Update app.py

app.py

@@ -1,10 +1,53 @@
 import streamlit as st
-
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
-
 import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import seaborn as sns
+import tensorflow_probability as tfp
+import pandas as pd
+tfd = tfp.distributions
+
+st.title("1 dimensional normal distribution")
+mean = st.slider('Mean', -5, 5, 0)
+std = st.slider('Scale', 0, 5, 1)
+
+p = tfd.Normal(2, 1)
+
+
+st.latex(f"\mu={mean}"+"\n\n"+f"\sigma={std}")
+
+
+q = tfd.Normal(mean, std)
+z_values = tf.linspace(-5, 5, 200)
+z_values = tf.cast(z_values, tf.float32)
+prob_values_p = p.prob(z_values)
+prob_values_q = q.prob(z_values)
+
 fig, ax = plt.subplots()
-ax.plot(np.random.randn(10, 5))
+ax.plot(z_values, prob_values_p, label=r'p', linestyle='--', lw=5, alpha=0.5)
+ax.plot(z_values, prob_values_q, label=r'q')
+
+ax.set_xlabel("x")
+ax.set_ylabel("PDF(x)")
+ax.legend()
+ax.set_ylim((0, 1))
+
+sns.despine()
+
+kl = tfd.kl_divergence(q, p)
+st.latex(f"D_{{KL}}(q||p) is : {kl:0.2f}")
+
+#arr = np.random.normal(mean, std, size=10000)
+
+##ax.hist(arr, bins=20)
+#sns.despine()
 st.pyplot(fig)
+hide_streamlit_style = """
+            <style>
+            #MainMenu {visibility: hidden;}
+            footer {visibility: hidden;}
+            </style>
+            """
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)