app.py

import gradio as gr
from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np

def predict_image(img):
    img_4d = img.reshape(-1,224,224,3)
    prediction = model.predict(img_4d)[0]
    return {class_names[i]: float(prediction[i]) for i in range(5)}

model = VGG16()
model.summary()
image = gr.inputs.Image(shape=(224,224))
label = gr.outputs.Label(num_top_classes=5)

gr.Interface(fn=predict_image, 
                           title="VGG16 Classification",
                           description="VGG16 CNN",
                           inputs = image,
                           outputs = label,
                           live=True,
                           interpretation='default',
                           allow_flagging="never").launch()