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app.py

@@ -25,13 +25,13 @@ rot_speed = st.number_input("Rotational Speed (RPM)", min_value=0, max_value=300
 torque = st.number_input("Torque (Nm)", min_value=0.0, max_value=100.0, value=40.0, step=0.1)
 tool_wear = st.number_input("Tool Wear (min)", min_value=0, max_value=300, value=10)
 
-# Assemble input into DataFrame
+# Assemble input into DataFrame with correct column names
 input_data = pd.DataFrame([{
-    'Air_temperature': air_temp,
-    'Process_temperature': process_temp,
-    'Rotational_speed': rot_speed,
+    'Air temperature': air_temp,
+    'Process temperature': process_temp,
+    'Rotational speed': rot_speed,
     'Torque': torque,
-    'Tool_wear': tool_wear,
+    'Tool wear': tool_wear,
     'Type': Type
 }])
 
@@ -41,6 +41,11 @@ if st.button("Predict Failure"):
         # Align input columns with model training columns
         input_data = input_data[model.feature_names_in_]
 
+        print("Model expects features:")
+        print(model.feature_names_in_)
+        print("Input data:")
+        print(input_data)
+        
         prediction = model.predict(input_data)[0]
         result = "Machine Failure" if prediction == 1 else "No Failure"
         st.subheader("Prediction Result:")