import gradio as gr from utils.predict import predict_action import os import glob ##Create Dataset for loading examples example_list = glob.glob("examples/*") example_list = list(map(lambda el:[el], example_list)) def load_example(video): return video[0] demo = gr.Blocks() with demo: gr.Markdown("# **

Video Classification with Transformers

**") 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:") gr.Markdown("CricketShot, PlayingCello, Punch, ShavingBeard, TennisSwing") with gr.Column(): # gr.Examples("examples", [input_video], [output_label,output_gif], predict_action, cache_examples=True) examples = gr.components.Dataset(components=[input_video], samples=example_list, type='values') examples.click(load_example, examples, input_video) submit_button.click(predict_action, inputs=input_video, outputs=[output_label,output_gif]) gr.Markdown('\n Author: Shivalika Singh
Based on this Keras example by Sayak Paul
Demo Powered by this Video Classification model') demo.launch()