Update app.py

app.py

@@ -5,40 +5,72 @@ import pickle
 # Load trained model
 model = pickle.load(open("life_expectancy_model.pkl", "rb"))
 
-# Streamlit UI
+# Custom CSS for styling
+st.markdown("""
+    <style>
+        body {
+            background-color: #f5f5f5;
+        }
+        .stApp {
+            background-color: #ffffff;
+            border-radius: 15px;
+            padding: 20px;
+        }
+        .prediction-box {
+            background-color: #4CAF50;
+            padding: 15px;
+            border-radius: 10px;
+            color: white;
+            text-align: center;
+            font-size: 24px;
+            font-weight: bold;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# App Title with Emoji
 st.title("🌍 Life Expectancy Prediction App")
+st.markdown("### 📊 Predict the average life expectancy based on health and economic factors.")
 
-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]
+# Sidebar for Inputs
+st.sidebar.header("🔢 Enter Data Below")
+st.sidebar.markdown("Fill in the details to predict life expectancy.")
+
+# Input Fields in Sidebar
+year = st.sidebar.number_input("📅 Year", min_value=2000, max_value=2015, value=2008)
+status = st.sidebar.radio("🌍 Status", ["Developing", "Developed"])
+status = 1 if status == "Developed" else 0
+
+adult_mortality = st.sidebar.slider("⚰️ Adult Mortality Rate", 1.0, 723.0, 144.0)
+infant_deaths = st.sidebar.slider("👶 Infant Deaths", 0, 1800, 3)
+alcohol = st.sidebar.slider("🍷 Alcohol Consumption", 0.01, 17.87, 4.55)
+percentage_expenditure = st.sidebar.slider("💰 % Healthcare Expenditure", 0.0, 19479.91, 738.25)
+hepatitis_b = st.sidebar.slider("🦠 Hepatitis B Immunization (%)", 1, 99, 83)
+measles = st.sidebar.slider("🤒 Measles Cases", 0, 212183, 2419)
+bmi = st.sidebar.slider("⚖️ BMI", 1.0, 87.3, 38.3)
+under_five_deaths = st.sidebar.slider("⚰️ Under-Five Deaths", 0, 2500, 4)
+polio = st.sidebar.slider("💉 Polio Immunization (%)", 3, 99, 82)
+total_expenditure = st.sidebar.slider("🏥 Total Healthcare Expenditure (%)", 0.37, 17.6, 5.92)
+diphtheria = st.sidebar.slider("💉 Diphtheria Immunization (%)", 2, 99, 82)
+hiv_aids = st.sidebar.slider("🦠 HIV/AIDS Prevalence Rate", 0.1, 50.6, 1.74)
+gdp = st.sidebar.slider("💰 GDP per Capita", 1.68, 119172.7, 6611.52)
+population = st.sidebar.slider("👨‍👩‍👦 Population", 34, 1293859000, 10230850)
+thinness_1_19 = st.sidebar.slider("🧍‍♂️ Thinness 1-19 years (%)", 0.1, 27.7, 4.83)
+thinness_5_9 = st.sidebar.slider("🧍 Thinness 5-9 years (%)", 0.1, 28.6, 4.86)
+income_composition = st.sidebar.slider("💰 Income Composition of Resources", 0.0, 0.948, 0.63)
+schooling = st.sidebar.slider("🎓 Schooling (Years)", 0.0, 20.7, 11.99)
+
+# Predict Button with Animation
+if st.button("🔮 Predict Life Expectancy"):
+    with st.spinner("🔍 Analyzing data... Please wait!"):
+        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**")
+    # Displaying Results in a Styled Box
+    st.markdown(f'<div class="prediction-box">📌 Predicted Life Expectancy: <b>{prediction:.2f} years</b></div>', 
+                unsafe_allow_html=True)
+