Create app.py

app.py

@@ -0,0 +1,36 @@
+def create_digit_samples(num_images):
+      random_latent_vectors = tf.random.normal(shape=(int(num_images), 128))
+      predictions = model.predict(random_latent_vectors)
+      num = ceil(sqrt(num_images))
+      digit_images = np.zeros((28*num, 28*num), dtype=float)
+      n = 0
+      for i in range(num):
+          for j in range(num):
+              if n == num_images:
+                  break
+              digit_images[i* 28 : (i+1)*28, j*28 : (j+1)*28] = predictions[n, :, :, 0]
+              n += 1
+      return digit_images
+
+title = "WGAN-GP"
+description = "Image Generation Using WGAN"
+article = """
+<p style='text-align: center'>
+            <a href='https://keras.io/examples/generative/wgan_gp/' target='_blank'>Keras Example given by A_K_Nain</a>
+            <br>
+            Space by Gitesh Chawda
+        </p>
+    """
+inputs = gr.inputs.Number(label="number of images")
+outputs = gr.outputs.Image(label="Predictions")
+
+examples = [
+            [4], 
+            [7],
+            [8],
+            [2], 
+            [10]
+]
+
+
+gr.Interface(create_digit_samples, inputs, outputs, title=title, examples=examples, description=description, article=article, analytics_enabled=False).launch(debug=True)