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| """ | |
| YouTube Video Summarizer – Gradio Space | |
| """ | |
| import os | |
| import gradio as gr | |
| import whisper | |
| from pytube import YouTube | |
| from transformers import pipeline | |
| import torch | |
| # ── 1. Load models once at startup ------------------------- | |
| print("⏳ Loading Whisper base model...") | |
| whisper_model = whisper.load_model("base") # 74 M params, fast on CPU | |
| print("⏳ Loading BART-large-cnn...") | |
| summarizer = pipeline("summarization", | |
| model="facebook/bart-large-cnn", | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32) | |
| # ── 2. Core pipeline --------------------------------------- | |
| def summarize_video(url: str) -> str: | |
| try: | |
| # a) Download audio stream | |
| yt = YouTube(url) | |
| audio_stream = yt.streams.filter(only_audio=True).first() | |
| if not audio_stream: | |
| return "❌ No audio stream found." | |
| audio_file = audio_stream.download(filename="audio.mp4") | |
| # b) Transcribe | |
| result = whisper_model.transcribe(audio_file) | |
| transcript = result["text"] | |
| # c) Summarize (BART has 1024 token limit; chunk if necessary) | |
| max_chunk = 900 # tokens | |
| chunks = [transcript[i:i+max_chunk] for i in range(0, len(transcript), max_chunk)] | |
| summary_parts = [] | |
| for chunk in chunks: | |
| summary_parts.append( | |
| summarizer(chunk, | |
| max_length=120, | |
| min_length=30, | |
| do_sample=False)[0]["summary_text"] | |
| ) | |
| summary = " ".join(summary_parts) | |
| # cleanup | |
| os.remove(audio_file) | |
| return summary | |
| except Exception as e: | |
| return f"⚠️ Error: {e}" | |
| # ── 3. Gradio UI ------------------------------------------- | |
| iface = gr.Interface( | |
| fn=summarize_video, | |
| inputs=gr.Textbox(label="Paste YouTube URL"), | |
| outputs=gr.Textbox(label="Summary", lines=10), | |
| title="YouTube Video Summarizer 🤖", | |
| description="Paste any public YouTube link and get an AI-generated summary in ~30 s." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch(server_name="0.0.0.0", server_port=7860) |