app.py

import gradio as gr
import yt_dlp
import os
import shutil
import subprocess
from faster_whisper import WhisperModel

# ===============================
# 1. Whisper Model (Lazy Load)
# ===============================
model = None

def load_model():
    global model
    if model is None:
        print("📥 Loading Whisper Model...")
        model = WhisperModel("base", device="cpu", compute_type="int8")
        print("✅ Model Loaded")
    return model

# ===============================
# 2. FFmpeg Path
# ===============================
def get_ffmpeg_path():
    path = shutil.which("ffmpeg")
    return path if path else "/usr/bin/ffmpeg"

# ===============================
# 3. Convert Video → Audio
# ===============================
def extract_audio(video_path):
    audio_path = "uploaded_audio.wav"
    if os.path.exists(audio_path):
        os.remove(audio_path)

    cmd = [
        get_ffmpeg_path(),
        "-i", video_path,
        "-vn",
        "-ac", "1",
        "-ar", "16000",
        audio_path,
        "-y"
    ]
    subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
    return audio_path

# ===============================
# 4. Download Audio from ANY URL
# ===============================
def download_audio_from_url(url):
    output = "url_audio.%(ext)s"

    ydl_opts = {
        "format": "bestaudio/best",
        "outtmpl": output,
        "ffmpeg_location": os.path.dirname(get_ffmpeg_path()),
        "postprocessors": [{
            "key": "FFmpegExtractAudio",
            "preferredcodec": "wav",
            "preferredquality": "192",
        }],
        "quiet": True,
        "nocheckcertificate": True,
    }

    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        ydl.download([url])

    return "url_audio.wav"

# ===============================
# 5. Main Transcribe Logic
# ===============================
def transcribe_media(url_input, file_input):

    try:
        audio_path = None

        # ---------- FILE UPLOAD ----------
        if file_input:
            ext = os.path.splitext(file_input)[1].lower()

            if ext in [".mp3", ".wav", ".m4a"]:
                audio_path = file_input
            else:
                audio_path = extract_audio(file_input)

        # ---------- URL ----------
        elif url_input and url_input.strip():
            audio_path = download_audio_from_url(url_input)

        else:
            return "⚠️ Please paste a link or upload a file."

        if not os.path.exists(audio_path):
            return "❌ Audio processing failed."

        model = load_model()

        segments, _ = model.transcribe(
            audio_path,
            beam_size=1,
            vad_filter=True
        )

        text = " ".join(seg.text for seg in segments)
        return text.strip() if text else "⚠️ No speech detected."

    except Exception as e:
        return f"❌ Error: {str(e)}"

# ===============================
# 6. UI
# ===============================
css = """
.container {max-width: 900px; margin: auto;}
.gr-button-primary {
    background: linear-gradient(90deg,#ff416c,#ff4b2b);
    border: none;
    color: white;
}
"""

with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
    with gr.Column(elem_classes="container"):
        gr.Markdown("## 🚀 Universal Video Transcript Tool")
        gr.Markdown(
            "Supports **YouTube, TikTok, Instagram, Facebook, Twitter/X**\n\n"
            "**OR** upload video/audio file."
        )

        with gr.Tabs():
            with gr.TabItem("🔗 Paste Link"):
                url_in = gr.Textbox(
                    label="Video URL",
                    placeholder="https://youtube.com / tiktok.com / instagram.com"
                )
                btn_url = gr.Button("🎧 Transcribe Link", variant="primary")

            with gr.TabItem("📂 Upload File"):
                file_in = gr.File(
                    label="Upload Video / Audio",
                    file_types=[".mp4", ".mkv", ".mov", ".webm", ".avi", ".mp3", ".wav"]
                )
                btn_file = gr.Button("📂 Transcribe File", variant="primary")

        output = gr.Code(label="Transcript Output", language="markdown", lines=15)

    btn_url.click(transcribe_media, [url_in, gr.State(None)], output)
    btn_file.click(transcribe_media, [gr.State(None), file_in], output)

demo.launch()