Update app.py

app.py

@@ -16,37 +16,32 @@ article = """
             Space by Gitesh Chawda
         </p>
     """
-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)
+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))
+      images = np.zeros((28*num, 28*num), dtype=float)
       n = 0
-      for i in range(size):
-          for j in range(size):
-              if n == n_samples:
+      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] = examples[n, :, :, 0]
+              images[i* 28 : (i+1)*28, j*28 : (j+1)*28] = predictions[n, :, :, 0]
               n += 1
-      #digit_images = (digit_images/127.5) -1
-      return digit_images
+      return images
 
 
 inputs = gr.inputs.Number(label="number of images")
-outputs = gr.outputs.Image(label="Output Image")
+outputs = gr.outputs.Image(label="Predictions")
 
 examples = [
-            [1], 
-            [2],
-            [3],
-            [4], 
-            [64]
+            [10], 
+            [7],
+            [1],
+            [3], 
+            [5]
 ]
 
 
-gr.Interface(create_digit_samples, inputs, outputs, analytics_enabled=False, examples=examples).launch()
+gr.Interface(create_digit_samples, inputs, outputs, article=article, description=description, article=article, analytics_enabled=False, examples=examples).launch(enable_queue=True)