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from keras.models import load_model
import numpy as np
from keras.preprocessing import image
import gradio as gr
from PIL import Image
def a(img):
#img = img.reshape(1, 64, 64,3)
img = img.reshape( 64, 64,3)
model=load_model('./cats&dog.h5')
#test_image=image.load_img("pic01.jpg",target_size=(64,64))
#test_image=image.img_to_array(img)
test_image=np.expand_dims(img, axis=0)
result=model.predict(test_image)
if result[0][0]==1:
prediction='Dog'
print(prediction)
return prediction
else:
prediction='Cat'
print(prediction)
return prediction
input = gr.inputs.Image(type='pil', label="Original Image", source="upload", optional=True)
#input_2 = gr.inputs.Image(type='pil', label="Original Image", source="webcam", optional=True)
#inputs = [input, input_2]
inputs = [input]
outputs = gr.outputs.Image(type="pil", label="Output Image")
title = "Dog and Cat Object detection"
image = gr.inputs.Image(shape=(64,64))
demo=gr.Interface(fn=a, inputs=image,examples=["a01.jpg", "a02.jpg","a03.jpg","a04.jpg"],outputs="text").launch(debug='True')
if __name__ == "__main__":
demo.launch() |