Upload app.py

app.py

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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, mono=True, duration=5)
+
+    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
+
+outputs = gr.outputs.Textbox()
+app = gr.Interface(fn=predictor, inputs='text', outputs=outputs,description="UTI Prediction Model")
+app.launch()