Update app.py

app.py

@@ -12,18 +12,16 @@ local_model_path = "./"
 extractor = AutoFeatureExtractor.from_pretrained(local_model_path)
 model = AutoModelForAudioClassification.from_pretrained(local_model_path)
 
-def predict_voice(audio_file):
+def predict_voice(audio_file_path):
     """
     Predicts whether a voice is real or spoofed from an audio file.
     
     Args:
-        audio_file: The input audio file to be classified.
+        audio_file_path: The file path of the uploaded audio file to be classified.
     
     Returns:
         A string with the prediction and confidence level.
     """
-    # Gradio passes the audio file as a tuple (file_name, file_path). We only need the file_path.
-    audio_file_path = audio_file[1]
     
     # Load the audio file. Adjust the loading mechanism based on your audio file format.
     waveform, sample_rate = sf.read(audio_file_path)
@@ -49,11 +47,9 @@ def predict_voice(audio_file):
     result = f"The voice is classified as '{label}' with a confidence of {confidence:.2f}%."
     return result
 
-
-# Setting up the Gradio interface
 iface = gr.Interface(
     fn=predict_voice,
-    inputs=gr.Audio(type="filepath", label="Upload Audio File"),  # Corrected usage
+    inputs=gr.Audio(type="filepath", label="Upload Audio File"),  # Ensure filepath is passed
     outputs=gr.Textbox(label="Prediction"),
     title="Voice Authenticity Detection",
     description="Detects whether a voice is real or AI-generated. Upload an audio file to see the results.",