Update main.py

main.py

@@ -1,8 +1,8 @@
-import numpy
-import keras
-import gradio
-import matplotlib.pyplot
-import matplotlib.colors
+import numpy # for miscellaneous
+import keras # for network
+import gradio # for interface
+import matplotlib.pyplot # for colormap
+import matplotlib.colors # for color conversion
 
 # Building the neural network
 model1 = keras.models.Sequential()
@@ -40,6 +40,7 @@ def scale(im, nR, nC):
              for c in range(nC)] for r in range(nR)])
 
 
+# Prediction function
 def predict(mask):
     scaled_mask = numpy.ones((101, 636)) if mask is None else numpy.round(scale(mask, 101, 636)/255.0)
     print(scaled_mask)
@@ -72,7 +73,6 @@ with gradio.Blocks() as demo:
     gradio.Markdown("It can be challenging to rapidly infer the stress and strain that are present in a material with a complex microstructure. This demo runs a rapid surrogate model to compute strain for the microstructure that you draw!")     
 
     mask = gradio.Image(image_mode="L", source="canvas", label="microstructure")    
-    mask.update(fn=predict, inputs=[mask], outputs=[exx, eyy, exy, legend])
 
     btn = gradio.Button("Run!", variant="primary")
     exx = gradio.Image(label="ε-xx")