add description to plots

app.py

@@ -127,6 +127,7 @@ def main():
         act_type = "softmax"
     st.write(WARNING_TEXT[language])
     show_pos_alpha = st.checkbox("Show positive alphas", value=False)
+    st.markdown("""In our method, we reweight the probability of the next token with the external classifier, namely, the Attribute model. If $\alpha$ parameter is equal to zero we can see that the distribution below collapses into a simple language model without any modification. If alpha is below zero then every generation step attribute model tries to minimize the probability of the desired attribute. Otherwise, the model is forced to produce text with a higher probability of the attribute.""")
     st.latex(r"p(x_i|x_{<i}, c) \propto p(x_i|x_{<i})p(c|x_{\leq i})^{\alpha}")
     if act_type == "softmax":
         alpha = st.slider("α", min_value=-40, max_value=40 if show_pos_alpha else 0, step=1, value=0)
@@ -151,6 +152,10 @@ def main():
     figure = go.Figure(data=[scatter, scatter_tip, scatter_tip_lines], layout=layout)
     figure.update_layout(paper_bgcolor="#FFFFFF", plot_bgcolor='#FFFFFF', showlegend=False)
     st.plotly_chart(figure, use_container_width=True)
+    with st.expander("What is it?"):
+        st.write("""
+            Text generation with an external classifier requires a huge amount of computation. Therefore text generating with CAIF could be slow. To overcome this issue, we can apply reweighting not for every step. Our hypothesis is that we can run reweighting only if entropy of the next token is above certain threshold. This strategy will reduce the amont of computation. Note that if entropy threshold is too high, we don't get desired attribute in generated text
+        """)
     auth_token = os.environ.get('TOKEN') or True
     fp16 = st.checkbox("FP16", value=True)
     st.session_state["generated_text"] = None
@@ -245,6 +250,7 @@ def inference(
             **kwargs
         )
         print(f"Output for prompt: {sequences}")
+
     return sequences[0]
 
 

generator.py

@@ -110,6 +110,8 @@ class Generator:
                 })
                 fig = go.Figure([scatter_data], layout=layout)
                 plot.plotly_chart(fig, use_container_width=True)
+                with st.expander("What is it?"):
+                    st.write("You can see how the probability of the desired attribute varies for every generation step.")
             text.text(current_decoded)
 
         return (