Update app.py

app.py

@@ -1,25 +1,38 @@
 import gradio as gr
 import numpy as np
-import joblib
+import json
+from joblib import load
 from tensorflow.keras.models import load_model
+from sklearn.preprocessing import MinMaxScaler
 import os
 
 # Directory paths for the saved models
 script_dir = os.path.dirname(os.path.abspath(__file__))
-scaler_path = os.path.join(script_dir, 'toolkit', 'scaler_X.pkl')
-rf_model_path = os.path.join(script_dir, 'toolkit', 'rf_model.pkl')
-mlp_model_path = os.path.join(script_dir, 'toolkit', 'mlp_model.h5')
-meta_model_path = os.path.join(script_dir, 'toolkit', 'meta_model.pkl')
+scaler_path = os.path.join(script_dir, 'toolkit', 'scaler_X.json')
+rf_model_path = os.path.join(script_dir, 'toolkit', 'rf_model.joblib')
+mlp_model_path = os.path.join(script_dir, 'toolkit', 'mlp_model.keras')
+meta_model_path = os.path.join(script_dir, 'toolkit', 'meta_model.joblib')
 
 # Load the scaler and models
 try:
-    scaler_X = joblib.load(scaler_path)
-    print("Scaler loaded successfully.")
-    loaded_rf_model = joblib.load(rf_model_path)
+    # Load the scaler
+    with open(scaler_path, 'r') as f:
+        scaler_params = json.load(f)
+    scaler_X = MinMaxScaler()
+    scaler_X.scale_ = np.array(scaler_params["scale_"])
+    scaler_X.min_ = np.array(scaler_params["min_"])
+    scaler_X.data_min_ = np.array(scaler_params["data_min_"])
+    scaler_X.data_max_ = np.array(scaler_params["data_max_"])
+    scaler_X.data_range_ = np.array(scaler_params["data_range_"])
+    scaler_X.n_features_in_ = scaler_params["n_features_in_"]
+    scaler_X.feature_names_in_ = np.array(scaler_params["feature_names_in_"])
+
+    # Load the models
+    loaded_rf_model = load(rf_model_path)
     print("Random Forest model loaded successfully.")
     loaded_mlp_model = load_model(mlp_model_path)
     print("MLP model loaded successfully.")
-    loaded_meta_model = joblib.load(meta_model_path)
+    loaded_meta_model = load(meta_model_path)
     print("Meta model loaded successfully.")
 except Exception as e:
     print(f"Error loading models or scaler: {e}")
@@ -71,7 +84,7 @@ def gradio_interface(velocity, temperature, precipitation, humidity):
 
 inputs = [
     gr.Slider(minimum=0, maximum=100, value=50, step=0.5, label="Velocity (mph)"),
-    gr.Slider(minimum=-2, maximum=20, value=0, step=0.5, label="Temperature (°C)"),
+    gr.Slider(minimum=-30, maximum=50, value=0, step=0.5, label="Temperature (°C)"),
     gr.Slider(minimum=0, maximum=10, value=0, step=0.01, label="Precipitation (inch)"),
     gr.Slider(minimum=0, maximum=100, value=50, label="Humidity (%)")
 ]