Create app.py

app.py

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+import tensorflow as tf
+import tensorflow_hub as hub
+
+import requests
+from PIL import Image
+from io import BytesIO
+
+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
+
+model_name = "efficientnetv2-s" 
+
+model_handle_map = {
+  "efficientnetv2-s": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_s/classification/2",
+  "efficientnetv2-m": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_m/classification/2",
+  "efficientnetv2-l": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_l/classification/2",
+  "efficientnetv2-s-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_s/classification/2",
+  "efficientnetv2-m-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_m/classification/2",
+  "efficientnetv2-l-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_l/classification/2",
+  "efficientnetv2-xl-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_xl/classification/2",
+  "efficientnetv2-b0-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b0/classification/2",
+  "efficientnetv2-b1-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b1/classification/2",
+  "efficientnetv2-b2-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b2/classification/2",
+  "efficientnetv2-b3-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b3/classification/2",
+  "efficientnetv2-s-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_s/classification/2",
+  "efficientnetv2-m-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_m/classification/2",
+  "efficientnetv2-l-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_l/classification/2",
+  "efficientnetv2-xl-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_xl/classification/2",
+  "efficientnetv2-b0-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b0/classification/2",
+  "efficientnetv2-b1-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b1/classification/2",
+  "efficientnetv2-b2-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b2/classification/2",
+  "efficientnetv2-b3-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b3/classification/2",
+  "efficientnetv2-b0": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b0/classification/2",
+  "efficientnetv2-b1": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b1/classification/2",
+  "efficientnetv2-b2": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b2/classification/2",
+  "efficientnetv2-b3": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b3/classification/2",
+  "efficientnet_b0": "https://tfhub.dev/tensorflow/efficientnet/b0/classification/1",
+  "efficientnet_b1": "https://tfhub.dev/tensorflow/efficientnet/b1/classification/1",
+  "efficientnet_b2": "https://tfhub.dev/tensorflow/efficientnet/b2/classification/1",
+  "efficientnet_b3": "https://tfhub.dev/tensorflow/efficientnet/b3/classification/1",
+  "efficientnet_b4": "https://tfhub.dev/tensorflow/efficientnet/b4/classification/1",
+  "efficientnet_b5": "https://tfhub.dev/tensorflow/efficientnet/b5/classification/1",
+  "efficientnet_b6": "https://tfhub.dev/tensorflow/efficientnet/b6/classification/1",
+  "efficientnet_b7": "https://tfhub.dev/tensorflow/efficientnet/b7/classification/1",
+  "bit_s-r50x1": "https://tfhub.dev/google/bit/s-r50x1/ilsvrc2012_classification/1",
+  "inception_v3": "https://tfhub.dev/google/imagenet/inception_v3/classification/4",
+  "inception_resnet_v2": "https://tfhub.dev/google/imagenet/inception_resnet_v2/classification/4",
+  "resnet_v1_50": "https://tfhub.dev/google/imagenet/resnet_v1_50/classification/4",
+  "resnet_v1_101": "https://tfhub.dev/google/imagenet/resnet_v1_101/classification/4",
+  "resnet_v1_152": "https://tfhub.dev/google/imagenet/resnet_v1_152/classification/4",
+  "resnet_v2_50": "https://tfhub.dev/google/imagenet/resnet_v2_50/classification/4",
+  "resnet_v2_101": "https://tfhub.dev/google/imagenet/resnet_v2_101/classification/4",
+  "resnet_v2_152": "https://tfhub.dev/google/imagenet/resnet_v2_152/classification/4",
+  "nasnet_large": "https://tfhub.dev/google/imagenet/nasnet_large/classification/4",
+  "nasnet_mobile": "https://tfhub.dev/google/imagenet/nasnet_mobile/classification/4",
+  "pnasnet_large": "https://tfhub.dev/google/imagenet/pnasnet_large/classification/4",
+  "mobilenet_v2_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4",
+  "mobilenet_v2_130_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4",
