import whisper
import numpy as np
import subprocess
import time
import sys
from config import RTSP_URL, MISTY_IP

def run_test_inference():
    print(f"--- Finaler Test-Lauf (SDK-Struktur) ---")
    
    # Modell laden
    print("Lade Whisper-Modell...")
    model = whisper.load_model("base")
    
    # FFmpeg-Befehl mit mehr "Geduld" (analyzeduration & probesize)
    # Das hilft, wenn Misty den Stream langsam startet
    command = [
        'ffmpeg',
        '-rtsp_transport', 'tcp',
        '-analyzeduration', '5000000',
        '-probesize', '5000000',
        '-i', RTSP_URL,
        '-ar', '16000',
        '-ac', '1',
        '-f', 's16le',
        '-'
    ]

    print(f"\nVersuche Verbindung zu: {RTSP_URL}")
    # Startet den Prozess
    process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    
    audio_buffer = []
    print("[MISTY HÖRT ZU] Sprich jetzt... (Sammle 10 Sekunden Audio)")
    
    start_time = time.time()
    try:
        # Wir sammeln jetzt erst mal stumpf 10 Sekunden, um den Puffer zu füllen
        while time.time() - start_time < 10:
            raw_chunk = process.stdout.read(3200)
            if raw_chunk:
                audio_buffer.append(np.frombuffer(raw_chunk, dtype=np.int16))
                sys.stdout.write(".")
                sys.stdout.flush()

        process.terminate()
        
        if not audio_buffer:
            # Wenn nichts kam, schauen wir in den Error-Log von FFmpeg
            _, stderr = process.communicate()
            print(f"\n❌ FFmpeg Fehler-Log:\n{stderr.decode()}")
            return

        print("\n\nAnalyse startet...")
        full_audio = np.concatenate(audio_buffer).astype(np.float32) / 32768.0
        result = model.transcribe(full_audio, language="de")
        
        print("\n" + "="*40)
        print(f"ERGEBNIS: {result['text'].strip()}")
        print("="*40 + "\n")

    except Exception as e:
        print(f"Fehler: {e}")
    finally:
        process.kill()

if __name__ == "__main__":
    run_test_inference()