:chore: checking

app.py

@@ -1,61 +1,67 @@
-import whisper
-from pytube import YouTube
-import gradio as gr
+# Import required libraries
 import os
 import re
 import logging
+import whisper
+from pytube import YouTube
+import gradio as gr
 
+# Setup logging
 logging.basicConfig(level=logging.INFO)
-model = whisper.load_model("base")
 
-def get_text(url):
-    #try:
-    if url != '':
-        output_text_transcribe = ''
+# Load the Whisper model
+model = whisper.load_model("base")
 
+def download_audio_from_youtube(url):
+    """
+    Download the audio from a YouTube video and return the path to the audio file.
+    """
     yt = YouTube(url)
-    #video_length = yt.length --- doesn't work anymore - using byte file size of the audio file instead now
-    #if video_length < 5400:
     video = yt.streams.filter(only_audio=True).first()
-    out_file=video.download(output_path=".")
+    out_file = video.download(output_path=".")
+    return out_file
 
+def get_text(url):
+    """
+    Transcribe the audio from a YouTube video and return the transcript.
+    """
+    if not url:
+        return ''
+    
+    out_file = download_audio_from_youtube(url)
     file_stats = os.stat(out_file)
-    logging.info(f'Size of audio file in Bytes: {file_stats.st_size}')
     
-    if file_stats.st_size <= 30000000:
-        base, ext = os.path.splitext(out_file)
-        new_file = base+'.mp3'
-        os.rename(out_file, new_file)
-        a = new_file
+    logging.info(f'Size of audio file in Bytes: {file_stats.st_size}')
     
-        result = model.transcribe(a)
-        return result['text'].strip()
-    else:
-        logging.error('Videos for transcription on this space are limited to about 1.5 hours. Sorry about this limit but some joker thought they could stop this tool from working by transcribing many extremely long videos. Please visit https://steve.digital to contact me about this space.')
-    #finally:
-    #    raise gr.Error("Exception: There was a problem transcribing the audio.")
-
-def get_summary(article):
-    first_sentences = ' '.join(re.split(r'(?<=[.:;])\s', article)[:5])
-    b = summarizer(first_sentences, min_length = 20, max_length = 120, do_sample = False)
-    b = b[0]['summary_text'].replace(' .', '.').strip()
-    return b
-
-with gr.Blocks() as demo:
-    gr.Markdown("<h1><center>Free Fast YouTube URL Video-to-Text using <a href=https://openai.com/blog/whisper/ target=_blank>OpenAI's Whisper</a> 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>")
-    gr.Markdown("<center>Enter the link of any YouTube video to generate a text transcript of the video.</center>")
-    gr.Markdown("<center><b>'Whisper is a neural net that approaches human level robustness and accuracy on English speech recognition.'</b></center>")
-    gr.Markdown("<center>Transcription takes 5-10 seconds per minute of the video (bad audio/hard accents slow it down a bit). #patience<br />If you have time while waiting, drop a ♥️ and check out my <a href=https://www.artificial-intelligence.blog target=_blank>AI blog</a> (opens in new tab).</center>")
+    if file_stats.st_size > 30000000:
+        logging.error('Videos for transcription on this space are limited to about 1.5 hours...')
+        return ''
     
-    input_text_url = gr.Textbox(placeholder='Youtube video URL', label='YouTube URL')
-    result_button_transcribe = gr.Button('Transcribe')
-    output_text_transcribe = gr.Textbox(placeholder='Transcript of the YouTube video.', label='Transcript')
+    base, ext = os.path.splitext(out_file)
+    new_file = base + '.mp3'
+    os.rename(out_file, new_file)
     
-    #result_button_summary = gr.Button('2. Create Summary')
-    #output_text_summary = gr.Textbox(placeholder='Summary of the YouTube video transcript.', label='Summary')
+    result = model.transcribe(new_file)
+    return result['text'].strip()
+
+def create_gradio_interface():
+    """
+    Create and launch a Gradio interface for transcribing YouTube videos.
+    """
+    with gr.Blocks() as demo:
+        gr.Markdown("<h1><center>Free Fast YouTube URL Video-to-Text using <a href=https://openai.com/blog/whisper/ target=_blank>OpenAI's Whisper</a> Model</center></h1>")
+        gr.Markdown("<center>Enter the link of any YouTube video to generate a text transcript of the video.</center>")
+        gr.Markdown("<center><b>'Whisper is a neural net that approaches human level robustness and accuracy on English speech recognition.'</b></center>")
+        gr.Markdown("<center>Transcription takes 5-10 seconds per minute of the video. #patience<br />If you have time while waiting, check out my <a href=https://www.artificial-intelligence.blog target=_blank>AI blog</a> (opens in new tab).</center>")
+        
+        input_text_url = gr.Textbox(placeholder='Youtube video URL', label='YouTube URL')
+        result_button_transcribe = gr.Button('Transcribe')
+        output_text_transcribe = gr.Textbox(placeholder='Transcript of the YouTube video.', label='Transcript')
+        
+        result_button_transcribe.click(get_text, inputs=input_text_url, outputs=output_text_transcribe)
     
-    result_button_transcribe.click(get_text, inputs = input_text_url, outputs = output_text_transcribe)
-    #result_button_summary.click(get_summary, inputs = output_text_transcribe, outputs = output_text_summary)
+    demo.queue().launch(debug=True)
 
-demo.queue(default_enabled = True).launch(debug = True)
+# Launch the Gradio interface
+if __name__ == "__main__":
+    create_gradio_interface()