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| import os | |
| import gradio as gr | |
| import torch | |
| from torch.utils.data import DataLoader | |
| from utils.unifiedmodel import RRUMDataset | |
| from utils.huggingface_model_wrapper import YoutubeVideoSimilarityModel | |
| from utils.helper_funcs import get_example_videos, update_youtube_embedded_html, get_input_data_df | |
| RR_EXAMPLES_URL = os.environ.get( | |
| 'RR_EXAMPLES_URL', 'https://public-data.telemetry.mozilla.org/api/v1/tables/telemetry_derived/regrets_reporter_study/v1/files/000000000000.json') | |
| NUM_RR_EXAMPLES = 5 | |
| example_videos, example_videos_rr = get_example_videos( | |
| RR_EXAMPLES_URL, NUM_RR_EXAMPLES) | |
| demo_title = 'Mozilla RegretsReporter YouTube video similarity' | |
| demo_description = f''' | |
| # {demo_title} | |
| This demo showcases the YouTube video semantic similarity model developed as part of the RegretsReporter research project at Mozilla Foundation. You can read more about the project [here](https://foundation.mozilla.org/en/youtube/user-controls/) and about the semantic similarity model [here](https://foundation.mozilla.org/en/blog/the-regretsreporter-user-controls-study-machine-learning-to-measure-semantic-similarity-of-youtube-videos/). Note: the model is multilingual so you can try it with non-English videos too while it probably works the best with English videos. | |
| This demo works by inserting two YouTube video URLs below and clicking the Run button. After a few seconds, you will see model's predicted probability of how similar those two videos are. You can copy URLs from YouTube or also try out a few predefined examples by clicking them on the examples table. | |
| ''' | |
| placeholder_youtube_embedded_html = ''' | |
| <p>Insert video URL first</p> | |
| ''' | |
| model_wt = YoutubeVideoSimilarityModel.from_pretrained( | |
| 'mozilla-foundation/youtube_video_similarity_model_wt') | |
| model_nt = YoutubeVideoSimilarityModel.from_pretrained( | |
| 'mozilla-foundation/youtube_video_similarity_model_nt') | |
| cross_encoder_model_name_or_path = model_wt.cross_encoder_model_name_or_path | |
| def get_video_similarity(video1_url, video2_url): | |
| df = get_input_data_df(video1_url, video2_url) | |
| if df['regret_transcript'].isna().any() or df['recommendation_transcript'].isna().any(): | |
| with_transcript = False | |
| else: | |
| with_transcript = True | |
| dataset = RRUMDataset(df, with_transcript=with_transcript, label_col=None, | |
| cross_encoder_model_name_or_path=cross_encoder_model_name_or_path) | |
| data_loader = DataLoader(dataset.test_dataset, shuffle=False, | |
| batch_size=1, num_workers=0, pin_memory=False) | |
| with torch.inference_mode(): | |
| if with_transcript: | |
| pred = model_wt(next(iter(data_loader))) | |
| else: | |
| pred = model_nt(next(iter(data_loader))) | |
| pred = torch.special.expit(pred).squeeze().tolist() | |
| return f'YouTube videos are {pred:.0%} similar' | |
| with gr.Blocks(title=demo_title) as demo: | |
| gr.Markdown(demo_description) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_text1 = gr.Textbox( | |
| label='Video 1', placeholder='Insert first YouTube video URL') | |
| input_text2 = gr.Textbox( | |
| label='Video 2', placeholder='Insert second YouTube video URL') | |
| inputs = [input_text1, input_text2] | |
| with gr.Row(): | |
| clear_btn = gr.Button('Clear', variant='secondary') | |
| run_btn = gr.Button('Run', variant='primary') | |
| with gr.Column(): | |
| output_label = gr.Label(label='Model prediction') | |
| outputs = [output_label] | |
| with gr.Accordion('See video details', open=False): | |
| with gr.Row(): | |
| with gr.Column(): | |
| video_embedded1 = gr.HTML( | |
| value=placeholder_youtube_embedded_html) | |
| with gr.Column(): | |
| video_embedded2 = gr.HTML( | |
| value=placeholder_youtube_embedded_html) | |
| with gr.Column(): | |
| if example_videos: | |
| examples = gr.Examples(examples=example_videos, inputs=inputs) | |
| if example_videos_rr: | |
| examples_rr = gr.Examples(examples=example_videos_rr, inputs=inputs, | |
| label='Example bad becommendations from the RegretsReporter report') | |
| def inputs_change(input, position): | |
| embedded_value = update_youtube_embedded_html( | |
| input, position) if input else placeholder_youtube_embedded_html | |
| if position == 1: | |
| return {video_embedded1: embedded_value, output_label: None} | |
| else: | |
| return {video_embedded2: embedded_value, output_label: None} | |
| run_btn.click(fn=get_video_similarity, inputs=inputs, outputs=outputs) | |
| clear_btn.click(lambda value_1, value_2, value_3: ( | |
| None, None, None), inputs=inputs + outputs, outputs=inputs + outputs) | |
| input_text1.change(lambda input: inputs_change( | |
| input, 1), inputs=input_text1, outputs=[video_embedded1, output_label]) | |
| input_text2.change(lambda input: inputs_change( | |
| input, 2), inputs=input_text2, outputs=[video_embedded2, output_label]) | |
| demo.launch() | |