| 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() |