Update app.py

app.py

@@ -9,24 +9,35 @@ model = load_model('voice_authentication_model.keras')  # Replace with your mode
 
 # Function to extract MFCC features and make a prediction
 def predict_user_or_non_user(audio):
-    # Load the audio file using librosa
-    y, sr = librosa.load(audio, sr=None)  # sr=None to keep the original sampling rate
-    
-    # Extract MFCC features from the audio
-    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)  # You can adjust n_mfcc as needed
-    mfccs = np.mean(mfccs.T, axis=0)  # Take the mean of MFCCs over time to reduce dimension
-    
-    # Reshape the MFCCs to match the input shape expected by the model
-    mfccs = mfccs.reshape(1, -1)  # Reshape to 1 sample, with the number of features
-    
-    # Predict the class (user or non-user)
-    prediction = model.predict(mfccs)
-    
-    # Convert prediction to readable format
-    if prediction > 0.5:
-        return "User"
-    else:
-        return "Non-User"
+    try:
+        # Load the audio file using librosa
+        y, sr = librosa.load(audio, sr=None)  # sr=None to keep the original sampling rate
+        
+        # Optional: Normalize the audio volume (if necessary)
+        y = librosa.util.normalize(y)
+        
+        # Extract MFCC features from the audio
+        mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)  # You can adjust n_mfcc as needed
+        mfccs = np.mean(mfccs.T, axis=0)  # Take the mean of MFCCs over time to reduce dimension
+        
+        # Reshape the MFCCs to match the input shape expected by the model
+        mfccs = mfccs.reshape(1, -1)  # Reshape to 1 sample, with the number of features
+        
+        # Predict the class (user or non-user)
+        prediction = model.predict(mfccs)
+        
+        # Debugging: print raw model output
+        print(f"Raw Prediction Output: {prediction}")
+        
+        # Apply thresholding based on the raw prediction value
+        # If the model outputs a probability, try adjusting the threshold (e.g., 0.6 instead of 0.5)
+        if prediction[0] > 0.6:
+            return "User"
+        else:
+            return "Non-User"
+    except Exception as e:
+        print(f"Error in prediction: {e}")
+        return "Error during prediction"
 
 # Define the Gradio interface
 iface = gr.Interface(
@@ -37,4 +48,4 @@ iface = gr.Interface(
 )
 
 # Launch the interface
-iface.launch() 
+iface.launch()