input("Year", min_value=2000, max_value=2015, value=2008)
+    status = st.radio("Status", ["Developing", "Developed"], horizontal=True)
     status = 1 if status == "Developed" else 0
-    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("💰 % Healthcare 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)
+    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)
 
 with col2:
-    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)
-    hiv_aids = st.number_input("🦠 HIV/AIDS Prevalence Rate", min_value=0.1, max_value=50.6, value=1.74)
-    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"):
+    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)
+
+
+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, hiv_aids, schooling]])
+                          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]
 
-    # Display Result in a Stylish Box
-    st.markdown(f'<div class="result-box">📌 Predicted Life Expectancy: <b>{prediction:.2f} years</b></div>', 
+    st.markdown(f'<div class="result-box">Predicted Life Expectancy: <b>{prediction:.2f} years</b></div>', 
                 unsafe_allow_html=True)