Update app.py

app.py

@@ -13,43 +13,39 @@ alpha = st.slider('Alpha', 0.5, 5.0, 0.5)
 beta = st.slider('Beta', 0.5, 5.0, 0.5)
 
 theta = np.linspace(0.01,0.99,100)
-rc('font', size=20)
+#rc('font', size=20)
 # rc('text', usetex=True)
-fig, ax = plt.subplots(figsize=(12,6))
+fig, ax = plt.subplots()
 axs = ax.twinx()
 
 def Bernoulli(theta, N, h):
     return (theta ** h) * ((1-theta) ** (N-h))
 
-def coin_toss(N, h, alpha, beta):
-    Likelihood = [Bernoulli(t,N,h) for t in theta]
-    
-    ax.cla();axs.cla()
-    ax.plot(theta, Likelihood, label='Likelihood',color='b');
-    axs.plot(theta, scipy.stats.beta.pdf(theta, alpha,beta), label='Prior',color='k');
-    ax.set_xlabel('p(head)');
-    ax.vlines(h/N, *ax.get_ylim(), linestyle='--',label='MLE', color='b')
-    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[0], Likelihood[0],'Likelihood', color='b')
-    axs.text(theta[-5], scipy.stats.beta.pdf(theta, alpha,beta)[-5],'Prior')
-    axs.text(alpha/(alpha+beta)-0.1,1,'Prior mean')
-    axs.text(theta[0],scipy.stats.beta.pdf(theta[0], h+alpha, N-h+beta),'Posterior',color='r')
-    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')
-    # ax.set_title(f"n_samples={int(N)}, n_heads={int(h)}");
-    ax.tick_params(axis='y', colors='b')
-    axs.tick_params(axis='y', colors='r')
-    ax.set_ylabel('Likelihood',color='b')
-    axs.set_ylabel('Prior/Posterior', color='r', rotation=270, labelpad=30)
-    return fig
-
-
+Likelihood = [Bernoulli(t,N,h) for t in theta]
+
+ax.cla();axs.cla()
+ax.plot(theta, Likelihood, label='Likelihood',color='b');
+axs.plot(theta, scipy.stats.beta.pdf(theta, alpha,beta), label='Prior',color='k');
+ax.set_xlabel('p(head)');
+ax.vlines(h/N, *ax.get_ylim(), linestyle='--',label='MLE', color='b')
+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[0], Likelihood[0],'Likelihood', color='b')
+axs.text(theta[-5], scipy.stats.beta.pdf(theta, alpha,beta)[-5],'Prior')
+axs.text(alpha/(alpha+beta)-0.1,1,'Prior mean')
+axs.text(theta[0],scipy.stats.beta.pdf(theta[0], h+alpha, N-h+beta),'Posterior',color='r')
+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')
+# ax.set_title(f"n_samples={int(N)}, n_heads={int(h)}");
+ax.tick_params(axis='y', colors='b')
+axs.tick_params(axis='y', colors='r')
+ax.set_ylabel('Likelihood',color='b')
+axs.set_ylabel('Prior/Posterior', color='r', rotation=270, labelpad=30)
 
 with st_col:
-    st.pyplot(coin_toss(n_samples, n_heads, alpha, beta))
+    st.pyplot(fig)
     
 hide_streamlit_style = """
             <style>