Create app.py

app.py

@@ -0,0 +1,46 @@
+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(url):
+  article = get_text(url)
+  b = summarizer(article)
+  b = b[0]['summary_text']
+  return b
+  
+with gr.Blocks() as demo:
+  gr.Markdown("<h1><center>Youtube video transcription with OpenAI's Whisper</center></h1>")
+  gr.Markdown("<center>Enter the link of any youtube video to get the transcription of the video and a summary of the video in the form of text.</center>")
+  with gr.Tab('Get the transcription of any Youtube video'):
+    with gr.Row():
+      input_text_1 = gr.Textbox(placeholder='Enter the Youtube video URL', label='URL')
+      output_text_1 = gr.Textbox(placeholder='Transcription of the video', label='Transcription')
+    result_button_1 = gr.Button('Get Transcription')
+  with gr.Tab('Summary of Youtube video'):
+    with gr.Row():
+      input_text = gr.Textbox(placeholder='Enter the Youtube video URL', label='URL')
+      output_text = gr.Textbox(placeholder='Summary text of the Youtube Video', label='Summary')
+    result_button = gr.Button('Get Summary')
+
+  result_button.click(get_summary, inputs = input_text, outputs = output_text)
+  result_button_1.click(get_text, inputs = input_text_1, outputs = output_text_1)
+demo.launch(debug=True)