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app.py
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app.py
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from functools import partial
from math import ceil, floor
import streamlit.components.v1 as components
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 preprocess import get_words
from predict import PredictArguments, SegmentationArguments, predict as pred
from shared import GeneralArguments, seconds_to_time, CATGEGORY_OPTIONS
from utils import regex_search
from model import get_model_tokenizer_classifier
from errors import TranscriptError
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!"
}
)
YT_VIDEO_REGEX = r'''(?x)^
(?:
# http(s):// or protocol-independent URL
(?:https?://|//)
(?:(?:(?:(?:\w+\.)?[yY][oO][uU][tT][uU][bB][eE](?:-nocookie|kids)?\.com/|
youtube\.googleapis\.com/) # the various hostnames, with wildcard subdomains
(?:.*?\#/)? # handle anchor (#/) redirect urls
(?: # the various things that can precede the ID:
# v/ or embed/ or e/
(?:(?:v|embed|e)/(?!videoseries))
|(?: # or the v= param in all its forms
# preceding watch(_popup|.php) or nothing (like /?v=xxxx)
(?:(?:watch|movie)(?:_popup)?(?:\.php)?/?)?
(?:\?|\#!?) # the params delimiter ? or # or #!
# any other preceding param (like /?s=tuff&v=xxxx or ?s=tuff&v=V36LpHqtcDY)
(?:.*?[&;])??
v=
)
))
|(?:
youtu\.be # just youtu.be/xxxx
)/)
)? # all until now is optional -> you can pass the naked ID
# here is it! the YouTube video ID
(?P<id>[0-9A-Za-z_-]{11})'''
# https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints
# https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#experimental-t5-pre-trained-model-checkpoints
# https://huggingface.co/docs/transformers/model_doc/t5
# https://huggingface.co/docs/transformers/model_doc/t5v1.1
# Faster caching system for predictions (No need to hash)
@st.cache(persist=True, allow_output_mutation=True)
def create_prediction_cache():
return {}
@st.cache(persist=True, allow_output_mutation=True)
def create_function_cache():
return {}
prediction_cache = create_prediction_cache()
prediction_function_cache = create_function_cache()
MODELS = {
'Small (293 MB)': {
'pretrained': 'google/t5-v1_1-small',
'repo_id': 'Xenova/sponsorblock-small',
'num_parameters': '77M'
},
'Base v1 (850 MB)': {
'pretrained': 't5-base',
'repo_id': 'Xenova/sponsorblock-base-v1',
'num_parameters': '220M'
},
'Base v1.1 (944 MB)': {
'pretrained': 'google/t5-v1_1-base',
'repo_id': 'Xenova/sponsorblock-base-v1.1',
'num_parameters': '250M'
}
}
# Create per-model cache
for m in MODELS:
if m not in prediction_cache:
prediction_cache[m] = {}
CLASSIFIER_PATH = 'Xenova/sponsorblock-classifier-v2'
TRANSCRIPT_TYPES = {
'AUTO_MANUAL': {
'label': 'Auto-generated (fallback to manual)',
'type': 'auto',
'fallback': 'manual'
},
'MANUAL_AUTO': {
'label': 'Manual (fallback to auto-generated)',
'type': 'manual',
'fallback': 'auto'
},
# 'TRANSLATED': 'Translated to English' # Coming soon
}
def predict_function(model_id, model, tokenizer, segmentation_args, classifier, video_id, words, ts_type_id):
cache_id = f'{video_id}_{ts_type_id}'
if cache_id not in prediction_cache[model_id]:
prediction_cache[model_id][cache_id] = pred(
video_id, model, tokenizer,
segmentation_args=segmentation_args,
words=words,
classifier=classifier
)
return prediction_cache[model_id][cache_id]
def load_predict(model_id):
model_info = MODELS[model_id]
if model_id not in prediction_function_cache:
# Use default segmentation and classification arguments
predict_args = PredictArguments(model_name_or_path=model_info['repo_id'])
general_args = GeneralArguments()
segmentation_args = SegmentationArguments()
model, tokenizer, classifier = get_model_tokenizer_classifier(predict_args, general_args)
prediction_function_cache[model_id] = partial(
predict_function, model_id, model, tokenizer, segmentation_args, classifier)
return prediction_function_cache[model_id]
def create_button(text, url):
return f"""<div class="row-widget stButton" style="text-align: center">
<a href="{url}" target="_blank" rel="noopener noreferrer" class="btn-link">
<button kind="primary" class="btn">{text}</button>
</a>
</div>"""
def main():
st.markdown("""<style>
.btn {
display: inline-flex;
-webkit-box-align: center;
align-items: center;
-webkit-box-pack: center;
justify-content: center;
font-weight: 600;
padding: 0.25rem 0.75rem;
border-radius: 0.25rem;
margin: 0px;
line-height: 1.5;
color: inherit;
width: auto;
user-select: none;
background-color: inherit;
border: 1px solid rgba(49, 51, 63, 0.2);
}
.btn-link {
color: inherit;
text-decoration: none;
}
</style>""", unsafe_allow_html=True)
top = st.container()
output = st.empty()
# Display heading and subheading
top.markdown('# SponsorBlock ML')
top.markdown(
'##### Automatically detect in-video YouTube sponsorships, self/unpaid promotions, and interaction reminders.')
