Update app.py

app.py

@@ -17,31 +17,43 @@ article = """
         </p>
     """
 
-def Predict(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
-
-
-inputs = gr.inputs.Number(label="number of images")
-outputs = gr.outputs.Image(label="Predictions")
-
-examples = [
-            [10], 
-            [7],
-            [1],
-            [3], 
-            [5]
-]
-
-
-gr.Interface(Predict, inputs, outputs, article=article, title=title, description=description, examples=examples, analytics_enabled=False).launch(enable_queue=True)
+def generate_latent_points(latent_dim, n_samples):
+	random_latent_vectors = tf.random.normal(shape=(n_samples, latent_dim))
+	return 
+
+def create_digit_samples(digit, n_samples):
+    if digit in range(10):
+        latent_dim = 128
+        random_vector_labels = generate_latent_points(int(digit), latent_dim, int(n_samples))
+        examples = model.predict(random_vector_labels)
+        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
+
+description = "Keras implementation for Conditional GAN to generate samples for specific digit of MNIST"
+article = "Author:<a href=\"https://huggingface.co/rajrathi\"> Rajeshwar Rathi</a>; Based on the keras example by <a href=\"https://keras.io/examples/generative/conditional_gan/\">Sayak Paul</a>"
+title = "Conditional GAN for MNIST"
+
+examples = [[1, 10], [3, 5], [5, 15]]
+
+
+iface = gr.Interface(
+    fn = create_digit_samples,
+    inputs = ["number", "number"],
+    outputs = ["image"],
+    examples = examples,
+    description = description,
+    title = title,
+    article = article
+    )
+
+iface.launch()