Update cancer.py

cancer.py

@@ -52,7 +52,7 @@ def train_model(x_train, y_train, preprocess, model_name):
         ('preprocessor', preprocess),
         ('classifier', models[model_name])
     ])
-    pipeline.fit(X_train, y_train)
+    pipeline.fit(x_train, y_train)
     return pipeline
 
 # Streamlit UI
@@ -65,14 +65,14 @@ with st.sidebar:
     if st.button("Train Model"):
         # Load and preprocess data
         df = load_data()
-        (X_train, X_test, y_train, y_test), preprocess = preprocess_data(df)
+        (x_train, x_test, y_train, y_test), preprocess = preprocess_data(df)
         
         # Train model
         try:
-            model = train_model(X_train, y_train, preprocess, model_name)
-            accuracy = model.score(X_test, y_test)
+            model = train_model(x_train, y_train, preprocess, model_name)
+            accuracy = model.score(x_test, y_test)
             st.session_state['trained_model'] = model
-            st.session_state['X_train'] = X_train
+            st.session_state['x_train'] = x_train
             st.success(f"Model Trained Successfully! Accuracy: {accuracy:.2f}")
         except ValueError as e:
             st.error(f"Error: {e}")
@@ -104,10 +104,10 @@ input_data = [[age, tumor_size, tumor_grade, symptoms_severity, smoking_history,
 if st.button("Predict Cancer Presence"):
     if 'trained_model' in st.session_state:
         model = st.session_state['trained_model']
-        X_train = st.session_state['X_train']
+        x_train = st.session_state['X_train']
         
         # Prepare input data for prediction
-        input_df = pd.DataFrame(input_data, columns=X_train.columns)
+        input_df = pd.DataFrame(input_data, columns=x_train.columns)
         input_transformed = model.named_steps['preprocessor'].transform(input_df)
         
         # Make prediction