app.py

# import library
import gradio as gr
import librosa
import pandas as pd
import numpy as np
import pickle

import tensorflow as tf
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, Input

def get_waveform_label(file):
    lab = tf.strings.split(file, os.path.sep)[-2]
    audio_binary = tf.io.read_file(file)
    audio, _ = tf.audio.decode_wav(audio_binary)
    waveform=tf.squeeze(audio, axis=-1)
    return waveform

def get_spectrogram_label(audio):
    padding = tf.zeros([300000]-tf.shape(audio), dtype=tf.float32)
    wave = tf.cast(audio, tf.float32)
    eq_length = tf.concat([wave, padding], 0)
    spectrogram = tf.signal.stft(eq_length, frame_length=210, frame_step=110)    
    spectrogram = tf.abs(spectrogram)
    spectrogram = tf.expand_dims(spectrogram, -1)
    return spectrogram
    
 # %load saved model
model = pickle.load(open('audio_classifier_model.pkl', 'rb'))
    
def get_audio(audio):
  audio_waveform = get_waveform_label(audio)
  audio_spect = get_spectrogram_label(audio_waveform)
  final_feat = np.array([audio_spect])
  res = np.argmax(model.predict(final_feat),axis=1)
  if res == 1:
    res ="Dog Audio";
  else:
    res = "Cat Audio"
  return res
  
iface = gr.Interface(fn=get_audio,inputs=["audio", "audio"],title="CAT_DOG AUDIO CLASSIFICATION",outputs="html").launch()