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import gradio as gr
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
loaded_model = tf.keras.models.load_model(
('CatDogmodel.h5'),
custom_objects={'KerasLayer':hub.KerasLayer}
)
def model(image):
im_scaled = image/255
im_reshape = np.reshape(im_scaled,[1,224,224,3])
pred = loaded_model.predict(im_reshape)
cat_pred = pred
dog_pred = 1-pred
pred_label = np.argmax(pred)
if (pred_label == 0):
return "The Image is of a Dog."
if (pred_label == 1):
return "The Image is of a Cat."
image = gr.inputs.Image(shape=(224,224))
background='body{background-image:url("https://d2gg9evh47fn9z.cloudfront.net/800px_COLOURBOX35440124.jpg");}'
title = "Cat & Dog Classifier"
iface = gr.Interface(fn=model, inputs=image, outputs='text', css=background, title=title)
iface.launch(debug=True) |