Generate and display endpoints

app.py

@@ -242,9 +242,10 @@ density_options = ["{:.2f}".format(x) for x in numpy.linspace(0.1, 1, 10)]
 thickness_options = [str(int(x)) for x in numpy.linspace(0, 10, 11)]
 interpolation_options = [str(int(x)) for x in [3, 5, 10, 20]]
 
+
 def interpolate(t1, t2, d1, d2, th1, th2, steps):
+    # Load the decoder model
     decoder_model_boxes = tensorflow.keras.models.load_model('data/decoder_model_boxes', compile=False)
-    # # compile=False ignores a warning from tensorflow, can be removed to see warning
 
     # # import the encoder model architecture
     json_file_loaded = open('data/model.json', 'r')
@@ -293,16 +294,32 @@ def interpolate(t1, t2, d1, d2, th1, th2, steps):
 
     return transition_region
 
+def generate_unit_cell(t, d, th):
+    number_1 = globals()[t](int(th), float(d), 28)
+
+    # resize the array to match the prediction size requirement
+    number_1_expand = np.expand_dims(np.expand_dims(number_1, axis=2), axis=0)
+
+    return number_1_expand[0, :, :, 0]
+
 with gradio.Blocks() as demo:
     with gradio.Row():
         with gradio.Column():
             t1 = gradio.Dropdown(endpoint_types, label="Type 1", value=random.choice(endpoint_types))
             d1 = gradio.Dropdown(density_options, label="Density 1", value=random.choice(density_options))
             th1 = gradio.Dropdown(thickness_options, label="Thickness 1", value=random.choice(thickness_options))
+            img1 = gradio.Image(label="Endpoint 1")
+            t1.change(fn=generate_unit_cell, inputs=[t1, d1, th1], outputs=[img1])
+            d1.change(fn=generate_unit_cell, inputs=[t1, d1, th1], outputs=[img1])
+            th1.change(fn=generate_unit_cell, inputs=[t1, d1, th1], outputs=[img1])
         with gradio.Column():
             t2 = gradio.Dropdown(endpoint_types, label="Type 2", value=random.choice(endpoint_types))
             d2 = gradio.Dropdown(density_options, label="Density 2", value=random.choice(density_options))
             th2 = gradio.Dropdown(thickness_options, label="Thickness 2", value=random.choice(thickness_options))
+            img2 = gradio.Image(label="Endpoint 2")
+            t2.change(fn=generate_unit_cell, inputs=[t2, d2, th2], outputs=[img1])
+            d2.change(fn=generate_unit_cell, inputs=[t2, d2, th2], outputs=[img1])
+            th2.change(fn=generate_unit_cell, inputs=[t2, d2, th2], outputs=[img1])
     steps = gradio.Dropdown(interpolation_options, label="Interpolation Length", value=random.choice(interpolation_options))
     btn = gradio.Button("Run")
     img = gradio.Image(label="Transition")