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()


# 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 😴')

upload_file = st.file_uploader("Upload wav file", type=["wav"])
if upload_file is not None:
    file_details = {
        "Filename": upload_file.name,
        "Filesize":f"{upload_file.size / 1024:.2f} KB",
        "File Type": upload_file.type,
    }
    st.write("File Details:", file_details)

    with open("saved_file.wav", "wb") as f:
        f.write(upload_file.getvalue())

# wav_audio_data = None
# 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('saved_file.wav')
snore = 0
other = 0
s=0
n=16000
endReached = False

while(endReached==False):
    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

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.pyplot(plt)