Spaces:
Runtime error
Runtime error
File size: 4,841 Bytes
4822df2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
from math import ceil, floor
import streamlit.components.v1 as components
from transformers import (
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
import streamlit as st
import sys
import os
import json
from urllib.parse import quote
# Allow direct execution
sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), 'src')) # noqa
from predict import SegmentationArguments, ClassifierArguments, predict as pred, seconds_to_time # noqa
from evaluate import EvaluationArguments
from shared import device
st.set_page_config(
page_title="SponsorBlock ML",
page_icon="🤖",
# layout='wide',
# initial_sidebar_state="expanded",
menu_items={
'Get Help': 'https://github.com/xenova/sponsorblock-ml',
'Report a bug': 'https://github.com/xenova/sponsorblock-ml/issues/new/choose',
# 'About': "# This is a header. This is an *extremely* cool app!"
}
)
MODEL_PATH = 'Xenova/sponsorblock-small_v2022.01.19'
@st.cache(allow_output_mutation=True)
def persistdata():
return {}
# Faster caching system for predictions (No need to hash)
predictions_cache = persistdata()
@st.cache(allow_output_mutation=True)
def load_predict():
# Use default segmentation and classification arguments
evaluation_args = EvaluationArguments(model_path=MODEL_PATH)
segmentation_args = SegmentationArguments()
classifier_args = ClassifierArguments()
model = AutoModelForSeq2SeqLM.from_pretrained(evaluation_args.model_path)
model.to(device())
tokenizer = AutoTokenizer.from_pretrained(evaluation_args.model_path)
def predict_function(video_id):
if video_id not in predictions_cache:
predictions_cache[video_id] = pred(
video_id, model, tokenizer,
segmentation_args=segmentation_args,
classifier_args=classifier_args
)
return predictions_cache[video_id]
return predict_function
CATGEGORY_OPTIONS = {
'SPONSOR': 'Sponsor',
'SELFPROMO': 'Self/unpaid promo',
'INTERACTION': 'Interaction reminder',
}
# Load prediction function
predict = load_predict()
def main():
# Display heading and subheading
st.write('# SponsorBlock ML')
st.write('##### Automatically detect in-video YouTube sponsorships, self/unpaid promotions, and interaction reminders.')
# Load widgets
video_id = st.text_input('Video ID:', placeholder='e.g., axtQvkSpoto')
categories = st.multiselect('Categories:',
CATGEGORY_OPTIONS.keys(),
CATGEGORY_OPTIONS.keys(),
format_func=CATGEGORY_OPTIONS.get
)
# Hide segments with a confidence lower than
confidence_threshold = st.slider(
'Confidence Threshold (%):', min_value=0, max_value=100)
video_id_length = len(video_id)
if video_id_length == 0:
return
elif video_id_length != 11:
st.exception(ValueError('Invalid YouTube ID'))
return
with st.spinner('Running model...'):
predictions = predict(video_id)
if len(predictions) == 0:
st.success('No segments found!')
return
submit_segments = []
for index, prediction in enumerate(predictions, start=1):
if prediction['category'] not in categories:
continue # Skip
confidence = prediction['probability'] * 100
if confidence < confidence_threshold:
continue
submit_segments.append({
'segment': [prediction['start'], prediction['end']],
'category': prediction['category'].lower(),
'actionType': 'skip'
})
start_time = seconds_to_time(prediction['start'])
end_time = seconds_to_time(prediction['end'])
with st.expander(
f"[{prediction['category']}] Prediction #{index} ({start_time} \u2192 {end_time})"
):
url = f"https://www.youtube-nocookie.com/embed/{video_id}?&start={floor(prediction['start'])}&end={ceil(prediction['end'])}"
# autoplay=1controls=0&&modestbranding=1&fs=0
# , width=None, height=None, scrolling=False
components.iframe(url, width=670, height=376)
text = ' '.join(w['text'] for w in prediction['words'])
st.write(f"**Times:** {start_time} \u2192 {end_time}")
st.write(
f"**Category:** {CATGEGORY_OPTIONS[prediction['category']]}")
st.write(f"**Confidence:** {confidence:.2f}%")
st.write(f'**Text:** "{text}"')
json_data = quote(json.dumps(submit_segments))
link = f'[Submit Segments](https://www.youtube.com/watch?v={video_id}#segments={json_data})'
st.markdown(link, unsafe_allow_html=True)
if __name__ == '__main__':
main()
|