loading

app.py

@@ -80,7 +80,7 @@ def data_comparison(df):
     ).interactive()
 
     legend = alt.Chart(df).mark_point().encode(
-        y=alt.Y('cluster:N', axis=alt.Axis(orient='right'), title=""),
+        y=alt.Y('cluster:N', axis=alt.Axis(orient='left'), title=""),
         x=alt.X("label"),
         shape=alt.Shape('label', scale=alt.Scale(
         range=['circle', 'diamond']), legend=None),
@@ -89,7 +89,7 @@ def data_comparison(df):
         selection
     )
     
-    layered =  scatter | legend
+    layered = legend | scatter
 
     layered = layered.configure_axis(
         grid=False
@@ -99,7 +99,7 @@ def data_comparison(df):
 
     return layered
 
-
+@st.cache(ttl=600)
 def quant_panel(embedding_df):
     """ Quantitative Panel Layout"""
 
@@ -112,6 +112,7 @@ def quant_panel(embedding_df):
         st.markdown("* The **shape** of each point reflects the label category --  positive (diamond) or negative sentiment (circle).")
     st.altair_chart(data_comparison(down_samp(embedding_df)))
 
+@st.cache(ttl=600)
 def frequent_tokens(data, tokenizer, loss_quantile=0.95, top_k=200, smoothing=0.005):
     unique_tokens = []
     tokens = []
@@ -171,6 +172,7 @@ def clustering(data,num_clusters):
 
     return data, assigned_clusters
 
+@st.cache(ttl=600)
 def kmeans(df, num_clusters=3):
     data_hl = df.loc[df['slice'] == 'high-loss']
     data_kmeans,clusters = clustering(data_hl,num_clusters)
@@ -276,10 +278,11 @@ if __name__ == "__main__":
         st.write(dataframe,width=900, height=300)
     
     with rcol:
-        st.markdown('<h3>Word Distribution in Error Slice</h3>', unsafe_allow_html=True)
-        commontokens = frequent_tokens(merged, tokenizer, loss_quantile=loss_quantile)
-        with st.expander("How to read the table:"):
-            st.markdown("* The table displays the most frequent tokens in error slices, relative to their frequencies in the val set.")
-        st.write(commontokens)
-
-    quant_panel(merged)
+        with st.spinner(text='loading...'):
+            st.markdown('<h3>Word Distribution in Error Slice</h3>', unsafe_allow_html=True)
+            commontokens = frequent_tokens(merged, tokenizer, loss_quantile=loss_quantile)
+            with st.expander("How to read the table:"):
+                st.markdown("* The table displays the most frequent tokens in error slices, relative to their frequencies in the val set.")
+            st.write(commontokens)
+    with st.spinner(text='visualizing...'):
+        quant_panel(merged)