:chore: routine checks

app.py

@@ -1,67 +1,61 @@
-# Import required libraries
-import os
-import re
-import logging
 import whisper
 from pytube import YouTube
 import gradio as gr
+import os
+import re
+import logging
 
-# Setup logging
 logging.basicConfig(level=logging.INFO)
-
-# 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.
-    """
+def get_text(url):
+    #try:
+    if url != '':
+        output_text_transcribe = ''
+
     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=".")
-    return out_file
+    out_file=video.download(output_path=".")
 
-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:
-        logging.error('Videos for transcription on this space are limited to about 1.5 hours...')
-        return ''
+    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
     
-    base, ext = os.path.splitext(out_file)
-    new_file = base + '.mp3'
-    os.rename(out_file, new_file)
-    
-    result = model.transcribe(new_file)
-    return result['text'].strip()
+        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
 
-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)
+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>")
+    
+    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_summary = gr.Button('2. Create Summary')
+    #output_text_summary = gr.Textbox(placeholder='Summary of the YouTube video transcript.', label='Summary')
     
-    demo.queue(default_enabled=None).launch(debug=True)
+    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)
 
-# Launch the Gradio interface
-if __name__ == "__main__":
-    create_gradio_interface()
+demo.queue(default_enabled = True).launch(debug = True)