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import torch
import os
import gradio as gr
from video_transformers import VideoModel
from utils import (
convert_frames_to_gif,
download_youtube_video,
sample_frames_from_video_file,
)
video_model = VideoModel.from_transformers("facebook/timesformer-base-finetuned-k400")
examples = [
["https://www.youtube.com/watch?v=huAJ9dC5lmI"],
["https://www.youtube.com/watch?v=wvcWt6u5HTg"],
["https://www.youtube.com/watch?v=-3kZSi5qjRM"],
["https://www.youtube.com/watch?v=-6usjfP8hys"],
["https://www.youtube.com/watch?v=BDHub0gBGtc"],
["https://www.youtube.com/watch?v=B9ea7YyCP6E"],
["https://www.youtube.com/watch?v=BBkpaeJBKmk"],
["https://www.youtube.com/watch?v=BBqU8Apee_g"],
["https://www.youtube.com/watch?v=B8OdMwVwyXc"],
["https://www.youtube.com/watch?v=I7cwq6_4QtM"],
["https://www.youtube.com/watch?v=Z0mJDXpNhYA"],
["https://www.youtube.com/watch?v=QkQQjFGnZlg"],
["https://www.youtube.com/watch?v=IQaoRUQif14"],
]
def predict(youtube_url):
video_path = download_youtube_video(youtube_url)
frames = sample_frames_from_video_file(video_path, num_frames=16)
gif_path = convert_frames_to_gif(frames)
result = video_model.predict(video_or_folder_path=video_path)
os.remove(video_path)
return result["predictions"], gif_path
app = gr.Blocks()
with app:
gr.Markdown("# **<p align='center'>Video Classification with Timesformer</p>**")
gr.Markdown(
"""
<p style='text-align: center'>
Timesformer is a video model that uses a Transformer architecture to process video frames.
<br>It is released by Facebook AI Research in ICML 2021.
<br>This version is trained on Kinetics-400 dataset and can classify videos into 400 classes.
</p>
"""
)
gr.Markdown(
"""
<p style='text-align: center'>
Follow me for more!
<br> <a href='https://twitter.com/fcakyon' target='_blank'>twitter</a> | <a href='https://github.com/fcakyon' target='_blank'>github</a> | <a href='https://www.linkedin.com/in/fcakyon/' target='_blank'>linkedin</a> | <a href='https://fcakyon.medium.com/' target='_blank'>medium</a>
</p>
"""
)
with gr.Row():
with gr.Column():
gr.Markdown("Provide a Youtube video URL.")
youtube_url = gr.Textbox(label="Youtube URL:", show_label=True)
predict_btn = gr.Button(value="Predict")
with gr.Column():
video_gif = gr.Image(
label="Input Clip",
show_label=True,
)
with gr.Column():
predictions = gr.Label(
label="Predictions:", show_label=True, num_top_classes=5
)
gr.Markdown("**Examples:**")
gr.Examples(
examples,
youtube_url,
[predictions, video_gif],
fn=predict,
cache_examples=True,
)
predict_btn.click(predict, inputs=youtube_url, outputs=[predictions, video_gif])
gr.Markdown(
"""
\n Demo created by: <a href=\"https://github.com/fcakyon\">fcakyon</a>
<br> Based on this <a href=\"https://huggingface.co/docs/transformers/main/model_doc/timesformer">HuggingFace model</a>
"""
)
app.launch()
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