Update app.py

app.py

@@ -18,20 +18,19 @@ article = """
         </p>
         """
 
-def Predict(model, num_images):
-        random_latent_vectors = tf.random.normal(shape=(int(num_images), 128))
-        preds = model(random_latent_vectors) 
-        num = ceil(sqrt(num_images))
-        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
-                images[i* 28 : (i+1)*28, j*28 : (j+1)*28] = preds[n, :, :, 0]
-                n += 1
-        return images
-
+def create_digit_samples(model, 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(num):
+          for j in range(num):
+              if n == num_images:
+                  break
+              images[i* 28 : (i+1)*28, j*28 : (j+1)*28] = predictions[n, :, :, 0]
+              n += 1
+      return images
 
 def inference(num_images, select: str):
     if select == 'fmnist':
@@ -39,16 +38,15 @@ def inference(num_images, select: str):
     else:
         result = create_digit_samples(model1, num_images)
     return result
-    
-examples = [[5],[8],[2],[3]]
+
 inputs = [gr.inputs.Number(label="number of images"), gr.inputs.Radio(['fmnist', 'mnist'])]
 outputs = gr.outputs.Image(label="Output Image")
+    
 
 interface = gr.Interface(
     fn = inference,
     inputs = inputs,
     outputs = outputs,
-    examples = examples,
     description = description,
     title = title,
     article = article