Delete the last plot

app.py

@@ -234,6 +234,8 @@ def build_donut_plot():
 
   return fig
 
+#def build_figures_cached(graph_objs):
+  #return [go.Figure(
 
 def main():
   # load the data
@@ -407,40 +409,56 @@ Later on,
 got me out of a jam.
 This `empet` character is pretty much the only one who answers Python posts on Plotly's forums.
 As far as I can tell, that's because they're the only person in the world who understands Plotly's Python library.
-""")
 
-  st.markdown('## Check out the data!')
-  st.markdown("""
-This plot is similar to the plot above, but the slider here coordinates with a table of the data I extracted to plot each tree.
+I'm glad it was challenging, though.
+I did go into this hoping for something more interesting than a donut plot.
+Maybe I'll think on the `value` and `gain` fields a bit and come up with a version 2.
 """)
 
+  #st.markdown("""
+#This plot is similar to the plot above, but the slider here coordinates with a table of the data I extracted to plot each tree.
+#""")
+
   # This works the way I want
   # but the plot is tiny
   # also it recalcualtes all of the plots
   # every time the slider value changes
+  # 
+  # This seems to be affecting the animation too
+  # so I'm going to leave it out
+  # It's the largest this by far in the flame graph
   #
   # I tried to cache the plots but build_plot() takes
   # a DataFrame which is mutable and therefore unhashable I guess
   # so it won't let me cache that function
   # I could pack the dataframe bytes to smuggle them past that check
   # but whatever
-  idx = st.slider(
-    label='Which tree do you want to see?',
-    min_value=0,
-    max_value=len(figures)-1,
-    value=0,
-    step=1
-  )
-  st.markdown(f'### Tree {idx}')
-  st.plotly_chart(figures[idx])
-  st.dataframe(trees[idx])
-  st.markdown("""
-This section is mostly just to warn you against making the same foolhardy decision to marry the innermost guts of SciKit-Learn to the sparsely documented world of Plotly animations in Python.
-
-I'm glad it was challenging, though.
-I did go into this hoping for something more interesting than a donut plot.
-Maybe I'll think on the `value` and `gain` fields a bit and come up with a version 2.
-""")
+  #idx = st.slider(
+    #label='Which tree do you want to see?',
+    #min_value=0,
+    #max_value=len(figures)-1,
+    #value=0,
+    #step=1
+  #)
+  #st.markdown(f'### Tree {idx}')
+  #st.plotly_chart(figures[idx])
+  #st.dataframe(trees[idx])
+  #st.markdown("""
+#This section is mostly just to warn you against making the same foolhardy decision to marry the innermost guts of SciKit-Learn to the sparsely documented world of Plotly animations in Python.
+#
+#""")
+
+  # This is still super slow even if it's only showing the dataframes
+  # I'm just going to leave it out entirely
+  #st.markdown('## Check out the data!')
+  #idx = st.slider(
+    #label='Which tree do you want to see?',
+    #min_value=0,
+    #max_value=len(figures)-1,
+    #value=0,
+    #step=1,
+  #)
+  #st.dataframe(trees[idx])