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import gradio as gr
import numpy as np
import tensorflow as tf
from PIL import Image
model = tf.keras.models.load_model('model')
def predict_model(im):
im = tf.image.convert_image_dtype(im,tf.float32)
im = tf.expand_dims(im,axis=0)
pred = model.predict(im)
pred = pred[0]
pred = tf.clip_by_value(pred, 0.0, 1.0)
pred = Image.fromarray((np.array(pred) * 255).astype(np.uint8))
return pred
inimage = gr.Image(shape=(None,None))
inimage.style(width=512,height=512)
outimage = gr.Image(shape=(None,None))
outimage.style(width=512,height=512)
app = gr.Interface(fn=predict_model, inputs=inimage, outputs=outimage,examples=['./assets/examples/creature.jpg',
"./assets/examples/butterfly.jpeg",
"./assets/examples/bw.jpg",
"./assets/examples/hillbefore.jpg"])
if __name__ == "__main__":
app.launch()
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