Update app.py

app.py

@@ -3,7 +3,7 @@ import streamlit as st
 import scipy.stats
 import matplotlib.pyplot as plt
 from matplotlib import rc
-plt.style.use('fivethirtyeight')
+# plt.style.use('fivethirtyeight')
 
 st.subheader("Bayesian Coin Toss")
 st_col = st.columns(1)[0]
@@ -31,10 +31,10 @@ ax.set_xlabel('p(head)');
 # axs.text(h/N,2,'MLE', color='b')
 
 axs.plot(theta, [scipy.stats.beta.pdf(t, h+alpha, N-h+beta) for t in theta], color='r')
-ax.text(theta[N_theta//4], Likelihood[N_theta//4], 'Likelihood', color='b')
-axs.text(theta[3*N_theta//4], scipy.stats.beta.pdf(theta, alpha,beta)[3*N_theta//4],'Prior')
+ax.text(theta[N_theta//4], Likelihood[N_theta//4], 'Likelihood', color='b',zorder=12)
+axs.text(theta[3*N_theta//4], scipy.stats.beta.pdf(theta, alpha,beta)[3*N_theta//4],'Prior',zorder=11)
 # axs.text(alpha/(alpha+beta)-0.1,1,'Prior mean')
-axs.text(theta[N_theta//2],scipy.stats.beta.pdf(theta[N_theta//2], h+alpha, N-h+beta),'Posterior',color='r')
+axs.text(theta[N_theta//2],scipy.stats.beta.pdf(theta[N_theta//2], h+alpha, N-h+beta),'Posterior',color='r',zorder=10)
 # axs.text((h+alpha)/(N+alpha+beta)-0.1,3,'Post. Mean',color='r')
 # ax.vlines(alpha/(alpha+beta), *ax.get_ylim(), linestyle='--',label='Prior mean',color='k')
 # ax.vlines((h+alpha)/(N+alpha+beta), *ax.get_ylim(), linestyle='--',label='Post. Mean',color='r')