#!/usr/bin/env python3
"""
Voice Activity Detection + Speaker Diarization
Simple demo script using the modular pipeline
"""

import torch
import librosa
import numpy as np
from pathlib import Path
import os
import sys

# Import from modular components
from src.vad import SileroVAD
from src.diarization import SpeakerDiarization
from src.pipeline import VADDiarizationPipeline
from src.utils import create_test_audio

def setup_vad():
    """Setup Silero VAD using modular wrapper"""
    print("Setting up Voice Activity Detection...")
    
    vad = SileroVAD(threshold=0.5)
    print("✓ Silero VAD loaded (40 MB)")
    
    return vad

def setup_diarization():
    """Setup Speaker Diarization using modular wrapper"""
    print("Setting up Speaker Diarization...")
    print("⚠️  First download requires 1GB+ bandwidth (one-time)")
    
    # Get token from environment or use provided one
    token = os.environ.get('HF_TOKEN', 'your_token_here')
    
    try:
        diarization = SpeakerDiarization(
            model_name="pyannote/speaker-diarization-3.1",
            use_auth_token=token
        )
        print("✓ Diarization pipeline loaded")
        return diarization
    except Exception as e:
        print(f"❌ Error: {e}")
        print("Get your HF token: https://huggingface.co/settings/tokens")
        print("Or set it: export HF_TOKEN='your_token_here'")
        return None

def demo_vad(audio_path, vad_model):
    """Demo VAD on an audio file using modular wrapper"""
    print(f"\nVAD Analysis: {audio_path}")
    
    timestamps, processing_time = vad_model.process_file(audio_path)
    
    print(f"Found {len(timestamps)} speech segments:")
    print(f"Processing time: {processing_time:.2f}ms")
    
    for i, ts in enumerate(timestamps, 1):
        start_s = ts['start']
        end_s = ts['end']
        duration_s = end_s - start_s
        print(f"  Segment {i}: {start_s:6.2f}s - {end_s:6.2f}s ({duration_s:6.2f}s)")
    
    return timestamps

def demo_diarization(audio_path, diar_pipeline):
    """Demo Diarization on an audio file using modular wrapper"""
    print(f"\nDiarization Analysis: {audio_path}")
    
    segments, processing_time, metadata = diar_pipeline.process_file(audio_path)
    
    print(f"Found {metadata['num_speakers']} speakers")
    print(f"Processing time: {processing_time:.2f}ms")
    print("\nSpeaker timeline:")
    for seg in segments:
        print(f"  {seg['start']:6.2f}s - {seg['end']:6.2f}s: {seg['speaker']}")

def demo_full_pipeline(audio_path):
    """Demo the full integrated pipeline"""
    print(f"\n{'='*60}")
    print("FULL PIPELINE DEMO")
    print(f"{'='*60}")
    
    token = os.environ.get('HF_TOKEN')
    if not token:
        print("\n⚠️  No HF_TOKEN found. Running VAD only...")
        vad = SileroVAD()
        demo_vad(audio_path, vad)
        return
    
    try:
        # Initialize full pipeline
        pipeline = VADDiarizationPipeline(
            use_auth_token=token,
            vad_threshold=0.5
        )
        
        # Process file
        result = pipeline.process_file(audio_path)
        
        # Display formatted output
        print("\n" + pipeline.format_output(result, format='text'))
        
    except Exception as e:
        print(f"\n❌ Error: {e}")
        print("Falling back to VAD only...")
        vad = SileroVAD()
        demo_vad(audio_path, vad)

def main():
    print("\n" + "=" * 60)
    print("VOICE ACTIVITY DETECTION + SPEAKER DIARIZATION")
    print("=" * 60)
    
    # Create test audio
    print("\nCreating test audio...")
    audio_path = create_test_audio("test_audio.wav", duration=10.0)
    print(f"✓ Created {audio_path}")
    
    # Option 1: Quick VAD demo
    print("\n" + "=" * 60)
    print("OPTION 1: VAD ONLY (No HF token needed)")
    print("=" * 60)
    vad_model = setup_vad()
    demo_vad(audio_path, vad_model)
    
    # Option 2: Full pipeline (requires HF token)
    print("\n" + "=" * 60)
    print("OPTION 2: FULL PIPELINE (VAD + Diarization)")
    print("=" * 60)
    demo_full_pipeline(audio_path)
    
    print("\n" + "=" * 60)
    print("✅ Demo complete!")
    print("\nNext steps:")
    print("1. Set HF_TOKEN: export HF_TOKEN='your_token_here'")
    print("2. Run Gradio demo: python app.py")
    print("3. Test on real audio files")
    print("4. Deploy with Docker: docker build -t vad-diarization .")
    print("5. Check notebooks/demo.ipynb for detailed examples")
    print("=" * 60 + "\n")

if __name__ == "__main__":
    main()