Update src/app.py

src/app.py

@@ -2,23 +2,21 @@ import streamlit as st
 import joblib
 import numpy as np
 import pandas as pd
-from xgboost import XGBClassifier
 
 # ----------------------------
 # Load Saved Model & Encoders
 # ----------------------------
 try:
     model = joblib.load("src/best_stroke_model_1.pkl")
+    x_num_scaler = joblib.load("src/x_num_scaler_1.pkl")
+    x_cat_encoder = joblib.load("src/x_cat_encoder_1.pkl")
+    x_cat_ordinal_encoder = joblib.load("src/x_cat_ordinal_encoder_1.pkl")
 except Exception as e:
-    st.error(f"Model loading failed: {e}. Please retrain the model with XGBoost ≥1.7.")
+    st.error(f"Failed to load model or encoders: {e}")
     st.stop()
 
-x_num_scaler = joblib.load("src/x_num_scaler_1.pkl")
-x_cat_encoder = joblib.load("src/x_cat_encoder_1.pkl")
-x_cat_ordinal_encoder = joblib.load("src/x_cat_ordinal_encoder_1.pkl")
-
 # ----------------------------
-# Streamlit App Layout
+# Streamlit Layout
 # ----------------------------
 st.set_page_config(page_title="Stroke Prediction App", page_icon="🧠", layout="centered")
 st.title("🧠 Stroke Prediction App")
@@ -43,7 +41,7 @@ if st.button("Predict"):
     input_df = pd.DataFrame([[gender, age, hypertension, heart_disease, work_type, avg_glucose_level, bmi, smoking_status]],
                             columns=["gender", "age", "hypertension", "heart_disease", "work_type", "avg_glucose_level", "bmi", "smoking_status"])
 
-    # Preprocess inputs
+    # Preprocessing
     scaled_x_num = x_num_scaler.transform(input_df[["age", "avg_glucose_level", "bmi"]])
     binary_x = input_df[["hypertension", "heart_disease"]].values
     encoded_x_cat = x_cat_encoder.transform(input_df[["work_type", "smoking_status"]])
@@ -51,10 +49,9 @@ if st.button("Predict"):
 
     final_x = np.hstack([scaled_x_num, binary_x, encoded_x_cat, ordinal_encoded_x_cat])
 
-    # Predict using XGBoost
+    # Predict
     prediction = model.predict(final_x)
     result = "⚠️ High Risk of Stroke" if prediction[0] == 1 else "✅ Low Risk of Stroke"
 
     st.success(f"**Prediction:** {result}")
     st.info("This prediction is based on machine learning and should not replace professional medical advice.")
-