import sys, json, base64
import numpy as np
import traceback

def main():
    from RealtimeSTT import AudioToTextRecorder
    from scipy.signal import resample
    import websockets
    import threading
    import logging
    import wave
    import json
    import time

    # Inicializá el modelo (usa tu clase personalizada)
    model = AudioToTextRecorder(
        model="base",
        compute_type="int8",
        language="es"
    )

    print(json.dumps({"type": "log", "msg": "✅ Worker iniciado con modelo Whisper"}), flush=True)

    for line in sys.stdin:
        try:
            request = json.loads(line.strip())
            if request["event"] == "transcribe":
                sample_rate = request.get("sample_rate", 16000)
                audio_data = base64.b64decode(request["audio_base64"])

                audio_float32 = (
                    np.frombuffer(audio_data, dtype=np.int16).astype(np.float32) / 32768.0
                )

                segments, _ = model.transcribe(audio_float32, sample_rate=sample_rate)
                text = " ".join([s.text for s in segments if s.text.strip()])
                print(json.dumps({"type": "realtime", "text": text}), flush=True)
            elif request["event"] == "set_param":
                setattr(model, request["param"], request["value"])
                print(json.dumps({"type": "log", "msg": f"Set {request['param']} to {request['value']}"}), flush=True)

            else:
                print(json.dumps({"type": "log", "msg": f"Evento desconocido: {request.get('event')}"}), flush=True)

        except Exception as e:
            print(json.dumps({
                "type": "error",
                "error": str(e),
                "trace": traceback.format_exc()
            }), flush=True)

if __name__ == "__main__":
    main()