Create app.py

app.py

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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
+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()