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import json
import librosa
import numpy as np
import PIL.Image as Image
import tensorflow as tf
import streamlit as st
from streamlit_extras.add_vertical_space import add_vertical_space
from warnings import filterwarnings
filterwarnings('ignore')
def streamlit_config():
# page configuration
st.set_page_config(page_title='Classification', layout='centered')
# page header transparent color
page_background_color = """
<style>
[data-testid="stHeader"]
{
background: rgba(0,0,0,0);
}
</style>
"""
st.markdown(page_background_color, unsafe_allow_html=True)
# title and position
st.markdown(f'<h1 style="text-align: center;">Bird Sound Classification</h1>',
unsafe_allow_html=True)
add_vertical_space(4)
# Streamlit Configuration Setup
streamlit_config()
def prediction(audio_file):
# Load the Prediction JSON File to Predict Target_Label
with open('prediction.json', mode='r') as f:
prediction_dict = json.load(f)
# Extract the Audio_Signal and Sample_Rate from Input Audio
audio, sample_rate =librosa.load(audio_file)
# Extract the MFCC Features and Aggrigate
mfccs_features = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40)
mfccs_features = np.mean(mfccs_features, axis=1)
# Reshape MFCC features to match the expected input shape for Conv1D both batch & feature dimension
mfccs_features = np.expand_dims(mfccs_features, axis=0)
mfccs_features = np.expand_dims(mfccs_features, axis=2)
# Convert into Tensors
mfccs_tensors = tf.convert_to_tensor(mfccs_features, dtype=tf.float32)
# Load the Model and Prediction
model = tf.keras.models.load_model('model.h5')
prediction = model.predict(mfccs_tensors)
# Find the Maximum Probability Value
target_label = np.argmax(prediction)
# Find the Target_Label Name using Prediction_dict
predicted_class = prediction_dict[str(target_label)]
confidence = round(np.max(prediction)*100, 2)
add_vertical_space(1)
st.markdown(f'<h2 style="text-align: center; color: orange;">{confidence}% Match Found</h2>',
unsafe_allow_html=True)
st.markdown(f'<h2 style="text-align: center; color: green;">{predicted_class}</h2>',
unsafe_allow_html=True)
_, col2, _ = st.columns([0.1,0.9,0.1])
with col2:
input_audio = st.file_uploader(label='Upload the Audio', type=['mp3', 'wav'])
if input_audio is not None:
_, col2, _ = st.columns([0.2,0.8,0.2])
with col2:
prediction(input_audio)
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