app.py

import gradio as gr
import yt_dlp
import os
import shutil
import subprocess
import tempfile
from faster_whisper import WhisperModel
from indic_transliteration import sanscript
from indic_transliteration.sanscript import transliterate
import torch

# ===============================
# 🔒 GLOBALS & CONFIG
# ===============================
MODEL_CACHE_DIR = "/tmp/qwen_whisper_cache"
os.makedirs(MODEL_CACHE_DIR, exist_ok=True)

# Lazy-loaded model (shared across calls)
_model = None

def load_whisper_model():
    global _model
    if _model is None:
        print("📥 Loading Whisper 'base' model (CPU/int8)...")
        _model = WhisperModel(
            "base",
            device="cpu",
            compute_type="int8",
            download_root=MODEL_CACHE_DIR
        )
        print("✅ Model loaded.")
    return _model

def get_ffmpeg():
    return shutil.which("ffmpeg") or "/usr/bin/ffmpeg"

# ===============================
# 📥 SAFE DOWNLOAD (YouTube, TikTok, etc.)
# ===============================
def download_video(url):
    video_path = os.path.join(tempfile.gettempdir(), "downloaded_video.mp4")
    if os.path.exists(video_path):
        os.remove(video_path)

    ydl_opts = {
        "format": "bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best",
        "outtmpl": video_path,
        "quiet": True,
        "nocheckcertificate": True,
        "noplaylist": True,        "extract_audio": False,
        "retries": 10,
        "fragment_retries": 10,
    }

    try:
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            info = ydl.extract_info(url, download=True)
            # Ensure file exists
            if not os.path.exists(video_path):
                raise FileNotFoundError("Download failed: no file created")
        return video_path, info.get("title", "Untitled")
    except Exception as e:
        raise RuntimeError(f"Download failed: {str(e)}")

# ===============================
# 🎧 EXTRACT AUDIO (robust)
# ===============================
def extract_audio(video_path):
    audio_path = os.path.join(tempfile.gettempdir(), "extracted_audio.wav")
    if os.path.exists(audio_path):
        os.remove(audio_path)

    cmd = [
        get_ffmpeg(),
        "-y",
        "-i", video_path,
        "-vn",
        "-ac", "1",
        "-ar", "16000",
        "-c:a", "pcm_s16le",
        audio_path
    ]

    try:
        result = subprocess.run(cmd, capture_output=True, text=True, timeout=60)
        if result.returncode != 0:
            raise RuntimeError(f"FFmpeg failed: {result.stderr}")
        if not os.path.exists(audio_path) or os.path.getsize(audio_path) < 5000:
            raise RuntimeError("Audio extraction produced empty/invalid file")
        return audio_path
    except subprocess.TimeoutExpired:
        raise RuntimeError("Audio extraction timed out (>60s)")

# ===============================
# 🌐 LANGUAGE-AWARE TRANSLITERATION & NORMALIZATION
# ===============================
def normalize_to_hindi(text):
    """Convert any script to Devanagari + clean up"""
    if not text.strip():        return ""
    
    # Step 1: Transliterate non-Devanagari scripts to Devanagari
    try:
        # Try Arabic → Devanagari (for Urdu)
        text = transliterate(text, sanscript.ARABIC, sanscript.DEVANAGARI)
        # Try Roman → Devanagari (for Hindi/English mixed)
        text = transliterate(text, sanscript.ITRANS, sanscript.DEVANAGARI)
    except Exception:
        pass  # fallback to raw text

    # Step 2: Clean punctuation & spacing
    import re
    text = re.sub(r'[^\u0900-\u097F\u0020\u002E\u002C\u003F\u0021\u003B\u003A\u002D\u0028\u0029]', '', text)
    text = re.sub(r'\s+', ' ', text).strip()
    text = re.sub(r'\.\s*\.', '.', text)  # fix .. → .
    text = re.sub(r'\?\s*\?', '?', text)
    text = re.sub(r'!\s*!', '!', text)

    # Step 3: Add proper full stops at end if missing
    if text and text[-1] not in "।.!?":
        text += "।"

    return text

# ===============================
# 🎯 CORE TRANSCRIBE FUNCTION (ALWAYS OUTPUT HINDI)
# ===============================
def transcribe_to_hindi(url=None, file=None, lang_choice="Auto Detect"):
    try:
        # ======== INPUT HANDLING ========
        if file:
            ext = os.path.splitext(file)[1].lower()
            if ext in [".mp3", ".wav", ".m4a", ".ogg"]:
                audio_path = file
                title = os.path.basename(file)
            else:
                video_path = file
                audio_path = extract_audio(video_path)
                title = os.path.basename(video_path)
        elif url:
            video_path, title = download_video(url)
            audio_path = extract_audio(video_path)
        else:
            return "⚠️ Please paste a URL or upload a file."

        # Safety check
        if not os.path.exists(audio_path) or os.path.getsize(audio_path) < 5000:
            return "❌ Audio file too small or missing. Try again."
        # ======== TRANSCRIPTION ========
        model = load_whisper_model()
        segments, info = model.transcribe(
            audio_path,
            beam_size=5,
            best_of=3,
            patience=1.0,
            temperature=(0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
            vad_filter=True,
            word_timestamps=False,
            language=None  # Auto-detect
        )

        raw_text = " ".join([seg.text for seg in segments]).strip()

        # ======== FORCE HINDI OUTPUT ========
        # Even if detected language is en/ur/tam, convert to Hindi script
        final_text = normalize_to_hindi(raw_text)

        # Optional: Add title & metadata
        header = f"🎬 {title[:50]}{'...' if len(title) > 50 else ''}\n"
        header += f"🌍 Detected: {info.language or 'Unknown'} → 🇮🇳 Output: Hindi (Devanagari)\n\n"

        return header + final_text

    except Exception as e:
        err_msg = str(e).lower()
        if "instagram" in err_msg:
            return (
                "❌ Instagram URLs are blocked on Hugging Face.\n\n"
                "✅ Solution: Download the video manually (e.g., via online downloader), then upload it here."
            )
        elif "timeout" in err_msg or "network" in err_msg:
            return "⚠️ Network timeout. Try again or upload file directly."
        else:
            return f"❌ Error: {str(e)[:200]}..."

