Create app.py

app.py

@@ -0,0 +1,46 @@
+import tensorflow as tf 
+import pathlib
+import gradio as gradio
+import matplotlib.pyplot as plt 
+from huggingface_hub import from_pretrained_keras
+import numpy as np
+
+# Normalizing the images to [-1, 1]
+def normalize_test(input_image):
+  input_image = tf.cast(input_image, tf.float32)
+  input_image = (input_image / 127.5) - 1
+  return input_image
+  
+def resize(input_image, height, width):
+  input_image = tf.image.resize(input_image, [height, width],
+                                method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
+  return input_image
+  
+def load_image_infer(image_file):
+  input_image = resize(image_file, 256, 256)
+  input_image = normalize_test(input_image)
+
+  return input_image
+  
+def generate_images(test_input):
+  test_input = load_image_infer(test_input)
+  prediction = generator(np.expand_dims(test_input, axis=0), training=True)
+  fig = plt.figure(figsize=(128, 128))
+  title = ['Predicted Image']
+
+  plt.title('Predicted Image')
+  # Getting the pixel values in the [0, 1] range to plot.
+  plt.imshow(prediction[0,:,:,:] * 0.5 + 0.5)
+  plt.axis('off')
+  return fig
+  
+  
+generator = from_pretrained_keras("keras-io/pix2pix-generator")
+
+
+img = gr.inputs.Image(shape=(256,256))
+plot = gr.outputs.Image(type="plot")
+
+description = "Pix2Pix Facade Reconstructor"
+gr.Interface(generate_images, inputs = img, outputs = plot, 
+title = "Pix2Pix Facade Reconstructor", description = description, examples = [["img.png"]]).launch(debug=True)