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| import gradio as gr | |
| from utils.predict import predict_action | |
| import os | |
| import glob | |
| ##Create list of examples to be loaded | |
| example_list = glob.glob("examples/*") | |
| example_list = list(map(lambda el:[el], example_list)) | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("# **<p align='center'>Video Classification with Transformers</p>**") | |
| gr.Markdown("This space demonstrates the use of hybrid Transformer-based models for video classification that operate on CNN feature maps.") | |
| with gr.Tabs(): | |
| with gr.TabItem("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) | |
| output_gif = gr.Image(label="Video Gif", show_label=True) | |
| gr.Markdown("**Predict**") | |
| with gr.Box(): | |
| with gr.Row(): | |
| submit_button = gr.Button("Submit") | |
| gr.Markdown("**Examples:**") | |
| gr.Markdown("The model is trained to classify videos belonging to the following classes: CricketShot, PlayingCello, Punch, ShavingBeard, TennisSwing") | |
| # gr.Markdown("CricketShot, PlayingCello, Punch, ShavingBeard, TennisSwing") | |
| with gr.Column(): | |
| gr.Examples(example_list, [input_video], [output_label,output_gif], predict_action, cache_examples=True) | |
| submit_button.click(predict_action, inputs=input_video, outputs=[output_label,output_gif]) | |
| gr.Markdown('\n Demo created by: <a href=\"https://www.linkedin.com/in/shivalika-singh/\">Shivalika Singh</a> <br> Based on this <a href=\"https://keras.io/examples/vision/video_transformers/\">Keras example</a> by <a href=\"https://twitter.com/RisingSayak\">Sayak Paul</a> <br> Demo Powered by this <a href=\"https://huggingface.co/shivi/video-transformers/\"> Video Classification</a> model') | |
| demo.launch() | |