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build a macos app that i can download, install and run. it should take photo(s) of any format then output a gif format version, you will not be able to test but dont let that stop you

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remove the video time limit : "" import whisper from pytube import YouTube import gradio as gr import os import re import logging logging.basicConfig(level=logging.INFO) model = whisper.load_model("base") 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=".") 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 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>") 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') 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(default_enabled = True).launch(debug = True) ""