Update app.py

app.py

@@ -2,11 +2,8 @@ import gradio as gr
 import pandas as pd
 import joblib
 import numpy as np
-from models.neural_network.inference import load_model_and_preprocessor
 
 # Load the pre-trained model
-nn_model, nn_preprocessor = load_model_and_preprocessor('saved_models/nn_model.keras',
-                                                        'saved_models/nn_preprocessor.pkl')
 xgboost_model = joblib.load('saved_models/xgboost_model.joblib')
 
 # Load the unique aircraft data
@@ -44,10 +41,8 @@ def predict_fuel_burn(model_name, origin, destination, seats, distance):
     df = pd.DataFrame(data)
 
     # Make the prediction
-    fuel_burn_prediction_nn = nn_model.predict(nn_preprocessor.transform(df))[0]
     fuel_burn_prediction_xgboost = xgboost_model.predict(df)
-
-    return f"Neural Network: {fuel_burn_prediction_nn[0]:.2f} kg, XGBoost: {fuel_burn_prediction_xgboost[0]:.2f} kg"
+    return f"{fuel_burn_prediction_xgboost[0]:.2f} kg"
 
 
 def update_fields(model_name):