debug

app.py

@@ -16,6 +16,35 @@ st.markdown("## Full demo content will be posted here on December 7th!")
 
 make_header()
 
+
+from bokeh.layouts import column
+from bokeh.models import ColumnDataSource, CustomJS, Slider
+from bokeh.plotting import Figure, output_file, show
+x = [x*0.005 for x in range(0, 200)]
+y = x
+source = ColumnDataSource(data=dict(x=x, y=y))
+
+plot = Figure(width=400, height=400)
+plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
+
+callback = CustomJS(args=dict(source=source), code="""
+    const data = source.data;
+    const f = cb_obj.value
+    const x = data['x']
+    const y = data['y']
+    for (let i = 0; i < x.length; i++) {
+        y[i] = Math.pow(x[i], f)
+    }
+    source.change.emit();
+    alert("123");
+""")
+
+slider = Slider(start=0.1, end=4, value=1, step=.1, title="power")
+slider.js_on_change('value', callback)
+
+layout = column(slider, plot)
+st.bokeh_chart(layout)
+
 content_text(f"""
 There was a time when you could comfortably train SoTA vision and language models at home on your workstation.
 The first ConvNet to beat ImageNet took in 5-6 days on two gamer-grade GPUs{cite("alexnet")}. Today's top-1 imagenet model