Upload app.py

app.py

@@ -0,0 +1,34 @@
+
+import numpy as np
+import tensorflow as tf
+import gradio as gr
+from huggingface_hub import from_pretrained_keras
+
+model = from_pretrained_keras("keras-io/conv_autoencoder")
+
+examples = [
+            ['./example_0.jpeg'], 
+            ['./example_1.jpeg'], 
+            ['./example_2.jpeg'], 
+            ['./example_3.jpeg'], 
+            ['./example_4.jpeg']
+]
+
+def infer(original_image):
+    image = tf.keras.utils.img_to_array(original_image)
+    image = image.astype("float32") / 255.0
+    image = np.reshape(image, (1, 28, 28, 1))
+    output = model.predict(image)
+    output = np.reshape(output, (28, 28, 1))
+    output_image = tf.keras.preprocessing.image.array_to_img(output)
+    return output_image
+
+iface = gr.Interface(
+    fn = infer,
+    title = "Image Denoising using Convolutional AutoEncoders",
+    description = "Keras Implementation of a deep convolutional autoencoder for image denoising",
+    inputs = gr.inputs.Image(image_mode='L', shape=(28, 28)),
+    outputs = gr.outputs.Image(type = 'pil'),
+    examples = examples,
+    article = "Author: <a href=\"https://huggingface.co/Blazer007\">Vivek Rai</a>. Based on the keras example from <a href=\"https://keras.io/examples/vision/autoencoder/\">Santiago L. Valdarrama</a> \n Model Link: https://huggingface.co/keras-io/conv_autoencoder",
+    ).launch(enable_queue=True, debug = True)