from keras.models import load_model
import numpy as np
from keras.preprocessing import image
import gradio as gr
from PIL import Image
model=load_model('./flower.h5')

def show(img):    
    img = img.reshape( 180, 180,3)
    test_image=np.expand_dims(img, axis=0)    
    prediction=model.predict(test_image).tolist()[0]
    class_names = ['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']    
    return {class_names[i]: prediction[i] for i in range(5)}

image = gr.inputs.Image(shape=(180,180))

demo = gr.Interface(
    fn = show, 
    inputs = image, 
    examples=["photo/a01.jpg", "photo/a02.jpg","photo/a03.jpg","photo/a04.jpg","photo/a05.jpg"],    
    title="Flower Image Classification",
    outputs = gr.outputs.Label(),
    
)


if __name__ == "__main__":
    demo.launch()