app.py

import os
import json
from flask import Flask, jsonify, request
from transformers import pipeline

# Create a Flask app
app = Flask(__name__)

# Initialize models at the start of the API
audio_model = None

def download_models():
    global audio_model
    print("Downloading models...")
    # Download and load the audio model with padding enabled
    audio_model = pipeline("audio-classification", model="MelodyMachine/Deepfake-audio-detection-V2", padding=True)
    print("Model downloaded and ready to use.")

# Download model when the server starts
download_models()

@app.route('/detect', methods=['POST'])
def detect_deepfake():
    # Expect an audio file in the request
    audio_file = request.files.get('audio_file')

    # If a single audio file is provided
    if audio_file:
        try:
            # Save the uploaded file temporarily
            file_path = os.path.join("/tmp", audio_file.filename)
            audio_file.save(file_path)

            # Perform detection
            result = audio_model(file_path)
            result_dict = {item['label']: item['score'] for item in result}

            # Remove the temporary file
            os.remove(file_path)

            return jsonify({"message": "Detection completed", "results": result_dict}), 200

        except Exception as e:
            return jsonify({"error": str(e)}), 500

    # Invalid request if no audio file is provided
    else:
        return jsonify({"error": "Invalid input. Please provide an audio file."}), 400

if __name__ == '__main__':
    # Run the Flask app
    app.run(host='0.0.0.0', port=7860)