Create app.py

app.py

@@ -0,0 +1,51 @@
+import gradio as gr
+import tensorflow as tf
+import librosa
+import numpy as np
+
+# Load the saved model
+model = tf.keras.models.load_model("voice_authentication_model.keras")
+
+# Function to extract features from audio
+def extract_features(file_path):
+    try:
+        # Load the audio file
+        audio, sample_rate = librosa.load(file_path, res_type='kaiser_fast', duration=3, sr=None)
+        
+        # Extract MFCC features (13 MFCC coefficients)
+        mfcc = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=13)
+        mfcc = np.mean(mfcc.T, axis=0)  # Taking the mean over time
+        
+        return mfcc
+    except Exception as e:
+        print(f"Error encountered while parsing file: {file_path}. Error: {e}")
+        return None
+
+# Prediction function for user vs non-user
+def predict_user_or_nonuser(audio):
+    # Extract features from the uploaded audio
+    feature = extract_features(audio.name)  # Audio file is passed as Gradio interface's input
+    if feature is None:
+        return "Error: Unable to process audio"
+    
+    # Reshape feature to match the model's input format (1, time_steps, 1)
+    feature = feature.reshape(1, feature.shape[0], 1)
+    
+    # Make prediction
+    prediction = model.predict(feature)
+    
+    # Return result: User or Non-User
+    return "User" if prediction > 0.5 else "Non-User"
+
+# Create a Gradio interface for the app
+iface = gr.Interface(
+    fn=predict_user_or_nonuser,  # Function to call for prediction
+    inputs=gr.Audio(source="upload", type="file"),  # Audio input
+    outputs="text",  # Output: Text label ("User" or "Non-User")
+    title="Voice Authentication",
+    description="This application can authenticate users based on their voice. Upload an audio file to check if it's from the user or a non-user."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch()