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


# 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:
    np_wav = np.frombuffer(wav_audio_data, dtype=np.int16)

    # PERCENTAGE OF SNORING PLOT


    model = np.random.rand(4, 5)

    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)