app.py

# -*- coding: utf-8 -*-
"""pod_to_sum_v3.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1rbZ98r1Z_IM0Z3VDuNQObxpuZf5KUgmL

### Initialization
"""

import os
save_dir= os.path.join('./','docs')
if not os.path.exists(save_dir):
    os.mkdir(save_dir)

transcription_model = "openai/whisper-base"
llm_model = "gmurro/bart-large-finetuned-filtered-spotify-podcast-summ"

import pandas as pd
import numpy as np
import pytube
from pytube import YouTube
import transformers
from transformers import pipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

"""### Define how to get transcript of the YT video"""

def get_transcript(url):
  yt_video = YouTube(str(url))
  yt_audio = yt_video.streams.filter(only_audio=True, file_extension='mp4').first() # get 1st available audio stream
  out_file = yt_audio.download(filename="audio.mp4", output_path = save_dir)

  asr = pipeline("automatic-speech-recognition", model=transcription_model, device=device)

  import librosa
  speech_array, sampling_rate = librosa.load(out_file, sr=16000) # getting audio file array

  audio_text = asr(
    speech_array,
    max_new_tokens=256,
    generate_kwargs={"task": "transcribe"},
    chunk_length_s=30,
    batch_size=8) # calling whisper model

  del(asr)
  torch.cuda.empty_cache() #deleting cache

  return audio_text['text']

"""### Define functions to generate summary"""

def clean_sent(sent_list):
    new_sent_list = [sent_list[0]]
    for i in range(len(sent_list)):
      if sent_list[i] != new_sent_list[-1]: new_sent_list.append(sent_list[i])
    return new_sent_list

import nltk
nltk.download('punkt')

def get_chunks (audio_text, sent_overlap, max_token, tokenizer):
  # pre-processing text
  sentences = nltk.tokenize.sent_tokenize(audio_text)
  sentences = clean_sent(sentences)

  first_sentence = 0
  last_sentence = 0
  chunks=[]
  while last_sentence <= len(sentences) - 1:
        last_sentence = first_sentence
        chunk_parts = []
        chunk_size = 0
        for sentence in sentences[first_sentence:]:
            sentence_sz = len(tokenizer.tokenize(sentence))
            if chunk_size + sentence_sz > max_token:
                break

            chunk_parts.append(sentence)
            chunk_size += sentence_sz
            last_sentence += 1

        chunks.append(" ".join(chunk_parts))
        first_sentence = last_sentence - sent_overlap
  return chunks

"""### Define how to get summary of the transcript"""

def get_summary(audio_text):
  import re
  audio_text = re.sub(r'\b(\w+) \1\b', r'\1', audio_text, flags=re.IGNORECASE) # cleaning text

  from transformers import AutoTokenizer
  tokenizer = AutoTokenizer.from_pretrained(llm_model) # set tockenizer

  from transformers import pipeline
  summarizer = pipeline("summarization", model=llm_model) # set summarizer

  model_max_tokens = tokenizer.model_max_length # get max tockens model can process
  text_tokens = len(tokenizer.tokenize(audio_text)) # get number of tockens in audio text

  def get_map_summary(chunk_text, summarizer):
      max_token = model_max_tokens - 2 #protect for "" before and after the text
      sent_overlap = 3 #overlapping sentences between 2 chunks
      sent_chunks = get_chunks(audio_text = chunk_text,sent_overlap = sent_overlap,max_token = max_token, tokenizer = tokenizer) # get chunks
      chunk_summary_list = summarizer(sent_chunks,min_length=50, max_length=200, batch_size=8) # get summary per chunk

      grouped_summary = ""
      for c in chunk_summary_list: grouped_summary += c['summary_text'] + " "


      return grouped_summary

  # check text requires map-reduce stategy
  map_text = audio_text
  long_summary = ""

  while text_tokens > model_max_tokens:
    map_summary = get_map_summary(chunk_text=map_text, summarizer=summarizer)
    text_tokens = len(tokenizer.tokenize(map_summary))
    long_summary = map_summary
    map_text = map_summary

  # else deploy reduce method
  else:
    max_token = round(text_tokens*0.3) # 1/3rd reduction
    final_summary = summarizer(map_text,min_length=35, max_length=max_token)
    final_summary = final_summary[0]["summary_text"]

  if long_summary == "": long_summary = "The video is too short to produce a descriptive summary"

  del(tokenizer, summarizer)
  torch.cuda.empty_cache() #deleting cache

  return final_summary, long_summary


"""### Defining Gradio App"""

import gradio as gr

import pytube
from pytube import YouTube

def get_youtube_title(url):
    yt = YouTube(str(url))
    return yt.title

def get_video(url):
    vid_id = pytube.extract.video_id(url)
    embed_html = '<iframe width="100%" height="315" src="https://www.youtube.com/embed/{}" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>'.format(vid_id)
    return embed_html

def summarize_youtube_video(url):
    print("URL:",url)
    text = get_transcript(url)
    print("Transcript:",text[:500])
    short_summary, long_summary = get_summary(text)
    print("Short Summary:",short_summary)
    print("Long Summary:",long_summary)
    return text, short_summary, long_summary

html = '<iframe width="100%" height="315" src="https://www.youtube.com/embed/" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>'

# Defining the structure of the UI
with gr.Blocks() as demo:
    with gr.Row():
        gr.Markdown("# Summarize a Long YouTube Video")

    with gr.Row():
        with gr.Column(scale=4):
            url = gr.Textbox(label="Enter YouTube video link here:",placeholder="Place for youtube link..")
        with gr.Column(scale=1):
            sum_btn = gr.Button("Summarize!")

    gr.Markdown("# Results")

    title = gr.Textbox(label="Video Title",placeholder="title...")

    with gr.Row():
        with gr.Column(scale=4):
          video = gr.HTML(html,scale=1)
        with gr.Column():
          with gr.Row():
            short_summary = gr.Textbox(label="Gist",placeholder="short summary...",scale=1)
          with gr.Row():
            long_summary = gr.Textbox(label="Summary",placeholder="long summary...",scale=2)


    with gr.Row():
        with gr.Group():
            text = gr.Textbox(label="Full Transcript",placeholder="transcript...",show_label=True)

    with gr.Accordion("Credits and Notes",open=False):
        gr.Markdown("""
                    1. Transcipt is generated by openai/whisper-base model by downloading YouTube video.\n
                    2. Summary is generated by gmurro/bart-large-finetuned-filtered-spotify-podcast-summ.\n
                    3. The app is possible because of Hugging Face Transformers.\n
                    """)

    # Defining the functions to call on clicking the button
    sum_btn.click(fn=get_youtube_title, inputs=url, outputs=title, api_name="get_youtube_title", queue=False)
    sum_btn.click(fn=summarize_youtube_video, inputs=url, outputs=[text, short_summary, long_summary], api_name="summarize_youtube_video", queue=True)
    sum_btn.click(fn=get_video, inputs=url, outputs=video, api_name="get_youtube_video", queue=False)

demo.queue()
demo.launch(share=False)