app.py

import streamlit as st
from st_audiorec import st_audiorec
import matplotlib.pyplot as plt
import sounddevice as sd
import numpy as np
import pandas as pd
import torch
import torchaudio
import wave
import io
from scipy.io import wavfile
# MODEL LOADING and INITIALISATION
model = torch.jit.load("snorenetv1_small.ptl")
model.eval()

# Session state
if 'text' not in st.session_state:
    st.session_state['text'] = 'Listening...'
    st.session_state['run'] = False

# Audio parameters
st.sidebar.header('Audio Parameters')

FRAMES_PER_BUFFER = int(st.sidebar.text_input('Frames per buffer', 3200))
FORMAT = 'audio/wav'
CHANNELS = 1
RATE = int(st.sidebar.text_input('Rate', 16000))

# Open an audio stream

monitoring = False
audio_data = []

def start_monitoring():
    global monitoring
    st.session_state['run'] = True
    monitoring = True

def stop_monitoring():
    global monitoring
    st.session_state['run'] = False
    monitoring = False

st.title('🎙️ Real-Time Snore Detection App')

with st.expander('About this App'):
    st.markdown('''
    This streamlit app from Hypermind Labs Helps users detect
    how much they are snoring during their sleep.
    ''')



wav_audio_data = st_audiorec()
if wav_audio_data is not None:
    data = np.frombuffer(wav_audio_data, dtype=np.int16)
    st.write(len(data))
    duration = len(data)//110000
    num_of_samples = len(data)
    sample_rate = num_of_samples // duration
    # data = np.array(wav_audio_data, dtype=float)
    max_abs_value = np.max(np.abs(data))
    np_array = (data/max_abs_value) * 32767
    scaled_data = np_array.astype(np.int16).tobytes()
    with io.BytesIO() as fp, wave.open(fp, mode="wb") as waveobj:
        waveobj.setnchannels(1)
        waveobj.setframerate(96000)
        waveobj.setsampwidth(2)
        waveobj.setcomptype("NONE", "NONE")
        waveobj.writeframes(scaled_data)
        wav_make = fp.getvalue()
    
    with open("output.wav", 'wb') as wav_file:
        wav_file.write(wav_make)
    sr, waveform = wavfile.read('output.wav')
    input_tensor = torch.tensor(waveform[:16000]).unsqueeze(0).to(torch.float32)
    st.write(input_tensor.shape)
    result = model(input_tensor)
    if np.abs(result[0][0]) > np.abs(result[0][1]):
        st.write("NON_SNORING")
    else:
        st.write("SNORING")

    
    # PERCENTAGE OF SNORING PLOT


    
    # waveform, sample_rate = torchaudio.load('test/0_10.wav')
    # resampler = T.Resample(sample_rate, RESAMPLE_RATE, dtype=waveform.dtype)
    # signal = resampler(waveform)
    # signal = torch.mean(signal, dim=0, keepdim=True)
    # ptl_model(signal)

    # snore = 0
    # other = 0

    # for row in model:
    #     for element in row:
    #         if element > 0.5:
    #             snore += 1
    #         else:
    #             other += 1

    # total = snore + other
    # snore_percentage = (snore / total) * 100
    # other_percentage = (other / total) * 100

    # categories = ["Snore", "Other"]
    # percentages = [snore_percentage, other_percentage]

    # plt.figure(figsize=(8, 4))
    # plt.barh(categories, percentages, color=['#ff0033', '#00ffee'])
    # plt.xlabel('Percentage')
    # plt.title('Percentage of "Snore" and "Other"')
    # plt.xlim(0, 100)

    # for i, percentage in enumerate(percentages):
    #     plt.text(percentage, i, f' {percentage:.2f}%', va='center')

    # st.pyplot(plt)