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| from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled, NoTranscriptFound, VideoUnavailable | |
| from urllib.parse import urlparse, parse_qs | |
| import gradio as gr | |
| from transformers import pipeline | |
| # Load Hugging Face summarization model | |
| text_summary = pipeline("summarization", model="sshleifer/distilbart-xsum-12-6") | |
| # Extract video ID from YouTube URL | |
| def get_video_id(youtube_url): | |
| query = urlparse(youtube_url) | |
| if query.hostname == 'youtu.be': | |
| return query.path[1:] | |
| elif query.hostname in ['www.youtube.com', 'youtube.com']: | |
| if query.path == '/watch': | |
| return parse_qs(query.query).get('v', [None])[0] | |
| elif query.path.startswith('/embed/') or query.path.startswith('/v/'): | |
| return query.path.split('/')[2] | |
| return None | |
| # Fetch transcript from video ID | |
| def fetch_transcript(video_url): | |
| video_id = get_video_id(video_url) | |
| if not video_id: | |
| return "❌ Invalid YouTube URL." | |
| try: | |
| transcript = YouTubeTranscriptApi.get_transcript(video_id) | |
| return " ".join([entry['text'] for entry in transcript]) | |
| except (NoTranscriptFound, TranscriptsDisabled, VideoUnavailable) as e: | |
| return f"❌ {str(e)}" | |
| except Exception: | |
| try: | |
| transcript_list = YouTubeTranscriptApi.list_transcripts(video_id) | |
| transcript = transcript_list.find_transcript(['en']) | |
| return " ".join([entry['text'] for entry in transcript.fetch()]) | |
| except Exception as e2: | |
| return f"❌ Error fetching transcript: {str(e2)}" | |
| # Split long text safely into small chunks | |
| def split_text(text, max_words=500): | |
| words = text.split() | |
| chunks = [] | |
| for i in range(0, len(words), max_words): | |
| chunk = " ".join(words[i:i+max_words]) | |
| chunks.append(chunk) | |
| return chunks | |
| # Main function: fetch + summarize any transcript length | |
| def summarize_youtube_video(url): | |
| transcript = fetch_transcript(url) | |
| if transcript.startswith("❌"): | |
| return transcript | |
| try: | |
| words = transcript.split() | |
| word_count = len(words) | |
| if word_count <= 500: | |
| summary = text_summary(transcript, max_length=150, min_length=60, do_sample=False) | |
| return summary[0]['summary_text'] | |
| chunks = split_text(transcript, max_words=500) | |
| partial_summaries = [] | |
| for chunk in chunks: | |
| summary = text_summary(chunk, max_length=150, min_length=60, do_sample=False) | |
| partial_summaries.append(summary[0]['summary_text']) | |
| combined_summary = " ".join(partial_summaries) | |
| # Final summary of all summaries | |
| final_summary = text_summary(combined_summary, max_length=200, min_length=80, do_sample=False) | |
| return final_summary[0]['summary_text'] | |
| except Exception as e: | |
| return f"❌ Error during summarization: {str(e)}" | |
| # Gradio UI | |
| gr.close_all() | |
| demo = gr.Interface( | |
| fn=summarize_youtube_video, | |
| inputs=gr.Textbox(label="Enter YouTube Video URL", lines=1, placeholder="https://youtu.be/..."), | |
| outputs=gr.Textbox(label="Video Summary", lines=10), | |
| title="@RosangenAi Project 2: YouTube Video Summarizer", | |
| description="Paste any YouTube video link. This app will fetch and summarize even long transcripts using Hugging Face models." | |
| ) | |
| demo.launch() | |