Update app.py

app.py

@@ -8,8 +8,8 @@ model = joblib.load("predictive_maintenance_model(1).pkl")
 model = joblib.load(model_path)
 
 # Define the prediction function
-def predict_maintenance(feature1, feature2, feature3, feature4):
-    input_data = np.array([[feature1, feature2, feature3, feature4]])  # Modify according to your dataset
+def predict_maintenance(feature1, feature2, feature3, feature4,feature5,feature6,feature7,feature8,feature9,feature10):
+    input_data = np.array([[feature1, feature2, feature3, feature4,feature5,feature6,feature7,feature8,feature9,feature10]])  # Modify according to your dataset
     prediction = model.predict(input_data)
     return f"Predicted Maintenance Requirement: {prediction[0]}"
 
@@ -21,6 +21,16 @@ interface = gr.Interface(
         gr.Number(label="Feature 2"),
         gr.Number(label="Feature 3"),
         gr.Number(label="Feature 4"),
+        gr.Number(label="Feature 5"),
+        gr.Number(label="Feature 6"),
+        gr.Number(label="Feature 7"),
+        gr.Number(label="Feature 8"),
+        gr.Number(label="Feature 9"),
+        gr.Number(label="Feature 10"),
+
+
+
+        
     ],
     outputs=gr.Textbox(label="Prediction"),
     title="Predictive Maintenance for Industrial Equipment",