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import gradio as gr | |
import numpy as np | |
import joblib | |
import librosa | |
from sklearn.preprocessing import StandardScaler | |
model = joblib.load('UTI.pkl') | |
def predictor(audio_filename): | |
y,sr = librosa.load(audio_filename) | |
chroma_stft = np.mean(librosa.feature.chroma_stft(y=y, sr=sr)) | |
rmse = np.mean(librosa.feature.rms(y=y)) | |
spec_cent = np.mean(librosa.feature.spectral_centroid(y=y, sr=sr)) | |
spec_bw = np.mean(librosa.feature.spectral_bandwidth(y=y, sr=sr)) | |
rolloff = np.mean(librosa.feature.spectral_rolloff(y=y, sr=sr)) | |
zcr = np.mean(librosa.feature.zero_crossing_rate(y)) | |
mfcc = librosa.feature.mfcc(y=y, sr=sr) | |
v = [] | |
for e in mfcc: | |
v.append(np.mean(e)) | |
mfcc1 = v[0] | |
mfcc2 = v[1] | |
mfcc3 = v[2] | |
mfcc4 = v[3] | |
mfcc5 = v[4] | |
mfcc6 = v[5] | |
mfcc7 = v[6] | |
mfcc8 = v[7] | |
mfcc9 = v[8] | |
mfcc10 = v[9] | |
mfcc11 = v[10] | |
mfcc12 = v[11] | |
mfcc13 = v[12] | |
mfcc14 = v[13] | |
mfcc15 = v[14] | |
mfcc16 = v[15] | |
mfcc17 = v[16] | |
mfcc18 = v[17] | |
mfcc19 = v[18] | |
mfcc20 = v[19] | |
features = np.array([[chroma_stft,rmse,spec_cent,spec_bw,rolloff,zcr,mfcc1,mfcc2,mfcc3,mfcc4,mfcc5,mfcc6,mfcc7,mfcc8,mfcc9,mfcc10,mfcc11,mfcc12,mfcc13,mfcc14,mfcc15,mfcc16,mfcc17,mfcc18,mfcc19,mfcc20]]) | |
prediction = model.predict(StandardScaler().fit_transform(features)) | |
if prediction[0] == 0: | |
result = 'Normal' | |
else: | |
result = 'Infected' | |
return result | |
app = gr.Interface(predictor, | |
inputs=gr.inputs.Audio(source="upload",label="Please Upload Audio file here:"), | |
outputs=gr.outputs.Textbox(label="Result"),title="SMART UTI DETECTOR",description="UTI Prediction Model",examples=[["normal 1_rn.wav"]]) | |
app.launch() | |