Several more cleanups

app.py

@@ -1,30 +1,34 @@
 import random
+import gradio
 import numpy
 import tensorflow
-from tensorflow.python.framework.ops import disable_eager_execution
 import math
+# from tensorflow.python.framework.ops import disable_eager_execution
 
 # Becuase important
-disable_eager_execution()
+tensorflow.python.framework.ops.disable_eager_execution()
 
 # Load the training and testing data
 load_data = numpy.load('data/train_test_split_data.npz')  # Data saved by the VAE
 
 # Convert Data to Tuples and Assign to respective variables
-box_matrix_train, box_density_train, additional_pixels_train, box_shape_train = tuple(load_data['box_matrix_train']), tuple(load_data['box_density_train']), tuple(load_data['additional_pixels_train']), tuple(load_data['box_shape_train'])
-box_matrix_test, box_density_test, additional_pixels_test, box_shape_test = tuple(load_data['box_matrix_test']), tuple(load_data['box_density_test']), tuple(load_data['additional_pixels_test']), tuple(load_data['box_shape_test'])
+box_matrix_train, box_density_train, additional_pixels_train, box_shape_train = tuple(
+    load_data['box_matrix_train']), tuple(load_data['box_density_train']), tuple(
+    load_data['additional_pixels_train']), tuple(load_data['box_shape_train'])
+box_matrix_test, box_density_test, additional_pixels_test, box_shape_test = tuple(load_data['box_matrix_test']), tuple(
+    load_data['box_density_test']), tuple(load_data['additional_pixels_test']), tuple(load_data['box_shape_test'])
 testX = box_matrix_test  # Shows the relationship to the MNIST Dataset vs the Shape Dataset
 image_size = numpy.shape(testX)[-1]  # Determines the size of the images
 test_data = numpy.reshape(testX, (len(testX), image_size, image_size, 1))
 
 # Creates tuples that contain all of the data generated
-allX = numpy.append(box_matrix_train,box_matrix_test, axis=0)
+allX = numpy.append(box_matrix_train, box_matrix_test, axis=0)
 all_box_density = numpy.append(box_density_train, box_density_test, axis=0)
-all_additional_pixels = numpy.append(additional_pixels_train, additional_pixels_test,axis=0)
-all_box_shape = numpy.append(box_shape_train, box_shape_test,axis=0)
+all_additional_pixels = numpy.append(additional_pixels_train, additional_pixels_test, axis=0)
+all_box_shape = numpy.append(box_shape_train, box_shape_test, axis=0)
 all_data = numpy.reshape(allX, (len(allX), image_size, image_size, 1))
 
-import math
+
 
 def basic_box_array(image_size):
     A = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
@@ -44,14 +48,16 @@ def back_slash_array(image_size):
                 A[i][j] = 1
     return A
 
+
 def forward_slash_array(image_size):
     A = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
     for i in range(image_size):
         for j in range(image_size):
-            if i == (image_size-1)-j:
+            if i == (image_size - 1) - j:
                 A[i][j] = 1
     return A
 
+
 def hot_dog_array(image_size):
     # Places pixels down the vertical axis to split the box
     A = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
@@ -61,6 +67,7 @@ def hot_dog_array(image_size):
                 A[i][j] = 1
     return A
 
+
 def hamburger_array(image_size):
     # Places pixels across the horizontal axis to split the box
     A = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
@@ -70,17 +77,18 @@ def hamburger_array(image_size):
                 A[i][j] = 1
     return A
 
+
 def center_array(image_size):
     A = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
     for i in range(image_size):
         for j in range(image_size):
-            if i == math.floor((image_size-1)/2) and j == math.ceil((image_size-1)/2):
+            if i == math.floor((image_size - 1) / 2) and j == math.ceil((image_size - 1) / 2):
                 A[i][j] = 1
-            if i == math.floor((image_size-1)/2) and j == math.floor((image_size-1)/2):
+            if i == math.floor((image_size - 1) / 2) and j == math.floor((image_size - 1) / 2):
                 A[i][j] = 1
-            if j == math.ceil((image_size-1)/2) and i == math.ceil((image_size-1)/2):
+            if j == math.ceil((image_size - 1) / 2) and i == math.ceil((image_size - 1) / 2):
                 A[i][j] = 1
-            if j == math.floor((image_size-1)/2) and i == math.ceil((image_size-1)/2):
+            if j == math.floor((image_size - 1) / 2) and i == math.ceil((image_size - 1) / 2):
                 A[i][j] = 1
     return A
 
@@ -103,7 +111,7 @@ def add_pixels(array_original, additional_pixels, image_size):
             for j in range(1, image_size - 1):
                 if A[i - 1][j] + A[i + 1][j] + A[i][j - 1] + A[i][j + 1] > 0:
                     A_updated[i][j] = 1
-        A = update_array(A, A_updated,image_size)
+        A = update_array(A, A_updated, image_size)
     return A
 
