Better initial values

app.py

@@ -249,6 +249,10 @@ 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 generate_unit_cell(t, d, th):
+    return globals()[t](int(th), float(d), 28)
+
+
 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)
@@ -264,8 +268,8 @@ def interpolate(t1, t2, d1, d2, th1, th2, steps):
     encoder_model_boxes.load_weights('data/model_tf')
 
     num_internal = int(steps)
-    number_1 = globals()[t1](int(th1), float(d1), 28)
-    number_2 = globals()[t2](int(th2), float(d2), 28)
+    number_1 = generate_unit_cell(t1, d1, th1)
+    number_2 = generate_unit_cell(t2, d2, th2)
 
     # resize the array to match the prediction size requirement
     number_1_expand = numpy.expand_dims(numpy.expand_dims(number_1, axis=2), axis=0)
@@ -302,22 +306,18 @@ def interpolate(t1, t2, d1, d2, th1, th2, steps):
     return transition_region
 
 
-def generate_unit_cell(t, d, th):
-    return globals()[t](int(th), float(d), 28)
-
-
 with gradio.Blocks() as demo:
     with gradio.Row():
         with gradio.Column(min_width=250):
-            t1 = gradio.Dropdown(endpoint_options, label="Type 1", value=random.choice(endpoint_options))
-            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))
+            t1 = gradio.Dropdown(endpoint_options, label="Type 1", value="hamburger_box")
+            d1 = gradio.Dropdown(density_options, label="Density 1", value="1.00")
+            th1 = gradio.Dropdown(thickness_options, label="Thickness 1", value="2")
         with gradio.Column(min_width=250):
-            img1 = gradio.Image(label="Endpoint 1")
+            img1 = gradio.Image(label="Endpoint 1", value=)
         with gradio.Column(min_width=250):
-            t2 = gradio.Dropdown(endpoint_options, label="Type 2", value=random.choice(endpoint_options))
-            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))
+            t2 = gradio.Dropdown(endpoint_options, label="Type 2", value="hot_dog_box")
+            d2 = gradio.Dropdown(density_options, label="Density 2", value="1.00")
+            th2 = gradio.Dropdown(thickness_options, label="Thickness 2", value="2")
         with gradio.Column(min_width=250):
             img2 = gradio.Image(label="Endpoint 2")