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import gradio as gr
from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
from keras.applications.vgg16 import decode_predictions
import numpy as np
def predict_image(img):
img_4d = img.reshape(-1,224,224,3)
prediction = model.predict(img_4d)
prediction_results = decode_predictions(prediction, top = 5)
return { prediction_results[0][i][1]: float(prediction_results[0][i][2]) 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() |