Create app.py

app.py

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+import os
+import time
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+import tensorflow_hub as hub
+import matplotlib.pyplot as plt
+os.environ["TFHUB_DOWNLOAD_PROGRESS"] = "True"
+
+os.system("wget https://user-images.githubusercontent.com/12981474/40157448-eff91f06-5953-11e8-9a37-f6b5693fa03f.png -O original.png")
+
+# Declaring Constants
+IMAGE_PATH = "original.png"
+SAVED_MODEL_PATH = "https://tfhub.dev/captain-pool/esrgan-tf2/1"
+
+def preprocess_image(image_path):
+  """ Loads image from path and preprocesses to make it model ready
+      Args:
+        image_path: Path to the image file
+  """
+  hr_image = tf.image.decode_image(tf.io.read_file(image_path))
+  # If PNG, remove the alpha channel. The model only supports
+  # images with 3 color channels.
+  if hr_image.shape[-1] == 4:
+    hr_image = hr_image[...,:-1]
+  hr_size = (tf.convert_to_tensor(hr_image.shape[:-1]) // 4) * 4
+  hr_image = tf.image.crop_to_bounding_box(hr_image, 0, 0, hr_size[0], hr_size[1])
+  hr_image = tf.cast(hr_image, tf.float32)
+  return tf.expand_dims(hr_image, 0)
+
+  
+def plot_image(image):
+  """
+    Plots images from image tensors.
+    Args:
+      image: 3D image tensor. [height, width, channels].
+      title: Title to display in the plot.
+  """
+  image = np.asarray(image)
+  image = tf.clip_by_value(image, 0, 255)
+  image = Image.fromarray(tf.cast(image, tf.uint8).numpy())
+  return image
+  
+model = hub.load(SAVED_MODEL_PATH)
+def inference(img)
+  hr_image = preprocess_image(img)
+  start = time.time()
+  fake_image = model(hr_image)
+  fake_image = tf.squeeze(fake_image)
+  print("Time Taken: %f" % (time.time() - start))
+  pil_image = plot_image(tf.squeeze(fake_image))
+  return pil_image
+  
+gr.Interface(inference,gr.inputs.Image(type="filepath"),"image").launch()
+