Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from huggingface_hub import from_pretrained_keras
+
+description = "Keras implementation for Video Vision Transformer trained with OrganMNIST3D (CT videos)"
+article = "Classes: liver, kidney-right, kidney-left, femur-right, femur-left, bladder, heart, lung-right, lung-left, spleen, pancreas.\n\nAuthor:<a href=\"https://huggingface.co/pablorodriper/\"> Pablo Rodríguez</a>; Based on the keras example by <a href=\"https://keras.io/examples/vision/vivit/\">Aritra Roy Gosthipaty and Ayush Thakur</a>"
+title = "Video Vision Transformer on OrganMNIST3D"
+
+def infer(x):
+    return model.predict(tf.expand_dims(x, axis=0))[0]
+
+model = from_pretrained_keras("keras-io/video-vision-transformer")
+
+labels = ['liver', 'kidney-right', 'kidney-left', 'femur-right', 'femur-left', 'bladder', 'heart', 'lung-right', 'lung-left', 'spleen', 'pancreas']
+
+iface = gr.Interface(
+    fn = infer,
+    inputs = "video",
+    outputs = "number",
+    description = description,
+    title = title,
+    article = article
+    )
+
+iface.launch()