created the app

app.py

@@ -0,0 +1,58 @@
+## 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('heartbeatsound_classification.h5')
+
+def loading_sound_file(sound_file):
+    X, sr = librosa.load(sound_file, sr=sr, duration=duration) 
+    dur = librosa.get_duration(y=X, sr=sr)
+    
+    # pad audio file same duration
+    if (round(dur) < duration):
+        print ("fixing audio lenght :", file_name)
+        y = librosa.util.fix_length(X, input_length) 
+    # extract normalized mfcc feature from data
+    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sr, n_mfcc=25).T,axis=0)
+
+    data = np.array(mfccs).reshape([-1,1])
+
+    return data
+
+
+
+def heart_signal_classification(data):
+    x = np.array(image)
+    X = np.array([x])
+    X = preprocess_input(X)
+    pred = model.predict(X)
+    result = pred[0].argmax()
+    ## lets create our labels
+    labels = {
+        0: 'Artifact',
+        1: 'Murmur',
+        2: 'Normal' 
+    }
+
+    label = labels[pred[0].argmax()]
+    return label
+################### Gradio Web APP ################################ 
+title = "Heart Signal Classification App"   
+Input = gr.Audio(shape=(299, 299), label="Please Upload An Image")
+Output1 = gr.Textbox(label="Type Of Heart Signal)
+description = "Type Of Signal: Artifact, Murmur, Normal"
+iface = gr.Interface(fn=maize_disease_classifier, inputs=Input, outputs=Output1, title=title, description=description)
+
+iface.launch(inline=False)