Update app.py

app.py

@@ -7,20 +7,6 @@ import numpy as np
 
 model = from_pretrained_keras("IMvision12/WGAN-GP")
 
-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 = """
@@ -33,23 +19,34 @@ article = """
 inputs = gr.inputs.Number(label="number of images")
 outputs = gr.outputs.Image(label="Predictions")
 
+def create_digit_samples(n_samples):
+      latent_dim = 128
+      random_latent_vectors = tf.random.normal(shape=(int(n_samples), 128))
+      examples = model.predict(random_latent_vectors)
+      #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
+
+
+inputs = gr.inputs.Number(label="number of images")
+outputs = gr.outputs.Image(label="Output Image")
+
 examples = [
+            [1], 
+            [2],
+            [3],
             [4], 
-            [7],
-            [8],
-            [2], 
-            [10]
+            [64]
 ]
 
 
-gr.Interface(
-        fn=create_digit_samples,
-        inputs=inputs,  # Resize to CIFAR
-        outputs=outputs,
-        examples=examples,
-        article=article,
-        allow_flagging="never",
-        analytics_enabled=False,
-        title=title,
-        description=description,
-    ).launch(enable_queue=True)
+gr.Interface(create_digit_samples, inputs, outputs, analytics_enabled=False, examples=examples).launch(debug=True)