Create app.py

app.py

@@ -0,0 +1,45 @@
+import whisper
+from pytube import YouTube
+from transformers import pipeline
+import gradio as gr
+import os
+
+model = whisper.load_model("base")
+summarizer = pipeline("summarization")
+
+def get_audio(url):
+  yt = YouTube(url)
+  video = yt.streams.filter(only_audio=True).first()
+  out_file=video.download(output_path=".")
+  base, ext = os.path.splitext(out_file)
+  new_file = base+'.mp3'
+  os.rename(out_file, new_file)
+  a = new_file
+  return a
+
+def get_text(url):
+  result = model.transcribe(get_audio(url))
+  return result['text']
+
+def get_summary(article):
+  print(article)
+  b = summarizer(article, min_length=5, max_length=20)
+  print(b)
+  #b = b[0]['summary_text']
+  return b
+  
+with gr.Blocks() as demo:
+  gr.Markdown("<h1><center>Free YouTube URL Video to Text using OpenAI's Whisper Model</center></h1>")
+  gr.Markdown("<center>Enter the link of any YouTube video to generate a text transcript of the video and then create a summary of the video transcript.</center>")
+
+  input_text_url = gr.Textbox(placeholder='Youtube video URL', label='URL')
+  result_button_transcribe = gr.Button('1. Transcribe')
+  output_text_transcribe = gr.Textbox(placeholder='Transcript of the YouTube video.', label='Transcript')
+
+  result_button = gr.Button('2. Create Summary')
+  output_text_summary = gr.Textbox(placeholder='Summary of the YouTube video transcript.', label='Summary')
+
+  result_button_1.click(get_text, inputs = input_text_url, outputs = output_text_transcribe)
+  result_button.click(get_summary, inputs = output_text_transcribe, outputs = output_text_summary)
+
+demo.launch(debug = True)