| import gradio as gr | |
| import tensorflow | |
| import numpy | |
| from tensorflow.keras.preprocessing import image | |
| from tensorflow.keras.models import load_model | |
| model = load_model('vgg_model.keras') | |
| def predict(img): | |
| img = image.img_to_array(img) | |
| img = numpy.expand_dims(img, axis=0) | |
| img = img.reshape((-1, 150, 150, 3)) | |
| prediction = model.predict(img) | |
| confidences = {"Cat": float(prediction[0]), 'Dog': float(prediction[1])} | |
| return confidences | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(shape=(150, 150)), | |
| outputs=gr.Label(num_top_classes=2), | |
| ) | |
| demo.launch() |