Update app.py

app.py

@@ -6,8 +6,10 @@ import gradio as gr
 import numpy as np
 import pickle
 import os
-import joblib
 from PIL import Image
+import joblib
+import json
+from sklearn.tree import export_text
 
 # Constants
 NUM_CLASSES = 4
@@ -37,11 +39,8 @@ def load_cnn_model():
 
 # Load the Random Forest model
 def load_rf_model():
-    try:
-        return joblib.load("rf_model.pkl")
-    except Exception as e:
-        print(f"Error loading RF model: {e}")
-        return None
+    with open("rf_model.pkl", "rb") as f:
+        return pickle.load(f)
 
 # Preprocess image for CNN
 def preprocess_image_cnn(image):
@@ -105,6 +104,46 @@ def predict(img, model_choice, cnn_model, rf_model):
     except Exception as e:
         return f"Error: {str(e)}", "An error occurred during prediction."
 
+# Convert Random Forest to a safer JSON format
+def convert_rf_to_safe_format():
+    """Convert Random Forest to a safer JSON format"""
+    
+    # Load the existing model
+    rf_model = joblib.load("models/rf_model.pkl")
+    
+    # Extract model parameters
+    model_params = {
+        'n_estimators': rf_model.n_estimators,
+        'max_depth': rf_model.max_depth,
+        'min_samples_split': rf_model.min_samples_split,
+        'random_state': rf_model.random_state,
+        'classes': rf_model.classes_.tolist(),
+        'n_features': rf_model.n_features_in_,
+        'feature_importances': rf_model.feature_importances_.tolist()
+    }
+    
+    # Save model parameters
+    with open("rf_model_params.json", "w") as f:
+        json.dump(model_params, f)
+    
+    # Save individual tree parameters (simplified approach)
+    trees_data = []
+    for i, tree in enumerate(rf_model.estimators_):
+        tree_data = {
+            'tree_id': i,
+            'feature': tree.tree_.feature.tolist(),
+            'threshold': tree.tree_.threshold.tolist(),
+            'children_left': tree.tree_.children_left.tolist(),
+            'children_right': tree.tree_.children_right.tolist(),
+            'value': tree.tree_.value.tolist()
+        }
+        trees_data.append(tree_data)
+    
+    with open("rf_trees_data.json", "w") as f:
+        json.dump(trees_data, f)
+    
+    print("Model converted to safe JSON format")
+
 # Load models
 cnn_model = load_cnn_model()
 rf_model = load_rf_model()
@@ -134,4 +173,7 @@ with gr.Blocks() as demo:
     gr.Markdown("3. Click 'Classify' to get the prediction")
 
 # Launch the app
-demo.launch()
+demo.launch()
+
+if __name__ == "__main__":
+    convert_rf_to_safe_format()