Update app.py

app.py

@@ -2,15 +2,17 @@ import gradio as gr
 import tensorflow as tf
 from huggingface_hub import from_pretrained_keras
 
-description = "Keras implementation for Video Vision Transformer to classify samples of medmnist"
-article = "Author:<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 medmnist"
+description = "Keras implementation for Video Vision Transformer to classify samples of OrganMNIST3D\n\nModel train to classify 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",