import os import gradio as gr from utils import ( create_gif_from_video_file, download_youtube_video, get_num_total_frames, ) from transformers import pipeline from huggingface_hub import HfApi, ModelFilter FRAME_SAMPLING_RATE = 4 DEFAULT_MODEL = "facebook/timesformer-base-finetuned-k400" VALID_VIDEOCLASSIFICATION_MODELS = [ "MCG-NJU/videomae-large-finetuned-kinetics", "facebook/timesformer-base-finetuned-k400", "fcakyon/timesformer-large-finetuned-k400", "MCG-NJU/videomae-base-finetuned-kinetics", "facebook/timesformer-base-finetuned-k600", "fcakyon/timesformer-large-finetuned-k600", "facebook/timesformer-hr-finetuned-k400", "facebook/timesformer-hr-finetuned-k600", "facebook/timesformer-base-finetuned-ssv2", "fcakyon/timesformer-large-finetuned-ssv2", "facebook/timesformer-hr-finetuned-ssv2", "MCG-NJU/videomae-base-finetuned-ssv2", "MCG-NJU/videomae-base-short-finetuned-kinetics", "MCG-NJU/videomae-base-short-ssv2", "MCG-NJU/videomae-base-short-finetuned-ssv2", "sayakpaul/videomae-base-finetuned-ucf101-subset", "nateraw/videomae-base-finetuned-ucf101", "MCG-NJU/videomae-base-ssv2", "zahrav/videomae-base-finetuned-ucf101-subset", ] pipe = pipeline( task="video-classification", model=DEFAULT_MODEL, top_k=5, frame_sampling_rate=FRAME_SAMPLING_RATE, ) 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 get_video_model_names(): filter = ModelFilter( task='video-classification', library='transformers', ) api = HfApi() video_models = list( iter(api.list_models(filter=filter, sort="downloads", direction=-1)) ) video_models = [video_model.id for video_model in video_models] return video_models def select_model(model_name): global pipe pipe = pipeline( task="video-classification", model=model_name, top_k=5, frame_sampling_rate=FRAME_SAMPLING_RATE, ) def predict(youtube_url_or_file_path): if youtube_url_or_file_path.startswith("http"): video_path = download_youtube_video(youtube_url_or_file_path) else: video_path = youtube_url_or_file_path # rearrange sampling rate based on video length and model input length num_total_frames = get_num_total_frames(video_path) num_model_input_frames = pipe.model.config.num_frames if num_total_frames < FRAME_SAMPLING_RATE * num_model_input_frames: frame_sampling_rate = num_total_frames // num_model_input_frames else: frame_sampling_rate = FRAME_SAMPLING_RATE gif_path = create_gif_from_video_file( video_path, frame_sampling_rate=frame_sampling_rate, save_path="video.gif" ) # run inference results = pipe(videos=video_path, frame_sampling_rate=frame_sampling_rate) os.remove(video_path) label_to_score = {result["label"]: result["score"] for result in results} return label_to_score, gif_path app = gr.Blocks() with app: gr.Markdown("# **

Video Classification with 🤗 Transformers

**") gr.Markdown( """

Perform video classification with HuggingFace Transformers video models.
For zero-shot classification, you can use the zero-shot classification demo.

""" ) gr.Markdown( """

Follow me for more!
twitter | github | linkedin | medium

""" ) with gr.Row(): with gr.Column(): model_names_dropdown = gr.Dropdown( choices=VALID_VIDEOCLASSIFICATION_MODELS, label="Model:", show_label=True, value=DEFAULT_MODEL, ) model_names_dropdown.change(fn=select_model, inputs=model_names_dropdown) with gr.Tab(label="Youtube URL"): gr.Markdown("### **Provide a Youtube video URL**") youtube_url = gr.Textbox(label="Youtube URL:", show_label=True) youtube_url_predict_btn = gr.Button(value="Predict") with gr.Tab(label="Local File"): gr.Markdown("### **Upload a video file**") video_file = gr.Video(label="Video File:", show_label=True) local_video_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, ) youtube_url_predict_btn.click( predict, inputs=youtube_url, outputs=[predictions, video_gif] ) local_video_predict_btn.click( predict, inputs=video_file, outputs=[predictions, video_gif] ) gr.Markdown( """ \n Demo created by: fcakyon.
Powered by HuggingFace Transformers video models . """ ) app.launch()