Update app.py

app.py

@@ -19,12 +19,11 @@ article = """
 
 def generate_latent_points(latent_dim, n_samples):
 	random_latent_vectors = tf.random.normal(shape=(n_samples, latent_dim))
-	return 
+	return random_latent_vectors
 
-def create_digit_samples(digit, n_samples):
-    if digit in range(10):
+def create_digit_samples(n_samples):
         latent_dim = 128
-        random_vector_labels = generate_latent_points(int(digit), latent_dim, int(n_samples))
+        random_vector_labels = generate_latent_points(latent_dim, int(n_samples))
         examples = model.predict(random_vector_labels)
         examples = examples * 255.0
         size = ceil(sqrt(n_samples))
@@ -39,9 +38,6 @@ def create_digit_samples(digit, n_samples):
         digit_images = (digit_images/127.5) -1
         return digit_images
 
-description = "Keras implementation for Conditional GAN to generate samples for specific digit of MNIST"
-article = "Author:<a href=\"https://huggingface.co/rajrathi\"> Rajeshwar Rathi</a>; Based on the keras example by <a href=\"https://keras.io/examples/generative/conditional_gan/\">Sayak Paul</a>"
-title = "Conditional GAN for MNIST"
 
 examples = [[1, 10], [3, 5], [5, 15]]