Update app.py

app.py

@@ -7,6 +7,7 @@ import streamlit as st
 import pandas as pd
 import joblib
 import numpy as np
+
 # Load trained models
 rf_model = joblib.load("rf_tuned_model.pkl")
 xgb_model = joblib.load("xgb_model.pkl")
@@ -17,71 +18,78 @@ label_encoders = joblib.load("label_encoders.pkl")
 label_encoders_2 = joblib.load("label_encoders_2.pkl")
 feature_order = joblib.load("feature_columns.pkl")
 
-st.title("🏥 Hospital Readmission Prediction App")
-
-st.sidebar.header("📋 Enter Patient Information")
-
-# User input fields
-time_in_hospital = st.sidebar.number_input("Days in Hospital", min_value=1, max_value=30, step=1)
-n_lab_procedures = st.sidebar.number_input("Number of Lab Procedures", min_value=0, max_value=100, step=1)
-n_procedures = st.sidebar.number_input("Number of Procedures", min_value=0, max_value=10, step=1)
-n_medications = st.sidebar.number_input("Number of Medications", min_value=0, max_value=50, step=1)
-age = st.sidebar.selectbox("Age Group", ["[0-10)", "[10-20)", "[20-30)", "[30-40)", "[40-50)", "[50-60)", "[60-70)", "[70-80)", "[80-90)", "[90-100)"])
-glucose_test = st.sidebar.selectbox("Glucose Test", ["Yes", "No"])
-A1Ctest = st.sidebar.selectbox("A1C Test", ["Yes", "No"])
-change = st.sidebar.selectbox("Change in Medication", ["Yes", "No"])
-diabetes_med = st.sidebar.selectbox("Diabetes Medication", ["Yes", "No"])
-
-# Convert user input into a DataFrame
-user_input_df = pd.DataFrame({
-    "time_in_hospital": [time_in_hospital],
-    "n_lab_procedures": [n_lab_procedures],
-    "n_procedures": [n_procedures],
-    "n_medications": [n_medications],
-    "age": [age],
-    "glucose_test": [glucose_test],
-    "A1Ctest": [A1Ctest],
-    "change": [change],
-    "diabetes_med": [diabetes_med]
-})
-
-# Encode categorical features
-for col in label_encoders:
-    if col in user_input_df:
-        user_input_df[col] = user_input_df[col].apply(lambda x: x if x in label_encoders[col].classes_ else "Unknown")
-        label_encoders[col].classes_ = np.append(label_encoders[col].classes_, "Unknown")
-        user_input_df[col] = label_encoders[col].transform(user_input_df[col])
-
-# Ensure the feature order matches the training data
-user_input_df = user_input_df.reindex(columns=feature_order, fill_value=0)
-
-# Predict using the models
-if st.sidebar.button("🔍 Predict Readmission"):
-    rf_prediction = rf_model.predict(user_input_df)[0]
-    rf_proba = rf_model.predict_proba(user_input_df)[0][1]
-
-    xgb_prediction = xgb_model.predict(user_input_df)[0]
-    xgb_proba = xgb_model.predict_proba(user_input_df)[0][1]
-
-    lgbm_prediction = lgbm_model.predict(user_input_df)[0]
-    lgbm_proba = lgbm_model.predict_proba(user_input_df)[0][1]
-
-    # Display Results
-    st.subheader("📊 Prediction Results")
-
-    def format_prediction(pred, proba):
-        if pred == 0:
-            return f"🔴 **Likely to be Readmitted** (Probability: {proba:.2%})"
-        else:
-            return f"🟢 **Not Likely to be Readmitted** (Probability: {proba:.2%})"
-
-    st.write("**Random Forest:**", format_prediction(rf_prediction, rf_proba))
-    st.write("**XGBoost:**", format_prediction(xgb_prediction, xgb_proba))
-    st.write("**LightGBM:**", format_prediction(lgbm_prediction, lgbm_proba))
-    
-    # Choose final prediction (majority vote)
-    final_prediction = round((rf_prediction + xgb_prediction + lgbm_prediction) / 3)
-    final_proba = (rf_proba + xgb_proba + lgbm_proba) / 3
-
-    st.markdown("### 🏥 **Final Prediction:**")
-    st.write(format_prediction(final_prediction, final_proba))
+# Set page layout
