.

app.py

@@ -15,7 +15,7 @@ st.set_page_config(
 
 st.title('T5-VAE 🙂😐🙁')
 
-st.text('''
+st.markdown('''
 This is a variational autoencoder trained on text.
 
 It allows interpolating on text at a high level, try it out!
@@ -23,7 +23,7 @@ It allows interpolating on text at a high level, try it out!
 See how it works [here](http://fras.uk/ml/large%20prior-free%20models/transformer-vae/2020/08/13/Transformers-as-Variational-Autoencoders.html).
 ''')
 
-st.text('''
+st.markdown('''
 ### t5-vae-python
 
 This model is trained on lines of Python code from GitHub ([dataset](https://huggingface.co/datasets/Fraser/python-lines).
@@ -111,7 +111,7 @@ def decode(cnt, ratio, txt_1, txt_2):
 
 in_1 = st.text_input("A line of Python code.", "x = a - 1")
 in_2 = st.text_input("Another line of Python code.", "x = a + 10 * 2")
-r = st.slider('Interpolation Ratio',  min_value=0.0, max_value=1.0, value=0.5)
+r = st.slider('Python Interpolation Ratio',  min_value=0.0, max_value=1.0, value=0.5)
 container = st.empty()
 container.write('Loading...')
 out = decode(container, r, in_1, in_2)
@@ -119,7 +119,7 @@ container.empty()
 st.write(out)
 
 
-st.text('''
+st.markdown('''
 ### t5-vae-wiki
 
 This model is trained on just 5% of the sentences on wikipedia.
@@ -141,7 +141,7 @@ model, tokenizer = get_wiki_model()
 
 in_1 = st.text_input("A sentence.", "Children are looking for the water to be clear.")
 in_2 = st.text_input("Another sentence.", "There are two people playing soccer.")
-r = st.slider('Interpolation Ratio',  min_value=0.0, max_value=1.0, value=0.5)
+r = st.slider('English Interpolation Ratio',  min_value=0.0, max_value=1.0, value=0.5)
 container = st.empty()
 container.write('Loading...')
 out = decode(r, in_1, in_2)
@@ -149,7 +149,7 @@ container.empty()
 st.write(out)
 
 
-st.text('''
+st.markdown('''
 Try arithmetic in latent space.
 
 Here latent codes for each sentence are found and arithmetic is done with them.
@@ -173,7 +173,7 @@ in_a = st.text_input("A", "Children are looking for the water to be clear.")
 in_b = st.text_input("B", "There are two people playing soccer.")
 in_c = st.text_input("C", "Children are looking for the water to be clear.")
 
-st.text('''
+st.markdown('''
 A is to B as C is to...
 ''')
 container = st.empty()