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import os

import tempfile

import numpy as np

import librosa

import joblib

from flask import Flask, request, jsonify

from flask_cors import CORS

MODEL_PATH = "voice_model.pkl"

N_MFCC = 40

app = Flask(__name__)

CORS(app)

if not os.path.exists(MODEL_PATH):

raise FileNotFoundError(

f"'{MODEL_PATH}' not found."

)

print("Loading trained model...")

model = joblib.load(MODEL_PATH)

print("Model loaded. Server ready.")

def extract_features(file_path):

audio, sample_rate = librosa.load(file_path, sr=16000)

mfccs = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=N_MFCC)

mfccs_mean = np.mean(mfccs.T, axis=0)

return mfccs_mean.reshape(1, -1) # reshape for a single prediction

@app.route("/predict", methods=["POST"])

def predict():

if "audio" not in request.files:

return jsonify({"error": "No audio file provided"}), 400

audio_file = request.files["audio"]

# Save the uploaded file temporarily so librosa can read it

with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:

audio_file.save(tmp.name)

temp_path = tmp.name

try:

features = extract_features(temp_path)

prediction = model.predict(features)[0]

probabilities = model.predict_proba(features)[0]

confidence = round(max(probabilities) * 100, 1)

label = "FAKE" if prediction == 1 else "REAL"

return jsonify({

"result": label,

"confidence": confidence

})

except Exception as e:

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

finally:

os.remove(temp_path) # clean up the temporary file

@app.route("/health", methods=["GET"])

def health():

return jsonify({"status": "ok", "model_loaded": True})

if __name__ == "_main_":

app.run(debug=True, port=port)