app.py

import os
import datetime
import hashlib
import requests
import numpy as np
import gradio as gr
import whisper
import srt
import torch

LANGUAGE_OPTIONS = {
    "Afrikaans": "af",
    "Arabic": "ar",
    "Azerbaijani": "az",
    "Belarusian": "be",
    "Bulgarian": "bg",
    "Bengali": "bn",
    "Catalan": "ca",
    "Czech": "cs",
    "Welsh": "cy",
    "Danish": "da",
    "German": "de",
    "Greek": "el",
    "English": "en",
    "Spanish": "es",
    "Estonian": "et",
    "Persian": "fa",
    "Finnish": "fi",
    "French": "fr",
    "Irish": "ga",
    "Galician": "gl",
    "Gujarati": "gu",
    "Hebrew": "he",
    "Hindi": "hi",
    "Croatian": "hr",
    "Hungarian": "hu",
    "Armenian": "hy",
    "Indonesian": "id",
    "Icelandic": "is",
    "Italian": "it",
    "Japanese": "ja",
    "Georgian": "ka",
    "Kazakh": "kk",
    "Khmer": "km",
    "Kannada": "kn",
    "Korean": "ko",
    "Lithuanian": "lt",
    "Latvian": "lv",
    "Macedonian": "mk",
    "Malayalam": "ml",
    "Mongolian": "mn",
    "Marathi": "mr",
    "Malay": "ms",
    "Maltese": "mt",
    "Nepali": "ne",
    "Dutch": "nl",
    "Norwegian": "no",
    "Odia": "or",
    "Punjabi": "pa",
    "Polish": "pl",
    "Portuguese": "pt",
    "Romanian": "ro",
    "Russian": "ru",
    "Sinhala": "si",
    "Slovak": "sk",
    "Slovenian": "sl",
    "Albanian": "sq",
    "Serbian": "sr",
    "Swedish": "sv",
    "Swahili": "sw",
    "Tamil": "ta",
    "Telugu": "te",
    "Thai": "th",
    "Turkish": "tr",
    "Ukrainian": "uk",
    "Urdu": "ur",
    "Vietnamese": "vi",
    "Chinese": "zh"
}

def transcribe_audio(audio_file_path, model_size='base', language="en"):
    model = whisper.load_model(model_size)
    model.to("cpu")
    result = model.transcribe(audio_file_path, language=language)
    transcription = result["text"]
    segments = result["segments"]

    try:
        from whisper.utils import format_srt
        srt_text = format_srt(segments)
    except Exception:
        srt_text = generate_srt(segments)
    
    return transcription, srt_text, segments

def generate_srt(segments):
    import datetime
    import srt
    subtitles = []
    for i, seg in enumerate(segments):
        start_td = datetime.timedelta(seconds=seg["start"])
        end_td = datetime.timedelta(seconds=seg["end"])
        subtitle = srt.Subtitle(index=i+1, start=start_td, end=end_td, content=seg["text"])
        subtitles.append(subtitle)
    return srt.compose(subtitles)

def prepare_chapter_prompt(srt_text):
    system_prompt = (
        "You are a highly skilled video content segmentation and optimization expert. "
        "Your task is to analyze a transcript of a YouTube video provided in SRT format and produce engaging and concise chapter headers. "
        "Each chapter header must be on its own line in the exact format: 'mm:ss Chapter Title'.\n\n"
        "- 'mm:ss' represents the starting time of the chapter (minutes and seconds).\n"
        "- 'Chapter Title' must be a catchy, audience-friendly title that summarizes the key idea or transition at that point in the video.\n\n"
        "IMPORTANT: Although these instructions are in English, please ensure that your output is in the same language as the provided SRT transcript."
    )
    user_prompt = (
        "Below is the transcript of a YouTube video in SRT format:\n\n"
        "```\n"
        f"{srt_text}\n"
        "```\n\n"
        "Please generate only the chapter breakdown using the guidelines above. "
        "Each chapter header should be formatted as:\n"
        "mm:ss Chapter Title"
    )
    return system_prompt + "\n\n" + user_prompt

def format_prompt_html(prompt):
    """
    Displays the prompt in a read-only textarea using Gradio's color variables for background and text.
    Includes a 'Copy Prompt' button (blue) and a short 'Prompt Copied!' confirmation message.
    """
    html_content = f"""
    <div style="display: flex; flex-direction: column; gap: 10px; margin-top: 10px;">
      <textarea id="prompt_text" rows="10" 
        style="width: 100%; resize: vertical; 
               background-color: var(--block-background-fill); 
               color: var(--block-text-color); 
               border: 1px solid var(--block-border-color); 
               border-radius: 4px;"
        readonly>{prompt}</textarea>
      <button 
        style="width: 150px; padding: 8px; 
               background-color: #007bff; 
               color: white; 
               border: none; 
               border-radius: 4px; 
               cursor: pointer;"
        onclick="
          navigator.clipboard.writeText(document.getElementById('prompt_text').value);
          const copiedMsg = document.getElementById('copied_msg');
          copiedMsg.style.display = 'inline';
          setTimeout(() => copiedMsg.style.display = 'none', 2000);
        ">
        Copy Prompt
      </button>
      <span id="copied_msg" style="display: none; color: var(--primary-text-color); font-weight: bold;">Prompt Copied!</span>
    </div>
    """
    return html_content

def process_audio(audio, language_name):
    lang_code = LANGUAGE_OPTIONS.get(language_name, "en")
    try:
        transcription, srt_text, segments = transcribe_audio(audio, model_size='base', language=lang_code)
    except Exception as e:
        return f"Error during transcription: {str(e)}", "", ""
    
    chapter_prompt = prepare_chapter_prompt(srt_text)
    prompt_html = format_prompt_html(chapter_prompt)
    return transcription, srt_text, prompt_html

iface = gr.Interface(
    fn=process_audio,
    inputs=[
        gr.Audio(type="filepath", label="Upload Audio"),
        gr.Dropdown(choices=list(LANGUAGE_OPTIONS.keys()), label="Audio Language", value="English")
    ],
    outputs=[
        gr.Textbox(label="Full Transcription", lines=10),
        gr.Textbox(label="SRT File Content", lines=10),
        gr.HTML(label="Prepared Chapter Prompt (Copy & Paste into ChatGPT)")
    ],
    title="Video Chapter Splitter from Audio (MP3)",
    description=(
        "Upload an audio file (e.g., MP3) of your YouTube video and select the audio language. "
        "The app will transcribe the audio using Whisper, generate subtitles in SRT format, "
        "and prepare a single, complete prompt that instructs ChatGPT/GPT-4 to generate a chapter breakdown in the format 'mm:ss Chapter Title'.\n\n"
        "Click the 'Copy Prompt' button to copy the entire prompt, and a brief 'Prompt Copied!' message will appear."
    )
)

if __name__ == "__main__":
    iface.launch()