# Add controls
col1, col2 = top.columns(2)
with col1:
model_id = st.selectbox(
'Select model', MODELS.keys(), index=0, on_change=output.empty)
with col2:
ts_type_id = st.selectbox(
'Transcript type', TRANSCRIPT_TYPES.keys(), index=0, format_func=lambda x: TRANSCRIPT_TYPES[x]['label'], on_change=output.empty)
video_input = top.text_input('Video URL/ID:', on_change=output.empty)
categories = top.multiselect('Categories:',
CATGEGORY_OPTIONS.keys(),
CATGEGORY_OPTIONS.keys(),
format_func=CATGEGORY_OPTIONS.get, on_change=output.empty
)
# Hide segments with a confidence lower than
confidence_threshold = top.slider(
'Confidence Threshold (%):', min_value=0, value=50, max_value=100, on_change=output.empty)
if len(video_input) == 0: # No input, do not continue
return
# Load prediction function
with st.spinner('Loading model...'):
predict = load_predict(model_id)
with output.container(): # Place all content in output container
video_id = regex_search(video_input, YT_VIDEO_REGEX)
if video_id is None:
st.exception(ValueError('Invalid YouTube URL/ID'))
return
try:
with st.spinner('Downloading transcript...'):
words = get_words(video_id,
transcript_type=TRANSCRIPT_TYPES[ts_type_id]['type'],
fallback=TRANSCRIPT_TYPES[ts_type_id]['fallback']
)
except TranscriptError:
pass
if not words:
st.error('No transcript found!')
return
with st.spinner('Running model...'):
predictions = predict(video_id, words, ts_type_id)
if len(predictions) == 0:
st.success('No segments found!')
return
submit_segments = []
for index, prediction in enumerate(predictions, start=1):
category_key = prediction['category'].upper()
if category_key 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'],
'actionType': 'skip'
})
start_time = seconds_to_time(prediction['start'])
end_time = seconds_to_time(prediction['end'])
with st.expander(
f"[{category_key}] 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[category_key]}")
st.write(f"**Confidence:** {confidence:.2f}%")
st.write(f'**Text:** "{text}"')
if len(submit_segments) == 0:
st.success(
f'No segments found! ({len(predictions)} ignored due to filters/settings)')
return
num_hidden = len(predictions) - len(submit_segments)
if num_hidden > 0:
st.info(
f'{num_hidden} predictions hidden (adjust the settings and filters to view them all).')
json_data = quote(json.dumps(submit_segments))
link = f'https://www.youtube.com/watch?v={video_id}#segments={json_data}'
st.markdown(create_button('Submit Segments', link),
unsafe_allow_html=True)
st.markdown(f"""<div style="text-align: center;font-size: 16px;margin-top: 6px">
<a href="https://wiki.sponsor.ajay.app/w/Automating_Submissions" target="_blank" rel="noopener noreferrer">(Review before submitting!)</a>
</div>""", unsafe_allow_html=True)
if __name__ == '__main__':
main()