## libraries for data preprocessing
import numpy as np 
import pandas as pd 

## libraries for training dl models
import tensorflow as tf
from tensorflow import keras

## libraries for reading audio files 
import librosa as lib 


import gradio as gr 

  
## lets load the model 
model = keras.models.load_model('best_heartbeatsound_classification.h5')

def loading_sound_file(sound_file, sr=22050, duration=10):
    input_length = sr * duration
    X, sr = lib.load(sound_file, sr=sr, duration=duration) 
    dur = lib.get_duration(y=X, sr=sr)
    
    # # pad audio file same duration
    # if (round(dur) < duration):
    #     print ("fixing audio lenght :", file_name)
    #     y = lib.util.fix_length(X, input_length) 
    # extract normalized mfcc feature from data
    
    # ## pad audio to same duration 
    # if round(dur) < duration:
    #     X = lib.util.fix_length(X, input_length)

    # Pad or truncate audio file to the same duration
    if round(dur) < duration:
        pad_amount = input_length - len(X)
        X = np.pad(X, (0, pad_amount), mode='constant')
    elif round(dur) > duration:
        X = X[:input_length]

        
    mfccs = np.mean(lib.feature.mfcc(y=X, sr=sr, n_mfcc=25).T,axis=0)

    ## Reshape to match the model's input shape
    data = np.array(mfccs).reshape(1, -1, 1)

    return data



def heart_signal_classification(data):
    X = loading_sound_file(data)
    pred = model.predict(X)
    ## Define the threshold
    threshold = 0.6
    max_prob = np.max(pred)
    
    ## Create labels
    labels = {
        0: 'artifact',
        1: 'unlabel',
        2: 'extrastole',
        3: 'extrahls',
        4: 'normal',
        5: 'murmur'
    }
    
    if max_prob < threshold:
        label = 'unknown'
    else:
        result = pred[0].argmax()
        label = labels[result]
    
    return label
################### Gradio Web APP ################################ 
title = "Heart Signal Classification App"   
Input = gr.Audio(sources=["upload"], type="filepath")
Output1 = gr.Textbox(label="Type Of Heart Signal")
description = "Type Of Signal: Artifact, Murmur, Normal, Extrastole, Extrahls"
iface = gr.Interface(fn=heart_signal_classification, inputs=Input, outputs=Output1, title=title, description=description)

iface.launch(inline=False)