Upload app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import tensorflow as tf
+from tensorflow import keras
+import matplotlib.pyplot as plt
+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, latent_dim=latent_dim):
+    random_vector_labels = generate_latent_points(digit, latent_dim, n_samples)
+    examples = cgan_generator.predict(random_vector_labels)
+    examples = examples * 255.0
+    size = ceil(sqrt(n_samples))
+    digit_images = np.zeros((28*size, 28*size))
+    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
+
+    return digit_images
+
+description = "This model is based on the example created here: https://keras.io/examples/generative/conditional_gan/"
+
+title = "Conditional GAN for MNIST"
+
+examples = [[1, 10], [3, 5], [5, 15]]
+
+
+iface = gr.Interface(
+    fn = create_digit_samples,
+    inputs = ["number", "number"],
+    outputs = [gradio.outputs.Image(invert_colors=True, type="numpy", label="Samples for given digit")],
+    examples = examples,
+    description = description,
+    title = title
+    )
+
+iface.launch()