app.py

import gradio as gr
import torch
import torchaudio
from transformers import AutoProcessor, 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")

def detect_deepfake_audio(audio_path: str) -> str:
    # Load audio (supports WAV, MP3, FLAC, etc.)
    waveform, sample_rate = torchaudio.load(audio_path)

    # Convert to mono if necessary
    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")
    with torch.no_grad():
        outputs = model(**inputs)

    # Get prediction
    probs = torch.softmax(outputs.logits, dim=-1)[0]
    idx = torch.argmax(probs).item()
    label = model.config.id2label[idx]
    confidence = probs[idx].item()

    return f"The audio is classified as **{label}** with confidence **{confidence:.2f}**"

# Build Gradio interface
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")
    txt_out = gr.Textbox(label="Result")
    gr.Button("Detect").click(fn=detect_deepfake_audio, inputs=audio_in, outputs=txt_out)

if __name__ == "__main__":
    demo.launch()