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import numpy as np
import tensorflow as tf
import gradio as gr
from huggingface_hub import from_pretrained_keras
import cv2
# import matplotlib.pyplot as plt
model = from_pretrained_keras("keras-io/conv_Mixer")
# functions for inference
IMG_SIZE = 32
class_names = [
"Airplane",
"Automobile",
"Bird",
"Cat",
"Deer",
"Dog",
"Frog",
"Horse",
"Ship",
"Truck",
]
# resize the image and it to a float between 0,1
def preprocess_image(image, label):
image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))
return image, label
def read_image(image):
image = tf.convert_to_tensor(image)
image.set_shape([None, None, 3])
print('$$$$$$$$$$$$$$$$$$$$$ in read image $$$$$$$$$$$$$$$$$$$$$$')
print(image.shape)
# plt.imshow(image)
# plt.show()
image, _ = preprocess_image(image, 1) # 1 here is a temporary label
return image
def infer(input_image):
print('#$$$$$$$$$$$$$$$$$$$$$$$$$ IN INFER $$$$$$$$$$$$$$$$$$$$$$$')
image_tensor = read_image(input_image)
print(image_tensor.shape)
predictions = model.predict(np.expand_dims((image_tensor), axis=0))
predictions = np.squeeze(predictions).astype(float)
return dict(zip(class_names, predictions))
# get the inputs
input = gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE))
# the app outputs two segmented images
output = [gr.outputs.Label()]
# it's good practice to pass examples, description and a title to guide users
examples = [["./content/examples/Frog.jpg"], ["./content/examples/Truck.jpg"], ["./content/examples/car.jpg"]]
title = "Image classification with ConvMixer"
description = "Upload an image or select from examples to classify it. This is a <b>ConvMixer Model</b> trained on <b>CIFAR-10</b>. The allowed classes are - Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck <p><b>Space author: Harshavardhan</b> <br><b> Keras example author: <a href=\"https://twitter.com/RisingSayak\"> Sayak Paul </a> </b> <br> <a href=\"https://keras.io/examples/vision/convmixer/\">link to the original Keras example</a> </p>"
gr_interface = gr.Interface(infer, input, output, examples=examples, allow_flagging=False, analytics_enabled=False, title=title, description=description).launch(enable_queue=True, debug=False)
gr_interface.launch()
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