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import gradio as gr
import tensorflow
import numpy
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model
model = load_model('vgg_model.keras')
def predict(img):
img = image.img_to_array(img)
img = numpy.expand_dims(img, axis=0)
img = img.reshape((-1, 150, 150, 3))
prediction = model.predict(img)
confidences = {"Cat": float(prediction[0]), 'Dog': float(prediction[1])}
return confidences
demo = gr.Interface(
fn=predict,
inputs=gr.Image(shape=(150, 150)),
outputs=gr.Label(num_top_classes=2),
)
demo.launch() |