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| import streamlit as st | |
| from urllib.parse import urlparse, parse_qs | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline | |
| # https://pypi.org/project/youtube-transcript-api/ | |
| from youtube_transcript_api import YouTubeTranscriptApi | |
| def get_video_id(url: str) -> str: | |
| """ | |
| Examples: | |
| - http://youtu.be/SA2iWivDJiE | |
| - http://www.youtube.com/watch?v=_oPAwA_Udwc&feature=feedu | |
| - http://www.youtube.com/embed/SA2iWivDJiE | |
| - http://www.youtube.com/v/SA2iWivDJiE?version=3&hl=en_US | |
| """ | |
| query = urlparse(url) | |
| if query.hostname == 'youtu.be': | |
| return query.path[1:] | |
| if query.hostname in ('www.youtube.com', 'youtube.com'): | |
| if query.path == '/watch': | |
| p = parse_qs(query.query) | |
| return p['v'][0] | |
| if query.path[:7] == '/embed/': | |
| return query.path.split('/')[2] | |
| if query.path[:3] == '/v/': | |
| return query.path.split('/')[2] | |
| return None | |
| def get_youtube_subtitle(video_id: str) -> str: | |
| try: | |
| parse = YouTubeTranscriptApi.get_transcript(video_id, languages=['ru']) | |
| result = '' | |
| for i in parse: | |
| if (i['text'][0] =='[') & (i['text'][-1] ==']'): continue | |
| result += ' ' + i['text'] | |
| result = result.strip()[0].upper() + result.strip()[1:] | |
| return result.strip() | |
| except: | |
| return None | |
| if __name__ == "__main__": | |
| st.header("Annotation of subtitles from YouTube") | |
| # st.text('Load model...') | |
| # m_name = '/content/drive/MyDrive/Colab Notebooks/Netology/diplom_neto/summarize1' | |
| m_name = "csebuetnlp/mT5_multilingual_XLSum" | |
| # tokenizer = AutoTokenizer.from_pretrained(m_name) | |
| # model = AutoModelForSeq2SeqLM.from_pretrained(m_name) | |
| # st.text('Model is loaded') | |
| url = st.text_input('Enter the URL of the Youtube video', 'https://www.youtube.com/watch?v=HGSVsK32rKA') | |
| video_id = get_video_id(url) | |
| if video_id is not None: | |
| subtitle = get_youtube_subtitle(video_id) | |
| if subtitle is not None: | |
| st.subheader('Subtitles') | |
| st.text(subtitle) | |
| st.text('Compute summary...') | |
| # inputs = tokenizer( | |
| # [subtitle], | |
| # max_length=600, | |
| # padding="max_length", | |
| # truncation=True, | |
| # return_tensors="pt", | |
| # )["input_ids"] | |
| # # inputs = tokenizer(subtitle, return_tensors="pt").input_ids | |
| # outputs = model.generate(inputs, max_new_tokens=100, do_sample=False) | |
| # summary = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| translator = pipeline("summarization", model=m_name, | |
| tokenizer=m_name, max_length=100, device=0 | |
| ) | |
| st.subheader('Summary') | |
| st.text(translator(subtitle)) | |
| else: | |
| st.write('Subtitles are disabled for this video') | |
| else: | |
| st.write('Video clip is not detected') | |