app.py

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)