app.py

import io
import pickle
import streamlit as st
import joblib
import shap
import pandas as pd
import matplotlib.pyplot as plt


# Load the LightGBM model and other necessary objects
with open('lgb1_model.pkl', 'rb') as f:
    lgb1 = pickle.load(f)

categorical_features = joblib.load("categorical_features.joblib")
encoder = joblib.load("encoder.joblib")

# Sidebar option to select the dashboard
option = st.sidebar.selectbox("Which dashboard?", ("Model information", "Stroke prediction"))
st.title(option)

def get_pred():
    """
    Function to display the stroke probability calculator and Shap force plot.
    """
    st.header("Stroke probability calculator ")

    # User input for prediction
    gender = st.selectbox("Select gender: ", ["Male", "Female", 'Other'])
    work_type = st.selectbox("Work type: ", ["Private", "Self_employed", 'children', 'Govt_job', 'Never_worked'])
    residence_status = st.selectbox("Residence status: ", ["Urban", "Rural"])
    smoking_status = st.selectbox("Smoking status: ", ["Unknown", "formerly smoked", 'never smoked', 'smokes'])
    age = st.slider("Input age: ", 0, 120)
    hypertension = st.select_slider("Do you have hypertension: ", [0, 1])
    heart_disease = st.select_slider("Do you have heart disease: ", [0, 1])
    ever_married = st.select_slider("Have you ever married? ", [0, 1])
    avg_glucosis_lvl = st.slider("Average glucosis level: ", 50, 280)
    bmi = st.slider("Input Bmi: ", 10, 100)

    # User input data
    data = {
        "gender": gender,
        "work_type": work_type,
        "Residence_type": residence_status,
        "smoking_status": smoking_status,
        "age": age,
        "hypertension": hypertension,
        "heart_disease": heart_disease,
        "ever_married": ever_married,
        "avg_glucose_level": avg_glucosis_lvl,
        "bmi": bmi
    }


    if st.button("Predict"):
        # Convert input data to a DataFrame
        X = pd.DataFrame([data])


        encoded_features = encoder.transform(X[categorical_features])


        feature_names = encoder.get_feature_names_out(input_features=categorical_features)


        encoded_df = pd.DataFrame(encoded_features, columns=feature_names)
        X_encoded = pd.concat([X.drop(columns=categorical_features), encoded_df], axis=1)


        prediction_proba = lgb1.predict_proba(X_encoded)


        explainer = shap.TreeExplainer(lgb1)
        shap_values = explainer.shap_values(X_encoded)


        probability = prediction_proba[0, 1]  # Assuming binary classification
        st.subheader(f"The predicted probability of stroke is {probability}.")
        st.subheader("IF you see result , higher than 0.3, we advice you to see a doctor")
        st.header("Shap forceplot")
        st.subheader("Features values impact on model made prediction")


        shap.force_plot(explainer.expected_value[1], shap_values[1], features=X_encoded.iloc[0, :], matplotlib=True)


        buf = io.BytesIO()
        plt.savefig(buf, format="png", dpi=800)
        buf.seek(0)


        st.image(buf, width=1100)


        shap.summary_plot(shap_values[1], X_encoded)


        shap_interaction_values = explainer.shap_interaction_values(X_encoded)
        shap.summary_plot(shap_interaction_values, X_encoded)


if option == "Information about training data":
    st.header("Stroke Prediction Dataset")
    st.subheader("According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient.")
    st.subheader(" Stroke dataset has 5110 records and 12 features.")
    st.subheader(" Correlation between features:.")
    st.image(r'Correlation.png')
    st.subheader("Features Shap values and how it effects Target variable "Stroke")
    st.image(r'Shap_Values.png')
    

    
if option == "Stroke prediction":
    get_pred()

if option == "Model information":
    st.header("Light gradient boosting model")
    st.subheader("First tree of light gradient boosting model and how it makes decisions")
    st.image(r'lgbm_tree.png')
    



    st.subheader("Shap values visualization of how features contribute to model prediction")
    st.image(r'lgbm_model_shap_evaluation.png')