 
@@ -111,7 +119,7 @@ def basic_box(additional_pixels, density, image_size):
     A = basic_box_array(image_size)  # Creates the outside edges of the box
     # Increase the thickness of each part of the box
     A = add_pixels(A, additional_pixels, image_size)
-    return A*density
+    return A * density
 
 
 def horizontal_vertical_box_split(additional_pixels, density, image_size):
@@ -121,7 +129,7 @@ def horizontal_vertical_box_split(additional_pixels, density, image_size):
     A = update_array(A, hamburger_array(image_size), image_size)
     # Increase the thickness of each part of the box
     A = add_pixels(A, additional_pixels, image_size)
-    return A*density
+    return A * density
 
 
 def diagonal_box_split(additional_pixels, density, image_size):
@@ -134,7 +142,7 @@ def diagonal_box_split(additional_pixels, density, image_size):
     # Adds pixels to the thickness of each component of the box
     # Increase the thickness of each part of the box
     A = add_pixels(A, additional_pixels, image_size)
-    return A*density
+    return A * density
 
 
 def back_slash_box(additional_pixels, density, image_size):
@@ -223,18 +231,20 @@ def center_box(additional_pixels, density, image_size):
 
 
 import tensorflow as tf
+
 sess = tf.compat.v1.Session()
 
 from keras import backend as K
+
 K.set_session(sess)
 
 # Gradio Interface
 
-import gradio
 
-endpoint_types = ("basic_box", "diagonal_box_split", "horizontal_vertical_box_split", "back_slash_box", "forward_slash_box",
-          "back_slash_plus_box", "forward_slash_plus_box", "hot_dog_box", "hamburger_box", "x_hamburger_box",
-          "x_hot_dog_box", "x_plus_box")
+endpoint_options = (
+"basic_box", "diagonal_box_split", "horizontal_vertical_box_split", "back_slash_box", "forward_slash_box",
+"back_slash_plus_box", "forward_slash_plus_box", "hot_dog_box", "hamburger_box", "x_hamburger_box",
+"x_hot_dog_box", "x_plus_box")
 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]]
@@ -280,14 +290,15 @@ def interpolate(t1, t2, d1, d2, th1, th2, steps):
     predicted_interps = [number_1_expand[0, :, :, 0]]
 
     for latent_point in range(2, num_interp + 2):  # cycles the latent points through the decoder model to create images
-        generated_image = decoder_model_boxes.predict(numpy.array([latent_matrix[latent_point - 2]]))[0]  # generates an interpolated image based on the latent point
+        generated_image = decoder_model_boxes.predict(numpy.array([latent_matrix[latent_point - 2]]))[
+            0]  # generates an interpolated image based on the latent point
         predicted_interps.append(generated_image[:, :, -1])
 
     predicted_interps.append(number_2_expand[0, :, :, 0])
 
     transition_region = predicted_interps[0]
-    for i in range(len(predicted_interps)-1):
-        transition_region = numpy.hstack((transition_region, predicted_interps[i+1]))
+    for i in range(len(predicted_interps) - 1):
+        transition_region = numpy.hstack((transition_region, predicted_interps[i + 1]))
 
     return transition_region
 
@@ -299,13 +310,13 @@ def generate_unit_cell(t, d, th):
 with gradio.Blocks() as demo:
     with gradio.Row():
         with gradio.Column():
-            t1 = gradio.Dropdown(endpoint_types, label="Type 1", value=random.choice(endpoint_types))
+            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))
         with gradio.Column():
             img1 = gradio.Image(label="Endpoint 1")
         with gradio.Column():
-            t2 = gradio.Dropdown(endpoint_types, label="Type 2", value=random.choice(endpoint_types))
+            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))
         with gradio.Column():
@@ -316,10 +327,14 @@ with gradio.Blocks() as demo:
         lattice_inputs_2 = [t2, d2, th2]
         [x.change(fn=generate_unit_cell, inputs=lattice_inputs_2, outputs=[img2]) for x in lattice_inputs_2]
 
-    steps = gradio.Dropdown(interpolation_options, label="Interpolation Length", value=random.choice(interpolation_options))
+    steps = gradio.Dropdown(interpolation_options, label="Interpolation Length",
+                            value=random.choice(interpolation_options))
     btn = gradio.Button("Run")
     img = gradio.Image(label="Transition")
     btn.click(fn=interpolate, inputs=[t1, t2, d1, d2, th1, th2, steps], outputs=[img])
-    examples = gradio.Examples(examples=[["hamburger_box", "hot_dog_box", "1.00", "1.00", "2", "2", "20"], ["hamburger_box", "hot_dog_box", "0.10", "1.00", "10", "10", "5"]], fn=interpolate, inputs=[t1, t2, d1, d2, th1, th2, steps], outputs=[img], cache_examples = True)
+    examples = gradio.Examples(examples=[["hamburger_box", "hot_dog_box", "1.00", "1.00", "2", "2", "20"],
+                                         ["hamburger_box", "hot_dog_box", "0.10", "1.00", "10", "10", "5"]],
+                               fn=interpolate,
+                               inputs=[t1, t2, d1, d2, th1, th2, steps], outputs=[img], cache_examples=True)
 
-demo.launch()
+demo.launch()