Add app and requirements

app.py

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+# Import dependencies
+import tensorflow as tf
+from tensorflow import keras
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+import gradio as gr
+import os
+
+(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.mnist.load_data()
+
+class Sampling(tf.keras.layers.Layer):
+    def call(self, inputs):
+        mean, log_var = inputs
+        return tf.random.normal(tf.shape(log_var)) * tf.exp(log_var / 2) + mean 
+
+custom_objects = {'Sampling': Sampling}
+
+variational_ae = keras.models.load_model('./vae_autoencoder.keras', custom_objects=custom_objects)
+variational_encoder = keras.models.load_model('./vae_encoder.keras', custom_objects=custom_objects)
+variational_decoder = keras.models.load_model('./vae_decoder.keras', custom_objects=custom_objects)
+
+def get_center_of_mass(df):
+    cms = []
+    labels = df.label.unique()
+    for label in sorted(labels):
+        cm = df[df.label == label][df.columns[:-1]].sum() / df[df.label == label].shape[0]
+        cms.append(cm.values)
+    return cms
+
+_, _, X_test_encoded = variational_encoder(X_test.reshape(-1, 28, 28, 1))
+df = pd.concat([pd.DataFrame(X_test_encoded), pd.DataFrame(y_test, columns=['label'])], axis=1)
+cms = get_center_of_mass(df)
+imgs_cm = [np.clip(variational_decoder(cms[i].reshape(1, -1))[0], 0.0, 255.0) for i in range(10)]
+
+many = []
+for i in range(10):#range(4, len(cms) - 5):
+    A, B = cms[i - 1].copy(), cms[i].copy()
+    versor = (B - A) / np.linalg.norm(B - A)
+    d = np.linspace(0, np.linalg.norm(B - A), 100)
+    for i in range(100):
+        row = A + d[i] * versor
+        many.append(row.copy())
+
+many = tf.convert_to_tensor(tf.squeeze(np.array(many)))
+images = variational_decoder(many).numpy()
+images_converted = []
+
+for x in images:
+    new_x = ((x - x.min()) / (x - x.min()).max()) * 255
+    new_x = new_x.astype(np.uint8)
+    images_converted.append(new_x)
+
+images_converted = np.array(images_converted)
+
+
+def image_classifier(value):
+    return np.clip(((variational_decoder(many[(value * 10)].numpy().reshape(1, -1))[0]) * 255), 0, 255).astype(int)[:, :, 0]
+
+input_value_d = gr.Slider(minimum=0, maximum=99, step=1)
+demo = gr.Interface(fn=image_classifier, inputs=input_value_d, outputs="image", live=True)
+
+# Launch the interface
+if __name__ == "__main__":
+    demo.launch(server_port=501, debug=True, server_name='localhost')

requirements.txt

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+tensorflow
+pandas
+numpy
+gradio
+matplotlib