+  "mobilenet_v2_140_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/4",
+  "mobilenet_v3_small_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/classification/5",
+  "mobilenet_v3_small_075_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_small_075_224/classification/5",
+  "mobilenet_v3_large_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/classification/5",
+  "mobilenet_v3_large_075_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_large_075_224/classification/5",
+}
+
+model_image_size_map = {
+  "efficientnetv2-s": 384,
+  "efficientnetv2-m": 480,
+  "efficientnetv2-l": 480,
+  "efficientnetv2-b0": 224,
+  "efficientnetv2-b1": 240,
+  "efficientnetv2-b2": 260,
+  "efficientnetv2-b3": 300,
+  "efficientnetv2-s-21k": 384,
+  "efficientnetv2-m-21k": 480,
+  "efficientnetv2-l-21k": 480,
+  "efficientnetv2-xl-21k": 512,
+  "efficientnetv2-b0-21k": 224,
+  "efficientnetv2-b1-21k": 240,
+  "efficientnetv2-b2-21k": 260,
+  "efficientnetv2-b3-21k": 300,
+  "efficientnetv2-s-21k-ft1k": 384,
+  "efficientnetv2-m-21k-ft1k": 480,
+  "efficientnetv2-l-21k-ft1k": 480,
+  "efficientnetv2-xl-21k-ft1k": 512,
+  "efficientnetv2-b0-21k-ft1k": 224,
+  "efficientnetv2-b1-21k-ft1k": 240,
+  "efficientnetv2-b2-21k-ft1k": 260,
+  "efficientnetv2-b3-21k-ft1k": 300, 
+  "efficientnet_b0": 224,
+  "efficientnet_b1": 240,
+  "efficientnet_b2": 260,
+  "efficientnet_b3": 300,
+  "efficientnet_b4": 380,
+  "efficientnet_b5": 456,
+  "efficientnet_b6": 528,
+  "efficientnet_b7": 600,
+  "inception_v3": 299,
+  "inception_resnet_v2": 299,
+  "mobilenet_v2_100_224": 224,
+  "mobilenet_v2_130_224": 224,
+  "mobilenet_v2_140_224": 224,
+  "nasnet_large": 331,
+  "nasnet_mobile": 224,
+  "pnasnet_large": 331,
+  "resnet_v1_50": 224,
+  "resnet_v1_101": 224,
+  "resnet_v1_152": 224,
+  "resnet_v2_50": 224,
+  "resnet_v2_101": 224,
+  "resnet_v2_152": 224,
+  "mobilenet_v3_small_100_224": 224,
+  "mobilenet_v3_small_075_224": 224,
+  "mobilenet_v3_large_100_224": 224,
+  "mobilenet_v3_large_075_224": 224,
+}
+
+model_handle = model_handle_map[model_name]
+
+
+max_dynamic_size = 512
+if model_name in model_image_size_map:
+  image_size = model_image_size_map[model_name]
+  dynamic_size = False
+  print(f"Images will be converted to {image_size}x{image_size}")
+else:
+  dynamic_size = True
+  print(f"Images will be capped to a max size of {max_dynamic_size}x{max_dynamic_size}")
+
+labels_file = "https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt"
+
+#download labels and creates a maps
+downloaded_file = tf.keras.utils.get_file("labels.txt", origin=labels_file)
+
+classes = []
+
+with open(downloaded_file) as f:
+  labels = f.readlines()
+  classes = [l.strip() for l in labels]
+
+
+classifier = hub.load(model_handle)
+
+
+def inference(img):
+  image, original_image = load_image(img, image_size, dynamic_size, max_dynamic_size)  
+  
+  
+  input_shape = image.shape
+  warmup_input = tf.random.uniform(input_shape, 0, 1.0)
+  warmup_logits = classifier(warmup_input).numpy()
+  
+  # Run model on image
+  probabilities = tf.nn.softmax(classifier(image)).numpy()
+  
+  top_5 = tf.argsort(probabilities, axis=-1, direction="DESCENDING")[0][:5].numpy()
+  np_classes = np.array(classes)
+  
+  # Some models include an additional 'background' class in the predictions, so
+  # we must account for this when reading the class labels.
+  includes_background_class = probabilities.shape[1] == 1001
+  result = {}
+  for i, item in enumerate(top_5):
+    class_index = item if includes_background_class else item + 1
+    line = f'({i+1}) {class_index:4} - {classes[class_index]}: {probabilities[0][top_5][i]}'
+    result[classes[class_index]] = probabilities[0][top_5][i].item()
+  return result
+
+title="efficientnetv2-s"
+description="Gradio Demo for efficientnetv2-s: EfficientNet V2 are a family of image classification models, which achieve better parameter efficiency and faster training speed than prior arts. To use it, simply upload your image or click on one of the examples to load them. Read more at the links below"
+article = "<p style='text-align: center'><a href='https://tfhub.dev/google/collections/efficientnet_v2/1' target='_blank'>Tensorflow Hub</a></p>"
+examples=[['apple1.jpg']]
+gr.Interface(inference,gr.inputs.Image(type="filepath"),"label",title=title,description=description,article=article,examples=examples).launch(enable_queue=True)