Update app.py

app.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import streamlit as st
+import pickle
+
+from scipy import stats
+from sklearn.impute import KNNImputer
+from scipy.stats import chi2_contingency 
+from sklearn.model_selection import train_test_split, cross_validate,StratifiedKFold
+from imblearn.over_sampling import SMOTE
+import optuna
+from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from imblearn.pipeline import Pipeline as ImbPipeline
+from optuna.samplers import TPESampler
+from optuna.visualization import plot_param_importances,plot_optimization_history
+
+from sklearn.tree import DecisionTreeClassifier
+
+st.title("10-Year CHD Risk Prediction")
+
+# Take Inputs
+
+with st.expander("Basic Details"):
+    age = st.number_input("Age", 1, 120, 30)
+    education = st.selectbox("Education Level", [1, 2, 3, 4])
+
+with st.expander("Medical History"):
+    sex = st.radio("Sex", ["Male", "Female"])
+    is_smoking = st.radio("Do you smoke?", ["Yes", "No"])
+    cigs_per_day = st.slider("Cigarettes Per Day", 0, 100, 0)
+    bp_meds = st.radio("Taking Blood Pressure Medication?", ["Yes", "No"])
+    prevalent_stroke = st.radio("Had a stroke?", ["Yes", "No"])
+    prevalent_hyp = st.radio("Hypertension?", ["Yes", "No"])
+    diabetes = st.radio("Diabetes?", ["Yes", "No"])
+
+with st.expander("Health Measurements"):
+    total_cholesterol = st.number_input("Total Cholesterol", 100.0, 400.0, 200.0)
+    systolic_bp = st.slider("Systolic BP", 50.0, 250.0, 120.0)
+    diastolic_bp = st.slider("Diastolic BP", 30.0, 150.0, 80.0)
+    bmi = st.number_input("BMI", 10.0, 50.0, 25.0)
+    heart_rate = st.slider("Heart Rate", 30.0, 200.0, 70.0)
+    glucose = st.number_input("Glucose", 50.0, 300.0, 90.0)
+
+model = pickle.load(open("final_pipeline.pkl","rb"))
+
+if st.button("Predict"):
+    prediction = model.predict([[age, education, sex, is_smoking, cigs_per_day, bp_meds, prevalent_stroke, 
+                                 prevalent_hyp, diabetes, total_cholesterol, systolic_bp, diastolic_bp, 
+                                 bmi, heart_rate, glucose]])
+
+    result = prediction[0]
+    st.write(f"**Predicted Value:** {result}")
+
+    if result == 1:
+        st.error("⚠️ High Risk: You have a higher chance of developing Coronary Heart Disease (CHD) in the next 10 years. Please consult a doctor and consider lifestyle changes.")
+    else:
+        st.success("✅ Low Risk: Your predicted risk of developing CHD in the next 10 years is low. Keep maintaining a healthy lifestyle!")