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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
import pydub
import time
# MODEL LOADING and INITIALISATION
model = torch.jit.load("snorenetv1_small.ptl")
model.eval()
endReached = False
snore = 0
other = 0
s=0
n=16000
# Audio parameters
st.sidebar.markdown(
"""
<div align="justify">
<h4>ABOUT</h4>
<p>Transform your sleep experience with the cutting-edge Snore Detector by Hypermind Labs!
Discover the power to monitor and understand your nighttime sounds like never before.
Take control of your sleep quality and uncover the secrets of your peaceful slumber with our innovative app.</p>
</div>
""",
unsafe_allow_html=True,
)
st.title('Real-Time Snore Detection App 😴')
uploaded_file = st.file_uploader("Upload Sample", type=["wav"])
if uploaded_file is not None:
st.write("Saving File.....")
audio = pydub.AudioSegment.from_wav(uploaded_file)
audio.export(uploaded_file.name, format='wav')
time.sleep(2)
st.write("File Saved!")
st.write("Analysing...")
while(endReached==False):
sr, waveform = wavfile.read(uploaded_file.name)
input_tensor = torch.tensor(waveform[s:n]).unsqueeze(0).to(torch.float32)
result = model(input_tensor)
if np.abs(result[0][0]) > np.abs(result[0][1]):
other += 1
else:
snore += 1
s += 16000
n += 16000
if(n >= len(waveform)):
endReached = True
# PERCENTAGE OF SNORING PLOT
if endReached == True:
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 Snoring')
plt.xlim(0, 100)
for i, percentage in enumerate(percentages):
plt.text(percentage, i, f' {percentage:.2f}%', va='center')
st.write("DONE")
st.pyplot(plt)
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