Update app.py

app.py

@@ -2,13 +2,14 @@ import gradio as gr
 from audioseal import AudioSeal
 import torch
 import torchaudio
+import traceback
 
 def detect_watermark(audio_filepath):
     try:
         # Load the audio file using torchaudio
         waveform, sample_rate = torchaudio.load(audio_filepath)
 
-        # Ensure waveform is 2D (channels, samples). If it's mono, add an axis.
+        # Check waveform dimensions and adjust if necessary
         if waveform.ndim == 2:
             waveform = waveform.unsqueeze(0)  # Add batch dimension
 
@@ -19,12 +20,16 @@ def detect_watermark(audio_filepath):
         # 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:
-        # Return the error message directly
-        return f"Error occurred: {e}"
-
-# Define Gradio interface
+        # Log the error with traceback for debugging
+        error_message = f"An error occurred: {str(e)}"
+        error_traceback = traceback.format_exc()
+        full_error_message = f"{error_message}\n\n{error_traceback}"
+        print(full_error_message)  # Consider logging to a file if print statements are not visible
+        return full_error_message
+
+# Define Gradio interface with "filepath" type for audio input
 interface = gr.Interface(
     fn=detect_watermark,
     inputs=gr.Audio(label="Upload your audio", type="filepath"),
@@ -35,4 +40,3 @@ interface = gr.Interface(
 
 if __name__ == "__main__":
     interface.launch()
-