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| import glob | |
| import gradio as gr | |
| import tensorflow as tf | |
| from utils.predict import predict_label | |
| ##Create list of examples to be loaded | |
| example_list = glob.glob("examples/*.mp4") | |
| example_list = list(map(lambda el:[el], example_list)) | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("# **<p align='center'>Video Vision Transformer on medmnist</p>**") | |
| with gr.Tab("Upload & Predict"): | |
| with gr.Box(): | |
| with gr.Row(): | |
| input_video = gr.Video(label="Input Video", show_label=True) | |
| output_label = gr.Label(label="Model Output", show_label=True) | |
| gr.Markdown("**Predict**") | |
| with gr.Box(): | |
| with gr.Row(): | |
| submit_button = gr.Button("Submit") | |
| gr.Markdown("The model is trained to classify videos belonging to the following classes: liver, kidney-right, kidney-left, femur-right, femur-left, bladder, heart, lung-right, lung-left, spleen and pancreas") | |
| gr.Examples(example_list, [input_video], [output_label], predict_label, cache_examples=True) | |
| submit_button.click(predict_label, inputs=input_video, outputs=output_label) | |
| gr.Markdown('\n Demo created by: <a href=\"https://huggingface.co/pablorodriper\"> Pablo Rodríguez</a> <br> Based on the Keras example by <a href=\"https://keras.io/examples/vision/vivit/\">Aritra Roy Gosthipaty and Ayush Thakur</a>') | |
| demo.launch() | |