from sys import byteorder
from array import array
from struct import pack

import pyaudio
import wave
import time
start = time.time()

form_1 = pyaudio.paInt16 # 16-bit resolution
chans = 1 # 1 channel
samp_rate = 44100 # 44.1kHz sampling rate
chunk = 4096 # 2^12 samples for buffer
record_secs = 2 # seconds to record
dev_index = 2 # device index found by p.get_device_info_by_index(ii)
wav_output_filename = 'recordedsound.wav' # name of .wav file

THRESHOLD = 300
CHUNK_SIZE = 1024
FORMAT = pyaudio.paInt16
RATE = 44100

import RPi.GPIO as GPIO # Import Raspberry Pi GPIO library
GPIO.setwarnings(False) # Ignore warning for now
GPIO.setmode(GPIO.BOARD) # Use physical pin numbering
GPIO.setup(8, GPIO.OUT, initial=GPIO.LOW) # Set pin 8 to be an output pin 

from preprocess import *
import keras
import tensorflow as tf
from keras.models import model_from_json

# load json and create model
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights("model.h5")
print("Loaded model from disk")


#print("Press button")
end = time.time()
print(end - start)

def is_silent(snd_data):
    "Returns 'True' if below the 'silent' threshold"
    return max(snd_data) < THRESHOLD

def normalize(snd_data):
    "Average the volume out"
    MAXIMUM = 16384
    times = float(MAXIMUM)/max(abs(i) for i in snd_data)

    r = array('h')
    for i in snd_data:
        r.append(int(i*times))
    return r

def trim(snd_data):
    "Trim the blank spots at the start and end"
    def _trim(snd_data):
        snd_started = False
        r = array('h')

        for i in snd_data:
            if not snd_started and abs(i)>THRESHOLD:
                snd_started = True
                r.append(i)

            elif snd_started:
                r.append(i)
        return r

    # Trim to the left
    snd_data = _trim(snd_data)

    # Trim to the right
    snd_data.reverse()
    snd_data = _trim(snd_data)
    snd_data.reverse()
    return snd_data

def add_silence(snd_data, seconds):
    "Add silence to the start and end of 'snd_data' of length 'seconds' (float)"
    silence = [0] * int(seconds * RATE)
    r = array('h', silence)
    r.extend(snd_data)
    r.extend(silence)
    return r

def record():
    """
    Record a word or words from the microphone and 
    return the data as an array of signed shorts.

    Normalizes the audio, trims silence from the 
    start and end, and pads with 0.5 seconds of 
    blank sound to make sure VLC et al can play 
    it without getting chopped off.
    """
    p = pyaudio.PyAudio()
    stream = p.open(format=FORMAT, channels=1, rate=RATE,
        input=True, output=True,
        frames_per_buffer=CHUNK_SIZE)

    num_silent = 0
    snd_started = False

    r = array('h')

    while 1:
        # little endian, signed short
        snd_data = array('h', stream.read(CHUNK_SIZE, exception_on_overflow = False))
        if byteorder == 'big':
            snd_data.byteswap()
        r.extend(snd_data)

        silent = is_silent(snd_data)

        if silent and snd_started:
            num_silent += 1
        elif not silent and not snd_started:
            snd_started = True

        if snd_started and num_silent > 30:
            break

    sample_width = p.get_sample_size(FORMAT)
    stream.stop_stream()
    stream.close()
    p.terminate()

    r = normalize(r)
    r = trim(r)
    r = add_silence(r, 0.5)
    return sample_width, r

def record_to_file(path):
    "Records from the microphone and outputs the resulting data to 'path'"
    sample_width, data = record()
    data = pack('<' + ('h'*len(data)), *data)

    wf = wave.open(path, 'wb')
    wf.setnchannels(1)
    wf.setsampwidth(sample_width)
    wf.setframerate(RATE)
    wf.writeframes(data)
    wf.close()
    
    folder_path = r'recordedsound.wav'
    start = time.time()
    sample_1 = wav2mfcc(folder_path)
    sample_1_reshaped = sample_1.reshape(1, 20, 25, 1)
    print("Result:  ", get_labels()[0][np.argmax(model.predict(sample_1_reshaped))])
    end = time.time()
    print(end - start)
    print("\n")
            
    if  get_labels()[0][np.argmax(model.predict(sample_1_reshaped))] == 'Sheila':
        for x in range(0, 5):
            GPIO.output(8,GPIO.HIGH)
            time.sleep(0.5)
            GPIO.output(8,GPIO.LOW)
            time.sleep(0.5)
                  
    GPIO.output(8,GPIO.LOW)

if __name__ == '__main__':
    while True:
        print("please speak a word into the microphone")
        record_to_file('recordedsound.wav')
        print("done - result written to recordedsound.wav")