Update app.py

app.py

@@ -1,26 +1,32 @@
 import gradio as gr
 import torch
 import torchaudio
-from transformers import AutoProcessor, AutoModelForAudioClassification
+from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
 
-# Load the Hugging Face processor and model for audio deepfake detection.
-processor = AutoProcessor.from_pretrained("MelodyMachine/Deepfake-audio-detection-V2")
-model = AutoModelForAudioClassification.from_pretrained("MelodyMachine/Deepfake-audio-detection-V2")
+# Load the Hugging Face feature extractor and model
+feature_extractor = AutoFeatureExtractor.from_pretrained(
+    "MelodyMachine/Deepfake-audio-detection-V2"
+)
+model = AutoModelForAudioClassification.from_pretrained(
+    "MelodyMachine/Deepfake-audio-detection-V2"
+)
 
 def detect_deepfake_audio(audio_path: str) -> str:
-    # Load audio (supports WAV, MP3, FLAC, etc.)
+    # Load any audio format via torchaudio
     waveform, sample_rate = torchaudio.load(audio_path)
 
-    # Convert to mono if necessary
+    # Convert to mono if stereo
     if waveform.shape[0] > 1:
         waveform = torch.mean(waveform, dim=0, keepdim=True)
 
     # Preprocess for model
-    inputs = processor(waveform, sampling_rate=sample_rate, return_tensors="pt")
+    inputs = feature_extractor(
+        waveform, sampling_rate=sample_rate, return_tensors="pt"
+    )
     with torch.no_grad():
         outputs = model(**inputs)
 
-    # Get prediction
+    # Softmax to get class probabilities
     probs = torch.softmax(outputs.logits, dim=-1)[0]
     idx = torch.argmax(probs).item()
     label = model.config.id2label[idx]
@@ -28,13 +34,15 @@ def detect_deepfake_audio(audio_path: str) -> str:
 
     return f"The audio is classified as **{label}** with confidence **{confidence:.2f}**"
 
-# Build Gradio interface
+# Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("# Audio Deepfake Detection App")
-    gr.Markdown("### Upload or record an audio clip to detect deepfake content.")
-    audio_in = gr.Audio(source="upload", type="filepath", label="Upload Audio")
+    gr.Markdown("# Audio Deepfake Detection")
+    gr.Markdown("Upload an audio clip to check for deepfake content.")
+    audio_in = gr.Audio(source="upload", type="filepath", label="Audio File")
     txt_out = gr.Textbox(label="Result")
-    gr.Button("Detect").click(fn=detect_deepfake_audio, inputs=audio_in, outputs=txt_out)
+    gr.Button("Detect").click(
+        fn=detect_deepfake_audio, inputs=audio_in, outputs=txt_out
+    )
 
 if __name__ == "__main__":
     demo.launch()