Update app.py

app.py

@@ -1,19 +1,14 @@
 import gradio as gr
-import torchaudio
 from audioseal import AudioSeal
 import torch
 import numpy as np
 import traceback
-import logging
-
-# Set up logging to file in the current directory
-logging.basicConfig(filename='app.log', filemode='w', level=logging.DEBUG)
 
 # 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  # Gradio passes the sample_rate separately, so it's ignored here
+        audio_array, _ = audio_data
 
         # Ensure audio_array is 2D (channels, samples). If it's mono, add an axis.
         if audio_array.ndim == 1:
@@ -24,7 +19,7 @@ def detect_watermark(audio_data, sample_rate):
         
         # Ensure waveform is 2D (batch, channels, samples) for AudioSeal
         if waveform.ndim == 2:
-            waveform = waveform.unsqueeze(0)  # Add batch dimension if necessary
+            waveform = waveform.unsqueeze(0)
 
         # Initialize and use the AudioSeal detector
         detector = AudioSeal.load_detector("audioseal_detector_16bits")
@@ -34,18 +29,17 @@ def detect_watermark(audio_data, sample_rate):
         detection_result = "AI-generated" if result else "genuine"
         return f"This audio is likely {detection_result} based on watermark detection."
     except Exception as e:
-        logging.error(f"An error occurred: {e}")
+        error_message = f"An error occurred: {str(e)}"
         traceback_str = ''.join(traceback.format_tb(e.__traceback__))
-        logging.error(traceback_str)
-        return "An error occurred. Please check the log file for details."
+        full_error_message = f"{error_message}\n{traceback_str}"
+        return full_error_message
 
-## Corrected Gradio interface definition
 interface = gr.Interface(fn=detect_watermark,
                          inputs=[gr.Audio(label="Upload your audio", type="numpy"),
-                                 gr.Number(label="Sample Rate", value=44100, visible=False)],  # Sample rate is now passed but not visible to the user
+                                 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.")
 
 if __name__ == "__main__":
-    interface.launch()
+    interface.launch(debug=True)