remove nb

app/main.py

@@ -183,10 +183,10 @@ async def get_answer(item: Item, token: str = Depends(verify_token)):
         raise HTTPException(status_code=500, detail=str(e))
 
 # naive bayes
-nb_model = joblib.load('app/1713630229.4965415_trained_model.joblib')
-nb_pca = joblib.load('app/pca.pkl')
-nb_scaler = joblib.load('app/scaler.pkl')
-nb_label_encoder = joblib.load('app/label_encoder.pkl')
+model = joblib.load('app/1713630229.4965415_trained_model.joblib')
+pca = joblib.load('app/pca.pkl')
+scaler = joblib.load('app/scaler.pkl')
+label_encoder = joblib.load('app/label_encoder.pkl')
 
 # def preprocess_audio(path, save_dir):
 #     y, sr = librosa.load(path)
@@ -333,16 +333,16 @@ async def handle_audio(file: UploadFile = File(...)):
         # features = extract_features(temp_filename)
         print("Extracted Features:", features)
 
-        # features = nb_scaler.transform(features)
-        # features = nb_pca.transform(features)
+        # features = scaler.transform(features)
+        # features = pca.transform(features)
         features = np.array(features).reshape(1, -1)
 
         # proceed with an inference
-        results = nb_model.predict(features)
-        # decoded_predictions = [nb_label_encoder.classes_[i] for i in results]
+        results = model.predict(features)
+        # decoded_predictions = [label_encoder.classes_[i] for i in results]
 
         # # Decode the predictions using the label encoder
-        # decoded_predictions = nb_label_encoder.inverse_transform(results)
+        # decoded_predictions = label_encoder.inverse_transform(results)
         # .tolist()
         # Clean up the temporary file
         os.remove(temp_filename)