Update app.py

app.py

@@ -9,6 +9,50 @@ import matplotlib.pyplot as plt
 import numpy as np
 import gradio as gr
 
+#@title Helper functions for loading image (hidden)
+
+original_image_cache = {}
+
+def preprocess_image(image):
+  image = np.array(image)
+  # reshape into shape [batch_size, height, width, num_channels]
+  img_reshaped = tf.reshape(image, [1, image.shape[0], image.shape[1], image.shape[2]])
+  # Use `convert_image_dtype` to convert to floats in the [0,1] range.
+  image = tf.image.convert_image_dtype(img_reshaped, tf.float32)
+  return image
+
+def load_image_from_url(img_url):
+  """Returns an image with shape [1, height, width, num_channels]."""
+  user_agent = {'User-agent': 'Colab Sample (https://tensorflow.org)'}
+  response = requests.get(img_url, headers=user_agent)
+  image = Image.open(BytesIO(response.content))
+  image = preprocess_image(image)
+  return image
+
+def load_image(image_url, image_size=256, dynamic_size=False, max_dynamic_size=512):
+  """Loads and preprocesses images."""
+  # Cache image file locally.
+  if image_url in original_image_cache:
+    img = original_image_cache[image_url]
+  elif image_url.startswith('https://'):
+    img = load_image_from_url(image_url)
+  else:
+    fd = tf.io.gfile.GFile(image_url, 'rb')
+    img = preprocess_image(Image.open(fd))
+  original_image_cache[image_url] = img
+  # Load and convert to float32 numpy array, add batch dimension, and normalize to range [0, 1].
+  img_raw = img
+  if tf.reduce_max(img) > 1.0:
+    img = img / 255.
+  if len(img.shape) == 3:
+    img = tf.stack([img, img, img], axis=-1)
+  if not dynamic_size:
+    img = tf.image.resize_with_pad(img, image_size, image_size)
+  elif img.shape[1] > max_dynamic_size or img.shape[2] > max_dynamic_size:
+    img = tf.image.resize_with_pad(img, max_dynamic_size, max_dynamic_size)
+  return img, img_raw
+
+
 
 image_size = 224
 dynamic_size = False
@@ -171,4 +215,4 @@ def inference(img):
   
   return line
   
-gr.Interface(inference,"image","text").launch()
+gr.Interface(inference,gr.inputs.Image(type="filepath"),"text").launch()