| 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() |