Update app.py

app.py

@@ -1,56 +1,56 @@
-import gradio as gr
-import tensorflow as tf
-from tensorflow import keras
-from math import sqrt, ceil
-
-from huggingface_hub import from_pretrained_keras
-
-import numpy as np
-
-
-model = from_pretrained_keras("keras-io/conditional-gan")
-
-latent_dim = 128
-
-def generate_latent_points(digit, latent_dim, n_samples, n_classes=10):
-	# generate points in the latent space
-	random_latent_vectors = tf.random.normal(shape=(n_samples, latent_dim))
-	labels = tf.keras.utils.to_categorical([digit for _ in range(n_samples)], n_classes)
-	return tf.concat([random_latent_vectors, labels], 1)
-
-def create_digit_samples(digit, n_samples):
-    if digit in range(10):
-        latent_dim = 128
-        random_vector_labels = generate_latent_points(int(digit), latent_dim, int(n_samples))
-        examples = model.predict(random_vector_labels)
-        examples = examples * 255.0
-        size = ceil(sqrt(n_samples))
-        digit_images = np.zeros((28*size, 28*size), dtype=float)
-        n = 0
-        for i in range(size):
-            for j in range(size):
-                if n == n_samples:
-                    break
-                digit_images[i* 28 : (i+1)*28, j*28 : (j+1)*28] = examples[n, :, :, 0]
-                n += 1
-        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]]
-
-
-iface = gr.Interface(
-    fn = create_digit_samples,
-    inputs = ["number", "number"],
-    outputs = ["image"],
-    examples = examples,
-    description = description,
-    title = title,
-    article = article
-    )
-
+import gradio as gr
+import tensorflow as tf
+from tensorflow import keras
+from math import sqrt, ceil
+
+from huggingface_hub import from_pretrained_keras
+
+import numpy as np
+
+
+model = from_pretrained_keras("keras-io/conditional-gan")
+
+latent_dim = 128
+
+def generate_latent_points(digit, latent_dim, n_samples, n_classes=10):
+	# generate points in the latent space
+	random_latent_vectors = tf.random.normal(shape=(n_samples, latent_dim))
+	labels = tf.keras.utils.to_categorical([digit for _ in range(n_samples)], n_classes)
+	return tf.concat([random_latent_vectors, labels], 1)
+
+def create_digit_samples(digit, n_samples):
+    if digit in range(10):
+        latent_dim = 128
+        random_vector_labels = generate_latent_points(int(digit), latent_dim, int(n_samples))
+        examples = model.predict(random_vector_labels)
+        examples = examples * 255.0
+        size = ceil(sqrt(n_samples))
+        digit_images = np.zeros((28*size, 28*size), dtype=float)
+        n = 0
+        for i in range(size):
+            for j in range(size):
+                if n == n_samples:
+                    break
+                digit_images[i* 28 : (i+1)*28, j*28 : (j+1)*28] = examples[n, :, :, 0]
+                n += 1
+        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 = "cGAN MNIST"
+
+examples = [[1, 10], [3, 5], [5, 15]]
+
+
+iface = gr.Interface(
+    fn = create_digit_samples,
+    inputs = ["number", "number"],
+    outputs = ["image"],
+    examples = examples,
+    description = description,
+    title = title,
+    article = article
+    )
+
 iface.launch()