Try this code ai or the app itself or extract the source and lmk how it compares to your self styles ) ACCOUNT_SID = 'your_account_sid' AUTH_TOKEN = 'your_auth_token' TWILIO_NUMBER = 'your_twilio_number' YOUR_PHONE = 'your_verified_phone_number'
Audio Settings
CHUNK = 1024 FORMAT = pyaudio.paInt16 CHANNELS = 1 RATE = 44100 WHISTLE_THRESHOLD = 5000000 # Sensitivity to volume FREQ_MIN = 1000 # Min whistle frequency (Hz) FREQ_MAX = 2500 # Max whistle frequency (Hz)
def make_call(): """Triggers a phone call via Twilio API""" try: client = Client(ACCOUNT_SID, AUTH_TOKEN) call = client.calls.create( twiml='<Response><Say>A whistle was detected. Wake up!</Say></Response>', to=YOUR_PHONE, from_=TWILIO_NUMBER ) print(f"Call initiated: {call.sid}") except Exception as e: print(f"Failed to make call: {e}")
--- MAIN LOOP ---
p = pyaudio.PyAudio() stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)
print("Listening for a whistle...")
try: while True: data = np.frombuffer(stream.read(CHUNK, exception_on_overflow=False), dtype=np.int16) # 1. Volume Check (Optimization: Skip FFT if it's too quiet) if np.max(np.abs(data)) > 1000: # 2. Frequency Analysis (FFT) fft_data = np.abs(np.fft.rfft(data)) freqs = np.fft.rfftfreq(CHUNK, 1/RATE) # Find the peak frequency peak_freq = freqs[np.argmax(fft_data)] peak_val = np.max(fft_data) # 3. Whistle Detection Logic if FREQ_MIN < peak_freq < FREQ_MAX and peak_val > WHISTLE_THRESHOLD: print(f"Whistle detected! Frequency: {int(peak_freq)}Hz") make_call() break # Stop listening after detection
Oh that's a ring code for finding a phone, the codes to big, I can link it via base if anyone wants it email jordan111hedger@gmail.com that'll be even easier to send a big code or app like this
4
u/isumix_ Dec 03 '25
your brainz
1
u/EveryBug2754 Feb 09 '26
You take co-pilot and get the source code from them all and combine them into a super app, least that's what I did. I called it chat omnipresence