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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()
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)
title = "Video Classification with Transformers"
description = "This space demonstrates the use of a hybrid (CNN-Transformer based) model for video classification. \n The model can classify videos belonging to the following action categories: CricketShot, Punch, ShavingBeard, TennisSwing, PlayingCello. \n Upload a video and try out 🤗 "
article = '\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'
gr.Interface(predict_action, input_video, [output_label, output_gif], examples=example_list, allow_flagging=False, analytics_enabled=False,
title=title, description=description, cache_examples=True, article=article).launch(enable_queue=True,share=True)
# 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:")
# 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: <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()
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