Update app.py

app.py

@@ -18,9 +18,9 @@ st.markdown(r"""
     """)
 
 
-st_col = st.columns(1)[0]
-
+st_col, st_met = st.columns([3,1])
 
+old_loss = 1000.0
 lengthscale = st.slider('Lengthscale', min_value=0.01, max_value=1.0, value=0.25, step=0.01)
 variance = st.slider('Variance', min_value=0.001, max_value=0.1, value=0.025, step=0.001)
 noise_variance = st.slider('Noise Variance', min_value=0.001, max_value=0.01, value=0.0, step=0.001)
@@ -60,11 +60,21 @@ ax.fill_between(x_test.ravel(), pred_mean.ravel()-pred_std2.ravel(),
                 pred_mean.ravel()+pred_std2.ravel(), alpha=0.5, label='$\\mu \\pm 2\\sigma$')
 ax.set_xlabel('x')
 ax.set_ylabel('y')
-ax.set_title(f"Negative Log Marginal Likelihood: {scipy.stats.multivariate_normal(np.zeros(3), k_train_with_noise).logpdf(y_train.ravel()):.4f}")
+# ax.set_title(f"Negative Log Marginal Likelihood: {scipy.stats.multivariate_normal(np.zeros(3), k_train_with_noise).logpdf(y_train.ravel()):.4f}")
 ax.legend()
 
 with st_col:
     st.pyplot(fig)
+new_loss = scipy.stats.multivariate_normal(np.zeros(3), k_train_with_noise).logpdf(y_train.ravel())
+new_loss_str = f"{new_loss:.4f}"
+
+def percentage_change():
+    ans = (new_loss - old_loss)/old_loss * 100
+    old_loss = new_loss
+    return f"{ans:.1f}%"
+
+with st_met:
+    st.metric(label='Loss', f"{new_loss:.4f}", percentage_change())
     
 hide_streamlit_style = """
             <style>