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import tensorflow_addons as tfa
import gradio as gr
import tensorflow as tf
import numpy as np
from tensorflow.keras.models import load_model
model=load_model('/content/saved_model/best_model.h5')
def classify_image(inp):
inp = inp.reshape((-1, IMG_SIZE, IMG_SIZE, 3))
#inp = tf.keras.applications.vgg16.preprocess_input(inp)
prediction = model.predict(inp).flatten()
return {labels[i]: float(prediction[i]) for i in range(NUM_CLASSES)}
image = gr.inputs.Image(shape=(IMG_SIZE, IMG_SIZE),label='Input')
label = gr.outputs.Label(num_top_classes=2)
gr.Interface(fn=classify_image, inputs=image, outputs=label, title='Cats Vs Dogs',height=600, width=1200).launch(debug=False)
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