+st.set_page_config(layout="wide")
+
+# Title
+st.markdown("<h1 style='text-align: center;'>🏥 Hospital Readmission Prediction App</h1>", unsafe_allow_html=True)
+
+# Centering the input form
+st.markdown("<h3 style='text-align: center;'>📋 Enter Patient Information</h3>", unsafe_allow_html=True)
+
+# Create a centered layout
+col1, col2, col3 = st.columns([1, 3, 1])
+with col2:
+    time_in_hospital = st.number_input("Days in Hospital", min_value=1, max_value=30, step=1)
+    n_lab_procedures = st.number_input("Number of Lab Procedures", min_value=0, max_value=100, step=1)
+    n_procedures = st.number_input("Number of Procedures", min_value=0, max_value=10, step=1)
+    n_medications = st.number_input("Number of Medications", min_value=0, max_value=50, step=1)
+    age = st.selectbox("Age Group", ["[0-10)", "[10-20)", "[20-30)", "[30-40)", "[40-50)", "[50-60)", "[60-70)", "[70-80)", "[80-90)", "[90-100)"])
+    glucose_test = st.selectbox("Glucose Test", ["Yes", "No"])
+    A1Ctest = st.selectbox("A1C Test", ["Yes", "No"])
+    change = st.selectbox("Change in Medication", ["Yes", "No"])
+    diabetes_med = st.selectbox("Diabetes Medication", ["Yes", "No"])
+
+    # Convert user input into a DataFrame
+    user_input_df = pd.DataFrame({
+        "time_in_hospital": [time_in_hospital],
+        "n_lab_procedures": [n_lab_procedures],
+        "n_procedures": [n_procedures],
+        "n_medications": [n_medications],
+        "age": [age],
+        "glucose_test": [glucose_test],
+        "A1Ctest": [A1Ctest],
+        "change": [change],
+        "diabetes_med": [diabetes_med]
+    })
+
+    # Encode categorical features
+    for col in label_encoders:
+        if col in user_input_df:
+            user_input_df[col] = user_input_df[col].apply(lambda x: x if x in label_encoders[col].classes_ else "Unknown")
+            label_encoders[col].classes_ = np.append(label_encoders[col].classes_, "Unknown")
+            user_input_df[col] = label_encoders[col].transform(user_input_df[col])
+
+    # Ensure the feature order matches the training data
+    user_input_df = user_input_df.reindex(columns=feature_order, fill_value=0)
+
+    # Prediction button
+    if st.button("🔍 Predict Readmission"):
+        rf_prediction = rf_model.predict(user_input_df)[0]
+        rf_proba = rf_model.predict_proba(user_input_df)[0][1]
+
+        xgb_prediction = xgb_model.predict(user_input_df)[0]
+        xgb_proba = xgb_model.predict_proba(user_input_df)[0][1]
+
+        lgbm_prediction = lgbm_model.predict(user_input_df)[0]
+        lgbm_proba = lgbm_model.predict_proba(user_input_df)[0][1]
+
+        # Display Results
+        st.markdown("<h3 style='text-align: center;'>📊 Prediction Results</h3>", unsafe_allow_html=True)
+
+        def format_prediction(pred, proba):
+            if pred == 0:
+                return f"🔴 **Likely to be Readmitted** (Probability: {proba:.2%})"
+            else:
+                return f"🟢 **Not Likely to be Readmitted** (Probability: {proba:.2%})"
+
+        st.write("**Random Forest:**", format_prediction(rf_prediction, rf_proba))
+        st.write("**XGBoost:**", format_prediction(xgb_prediction, xgb_proba))
+        st.write("**LightGBM:**", format_prediction(lgbm_prediction, lgbm_proba))
+
+        # Choose final prediction (majority vote)
+        final_prediction = round((rf_prediction + xgb_prediction + lgbm_prediction) / 3)
+        final_proba = (rf_proba + xgb_proba + lgbm_proba) / 3
+
+        st.markdown("### 🏥 **Final Prediction:**")
+        st.write(format_prediction(final_prediction, final_proba))