app.py

from transformers import pipeline
from youtube_transcript_api import YouTubeTranscriptApi

def summarize(youtube_video):

  video_id = youtube_video.split("=")[1]
  transcript = YouTubeTranscriptApi.get_transcript(video_id)
  
  summarizer = pipeline('summarization')

  result = ""
  for i in transcript:
      result += ' ' + i['text']

  num_iters = int(len(result)/1000)
  summarized_text = []
  for i in range(0, num_iters + 1):
    start = 0
    start = i * 1000
    end = (i + 1) * 1000
    print("input text \n" + result[start:end])
    out = summarizer(result[start:end])
    out = out[0]
    out = out['summary_text']
    print("Summarized text\n"+out)
    summarized_text.append(out)

    inp = result.replace('\n','')
    summary = summarizer(inp)
    return summary
    
import gradio as gr

grad= gr.Interface(fn=summarize, inputs="text", outputs="text")
grad.launch()