import gradio as gr | |
import numpy as np | |
import tensorflow as tf | |
import tensorflow_hub as hub | |
import time | |
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]) | |
start = time.time() | |
pred = loaded_model.predict(im_reshape) | |
end=time.time() | |
t = start-end | |
cat_pred = pred | |
dog_pred = 1-pred | |
pred_label = np.argmax(pred) | |
if (pred_label == 0): | |
return "The Image is of a Dog."+"Accuracy:" +dog_pred[0]+"Time:"+t | |
if (pred_label == 1): | |
return "The Image is of a Cat."+"Accuracy:" +cat_pred[1]+"Time"+t | |
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) |