Update app.py

app.py

@@ -11,7 +11,45 @@ import matplotlib.pyplot as plt
 import os
 import sklearn
 
-# Assuming the previous setup and imports have been done
+# Display library versions
+print(f"Gradio version: {gr.__version__}")
+print(f"NumPy version: {np.__version__}")
+print(f"Scikit-learn version: {sklearn.__version__}")
+print(f"Joblib version: {joblib.__version__}")
+print(f"TensorFlow version: {tf.__version__}")
+print(f"Pandas version: {pd.__version__}")
+
+# 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.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')
+image_path = os.path.join(script_dir, 'toolkit', 'car.png')
+
+# Load the scaler and models
+try:
+    # 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 = load(meta_model_path)
+    print("Meta model loaded successfully.")
+except Exception as e:
+    print(f"Error loading models or scaler: {e}")
 
 def predict_and_plot(velocity, temperature, precipitation, humidity):
     try:
@@ -90,9 +128,6 @@ def predict_and_plot(velocity, temperature, precipitation, humidity):
         plt.legend()
         plt.grid(True)
         
-        # Save the plot to a file
-        plt.savefig('plot.png')
-        
         # Return the plot and predictions
         return plt, [f"{val * 100:.2f}%" for val in contamination_levels[0]], [f"{val:.2f}" for val in cleaning_times]