"""
API Client for VTT with Diarization Hugging Face Space
Usage example for calling the space via Gradio Client API
"""

from gradio_client import Client
import os

# Your Hugging Face Space URL
SPACE_URL = "MahmoudElsamadony/vtt-with-diariazation"

def transcribe_audio(
    audio_file_path: str,
    language: str = "ar",
    enable_diarization: bool = False,
    beam_size: int = 5,
    best_of: int = 5,
):
    """
    Transcribe audio file using the Hugging Face Space API
    
    Args:
        audio_file_path: Path to the audio file (mp3, wav, m4a, etc.)
        language: Language code ("ar", "en", "fr", etc.) or "" for auto-detect
        enable_diarization: Whether to enable speaker diarization
        beam_size: Beam size for Whisper (1-10)
        best_of: Best of parameter for Whisper (1-10)
    
    Returns:
        tuple: (transcript_text, detailed_json)
    """
    # Initialize the client
    client = Client(SPACE_URL)
    
    # Call the transcribe function
    result = client.predict(
        audio_path=audio_file_path,
        language=language,
        enable_diarization=enable_diarization,
        beam_size=beam_size,
        best_of=best_of,
        api_name="/predict"
    )
    
    return result


def main():
    """Example usage of the API client"""
    
    # Example 1: Basic transcription (Arabic, no diarization)
    print("=" * 60)
    print("Example 1: Basic Arabic transcription")
    print("=" * 60)
    
    # Replace with your actual audio file path
    audio_file = "path/to/your/audio.mp3"
    
    if os.path.exists(audio_file):
        transcript, details = transcribe_audio(
            audio_file_path=audio_file,
            language="ar",
            enable_diarization=False,
        )
        
        print(f"\nTranscript:\n{transcript}\n")
        print(f"Language: {details.get('language')}")
        print(f"Duration: {details.get('duration')} seconds")
        print(f"Number of segments: {len(details.get('segments', []))}")
    else:
        print(f"Audio file not found: {audio_file}")
    
    print("\n" + "=" * 60)
    print("Example 2: Transcription with speaker diarization")
    print("=" * 60)
    
    # Example 2: Transcription with diarization
    if os.path.exists(audio_file):
        transcript, details = transcribe_audio(
            audio_file_path=audio_file,
            language="ar",
            enable_diarization=True,
            beam_size=5,
            best_of=5,
        )
        
        print(f"\nTranscript:\n{transcript}\n")
        
        # Print speaker turns
        if "speakers" in details:
            print("\nSpeaker turns:")
            for turn in details["speakers"][:5]:  # Show first 5 turns
                print(f"  {turn['speaker']}: {turn['start']:.2f}s - {turn['end']:.2f}s")
        
        # Print segments with speakers
        print("\nSegments with speakers:")
        for segment in details.get("segments", [])[:3]:  # Show first 3 segments
            speaker = segment.get("speaker", "Unknown")
            text = segment.get("text", "")
            start = segment.get("start", 0)
            print(f"  [{start:.2f}s] {speaker}: {text}")
    else:
        print(f"Audio file not found: {audio_file}")
    
    print("\n" + "=" * 60)
    print("Example 3: Auto-detect language")
    print("=" * 60)
    
    # Example 3: Auto-detect language
    if os.path.exists(audio_file):
        transcript, details = transcribe_audio(
            audio_file_path=audio_file,
            language="",  # Empty string for auto-detect
            enable_diarization=False,
        )
        
        print(f"\nDetected language: {details.get('language')}")
        print(f"Language probability: {details.get('language_probability'):.2%}")
        print(f"\nTranscript:\n{transcript}")
    else:
        print(f"Audio file not found: {audio_file}")


if __name__ == "__main__":
    # Install gradio_client first:
    # pip install gradio_client
    
    main()