Create app.py

app.py

@@ -0,0 +1,49 @@
+import os
+import json
+from flask import Flask, jsonify, request
+from transformers import pipeline
+
+# Create a Flask app
+app = Flask(__name__)
+
+# Initialize the audio model at the start of the API
+audio_model = None
+
+def download_models():
+    global audio_model
+    print("Downloading audio model...")
+    # Download and load the audio model
+    audio_model = pipeline("audio-classification", model="MelodyMachine/Deepfake-audio-detection-V2")
+    print("Audio model downloaded and ready to use.")
+
+# Download the model when the server starts
+download_models()
+
+@app.route('/detect', methods=['POST'])
+def detect_deepfake():
+    folder_path = request.form.get('folder_path')
+
+    if not folder_path or not os.path.isdir(folder_path):
+        return jsonify({"error": "Invalid folder path"}), 400
+
+    results = {}
+    try:
+        # Process audio files only
+        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
+
+if __name__ == '__main__':
+    # Run the Flask app
+    app.run(host='0.0.0.0', port=5000)