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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
from huggingface_hub import hf_hub_download
# 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'
CLASSIFIER_PATH = 'Xenova/sponsorblock-classifier'
@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)
# Save classifier and vectorizer
hf_hub_download(repo_id=CLASSIFIER_PATH,
filename=classifier_args.classifier_file,
cache_dir=classifier_args.classifier_dir,
force_filename=classifier_args.classifier_file,
)
hf_hub_download(repo_id=CLASSIFIER_PATH,
filename=classifier_args.vectorizer_file,
cache_dir=classifier_args.classifier_dir,
force_filename=classifier_args.vectorizer_file,
)
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()
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