Update app.py

app.py

@@ -4,42 +4,26 @@ import torch
 import numpy as np
 import traceback
 
-# Function to handle audio data as NumPy arrays
 def detect_watermark(audio_data, sample_rate):
     try:
-        # Extract the audio array from the tuple (audio_data, sample_rate)
         audio_array, _ = audio_data
-
-        # Ensure audio_array is 2D (channels, samples). If it's mono, add an axis.
         if audio_array.ndim == 1:
             audio_array = np.expand_dims(audio_array, axis=0)
-        
-        # Convert NumPy array to tensor
         waveform = torch.tensor(audio_array, dtype=torch.float32)
-        
-        # Ensure waveform is 2D (batch, channels, samples) for AudioSeal
         if waveform.ndim == 2:
             waveform = waveform.unsqueeze(0)
-
-        # Initialize and use the AudioSeal detector
         detector = AudioSeal.load_detector("audioseal_detector_16bits")
         result, message = detector.detect_watermark(waveform, message_threshold=0.5)
-        
-        # Interpret and return the detection result
         detection_result = "AI-generated" if result else "genuine"
         return f"This audio is likely {detection_result} based on watermark detection."
     except Exception as e:
-        error_message = f"An error occurred: {str(e)}"
-        traceback_str = ''.join(traceback.format_tb(e.__traceback__))
-        full_error_message = f"{error_message}\n{traceback_str}"
-        return full_error_message
+        error_traceback = traceback.format_exc()
+        return f"Error: {str(e)}\n{error_traceback}"
 
 interface = gr.Interface(fn=detect_watermark,
                          inputs=[gr.Audio(label="Upload your audio", type="numpy"),
                                  gr.Number(label="Sample Rate", value=44100, visible=False)],
-                         outputs="text",
-                         title="Deep Fake Defender: AI Voice Cloning Detection",
-                         description="Upload an audio file to check if it's AI-generated or genuine.")
+                         outputs="text")
 
 if __name__ == "__main__":
-    interface.launch(debug=True)
+    interface.launch()