Upload app.py

app.py

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+# -*- 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"
+
+!pip install -U -q pytube transformers
+
+!pip -q install gradio==3.45.0
+
+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="https://www.youtube.com/watch?v=")
+        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 model 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()