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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()
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