# ===============================
# 🎨 MODERN UI (HUGGING FACE OPTIMIZED)
# ===============================
CSS = """
/* Glassmorphism + Dark Gradient */
body {
    background: radial-gradient(circle at top, #0c1445, #1a2a6c, #2c3e50);
    font-family: 'Inter', system-ui, sans-serif;
}
.glass-card {
    background: rgba(255, 255, 255, 0.07);
    backdrop-filter:);
    border-radius: 20px;    padding: 28px;
    box-shadow: 0 12px 32px rgba(0, 0, 0, 0.4);
    border: 1px solid rgba(255, 255, 255, 0.1);
}
.gr-button-primary {
    background: linear-gradient(135deg, #6a11cb 0%, #2575fc 100%);
    border: none;
    color: white;
    font-weight: 600;
    padding: 12px 24px;
    border-radius: 12px;
    transition: all 0.3s ease;
}
.gr-button-primary:hover {
    transform: translateY(-2px);
    box-shadow: 0 6px 15px rgba(37, 117252, 0.4);
}
.gr-input, .gr-textarea, .gr-dropdown {
    background: rgba(255, 255, 255, 0.08) !important;
    color: #e0e0ff !important;
    border: 1px solid rgba(255, 255, 255, 0.15) !important;
    border-radius: 10px;
}
.gr-markdown p, .gr-markdown h2 {
    color: #f0f4ff !important;
}
footer { display: none !important; }
.title {
    font-size: 2.2rem;
    font-weight: 800;
    background: linear-gradient(90deg, #ffd700, #ff8c00);
    -webkit-background-clip: text;
    background-clip: text;
    color: transparent;
    margin-bottom: 12px;
}
.subtitle {
    color: #a0d2eb;
    font-size: 1.1rem;
    margin-bottom: 24px;
}
.feature-badge {
    display: inline-block;
    background: rgba(106, 17, 203, 0.3);
    color: #ffd700;
    padding: 3px 10;
    border-radius: 20px;
    font-size: 0.85rem;
    margin: 0 4px;
}"""

with gr.Blocks(
    css=CSS,
    theme=gr.themes.Default(
        primary_hue=gr.themes.Color(c100="#6a11cb", c200="#2575fc", c300="#1a5fb4"),
        secondary_hue=gr.themes.Color(c100="#ff9e00", c200="#ff7b00"),
        neutral_hue=gr.themes.Color(c100="#1e293b", c200="#0f172a"),
    ),
    title="🗣️ AI Hindi Transcript Studio",
) as demo:
    with gr.Column(elem_classes=["glass-card"]):
        gr.HTML("<div class='title'>AI Hindi Transcript Studio</div>")
        gr.HTML("<div class='subtitle'>Upload or paste any video → Get clean Devanagari Hindi transcript instantly</div>")
        
        gr.Markdown(
            "✨ Supports: YouTube, TikTok, Facebook, Twitter/X, Instagram (via upload), local files<br>"
            "⚡ Zero ffprobe errors • Auto-script conversion • Real-time cleanup"
        )

        with gr.Tabs():
            with gr.TabItem("🔗 URL"):
                url_input = gr.Textbox(
                    label="🎥 Video URL",
                    placeholder="https://youtu.be/...",
                    info="Instagram? Upload file instead (HF restriction)"
                )
                btn_url = gr.Button("🔊 Transcribe to Hindi", variant="primary", size="lg")

            with gr.TabItem("📂 File"):
                file_input = gr.File(
                    label="📁 Upload Video/Audio",
                    file_types=["video", "audio"],
                    info="MP4, MOV, MP3, WAV, M4A, etc."
                )
                btn_file = gr.Button("📖 Convert to Hindi", variant="primary", size="lg")

        lang_dummy = gr.Dropdown(
            choices=["Auto (→ Hindi)"],
            value="Auto (→ Hindi)",
            interactive=False,
            visible=False
        )  # Hidden — we force Hindi output

        output_box = gr.Textbox(
            label="📝 Hindi Transcript (Devanagari)",
            lines=16,
            max_lines=25,
            show_copy_button=True,
            interactive=False,            elem_classes=["gr-textarea"]
        )

        gr.Markdown(
            "<div style='text-align:center; margin-top:20px; color:#a0d2eb; font-size:0.9rem;'>"
            "🚀 Powered by Faster-Whisper + Indic Transliteration | Deployed on Hugging Face Spaces"
            "</div>"
        )

    # Event bindings
    btn_url.click(
        fn=transcribe_to_hindi,
        inputs=[url_input, gr.State(None), lang_dummy],
        outputs=output_box
    )
    btn_file.click(
        fn=transcribe_to_hindi,
        inputs=[gr.State(None), file_input, lang_dummy],
        outputs=output_box
    )

# Optional: Enable queue for HF Spaces
demo.queue(concurrency_count=2, max_size=10)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)