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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
audio_model = pipeline("audio-classification", model="MelodyMachine/Deepfake-audio-detection-V2")
print("Model downloaded and ready to use.")
# Download model when the server starts
download_models()
@app.route('/detect', methods=['POST'])
def detect_deepfake():
data_type = request.form.get('type') # "audio"
audio_file = request.files.get('audio_file')
folder_path = request.form.get('folder_path')
# 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
# If a folder path is provided
elif folder_path and os.path.isdir(folder_path):
results = {}
try:
for file_name in os.listdir(folder_path):
if file_name.endswith('.wav') or file_name.endswith('.mp3'):
file_path = os.path.join(folder_path, file_name)
result = audio_model(file_path)
results[file_name] = {item['label']: item['score'] for item in result}
# Save results to a file
with open('detection_results.json', 'w') as f:
f.write(json.dumps(results))
return jsonify({"message": "Detection completed", "results": results}), 200
except Exception as e:
return jsonify({"error": str(e)}), 500
# Invalid request if neither audio file nor valid folder path is provided
else:
return jsonify({"error": "Invalid input. Provide an audio file or a valid folder path."}), 400
if __name__ == '__main__':
# Run the Flask app
app.run(host='0.0.0.0', port=7860)
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